modern technology and critical thinking

How Does Technology Affect Critical Thinking?

Impact of Technology on Critical Thinking

You use technology in one form or another every day. As time goes on, it plays a more significant role in our lives and changes the way we consume and process information. Critical thinking is all about analyzing the information in front of you, thinking about it rationally and without bias, and always asking questions.

How Does Technology Improve Critical Thinking?

Education and learning.

Schools are introducing more and more technology in the classroom to keep up with advances. They hope to better prepare students for the world of growing technology.

Appropriate technology in classrooms increases students’ academic achievement, self-confidence, motivation in class, and attendance. Technology helps students move beyond sitting attentively and listening and promotes more hands-on learning.

Simulations

Social media, is technology killing critical thinking.

In schools, the type of technology that students use can boost their learning quality or harm it. Having classrooms wired for student internet access has been shown to decrease learning. Students that use the internet during a class lecture do not pay as much attention to the speaker. In contrast, students without the internet pay more attention.

Final Thoughts

The world of technology is so infinite that it can seem overwhelming. All forms of media don’t work in every setting. There needs to be a balance of modern technology in life, or you run the risk of losing out on developing fundamental skills.

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Distance Learning

Using technology to develop students’ critical thinking skills.

by Jessica Mansbach

What Is Critical Thinking?

Critical thinking is a higher-order cognitive skill that is indispensable to students, readying them to respond to a variety of complex problems that are sure to arise in their personal and professional lives. The  cognitive skills at the foundation of critical thinking are  analysis, interpretation, evaluation, explanation, inference, and self-regulation.  

When students think critically, they actively engage in these processes:

• Communication
• Problem-solving

To create environments that engage students in these processes, instructors need to ask questions, encourage the expression of diverse opinions, and involve students in a variety of hands-on activities that force them to be involved in their learning.

Types of Critical Thinking Skills

Instructors should select activities based on the level of thinking they want students to do and the learning objectives for the course or assignment. The chart below describes questions to ask in order to show that students can demonstrate different levels of critical thinking.

*Adapted from Brown University’s Harriet W Sheridan Center for Teaching and Learning

Using Online Tools to Teach Critical Thinking Skills

Online instructors can use technology tools to create activities that help students develop both lower-level and higher-level critical thinking skills.

• Example: Use Google Doc, a collaboration feature in Canvas, and tell students to keep a journal in which they reflect on what they are learning, describe the progress they are making in the class, and cite course materials that have been most relevant to their progress. Students can share the Google Doc with you, and instructors can comment on their work.
• Example: Use the peer review assignment feature in Canvas and manually or automatically form peer review groups. These groups can be anonymous or display students’ names. Tell students to give feedback to two of their peers on the first draft of a research paper. Use the rubric feature in Canvas to create a rubric for students to use. Show students the rubric along with the assignment instructions so that students know what they will be evaluated on and how to evaluate their peers.
• Example: Use the discussions feature in Canvas and tell students to have a debate about a video they watched. Pose the debate questions in the discussion forum, and give students instructions to take a side of the debate and cite course readings to support their arguments.  
• Example: Us e goreact , a tool for creating and commenting on online presentations, and tell students to design a presentation that summarizes and raises questions about a reading. Tell students to comment on the strengths and weaknesses of the author’s argument. Students can post the links to their goreact presentations in a discussion forum or an assignment using the insert link feature in Canvas.
• Example:  Use goreact, a narrated Powerpoint, or a Google Doc and instruct students to tell a story that informs readers and listeners about how the course content they are learning is useful in their professional lives. In the story, tell students to offer specific examples of readings and class activities that they are finding most relevant to their professional work. Links to the goreact presentation and Google doc can be submitted via a discussion forum or an assignment in Canvas. The Powerpoint file can be submitted via a discussion or submitted in an assignment.

Pulling it All Together

Critical thinking is an invaluable skill that students need to be successful in their professional and personal lives. Instructors can be thoughtful and purposeful about creating learning objectives that promote lower and higher-level critical thinking skills, and about using technology to implement activities that support these learning objectives. Below are some additional resources about critical thinking.

Additional Resources

Carmichael, E., & Farrell, H. (2012). Evaluation of the Effectiveness of Online Resources in Developing Student Critical Thinking: Review of Literature and Case Study of a Critical Thinking Online Site.  Journal of University Teaching and Learning Practice ,  9 (1), 4.

Lai, E. R. (2011). Critical thinking: A literature review.  Pearson’s Research Reports ,  6 , 40-41.

Landers, H (n.d.). Using Peer Teaching In The Classroom. Retrieved electronically from https://tilt.colostate.edu/TipsAndGuides/Tip/180

Lynch, C. L., & Wolcott, S. K. (2001). Helping your students develop critical thinking skills (IDEA Paper# 37. In  Manhattan, KS: The IDEA Center.

Mandernach, B. J. (2006). Thinking critically about critical thinking: Integrating online tools to Promote Critical Thinking. Insight: A collection of faculty scholarship , 1 , 41-50.

Yang, Y. T. C., & Wu, W. C. I. (2012). Digital storytelling for enhancing student academic achievement, critical thinking, and learning motivation: A year-long experimental study. Computers & Education , 59 (2), 339-352.

Insight Assessment: Measuring Thinking Worldwide

http://www.insightassessment.com/

Michigan State University’s Office of Faculty  & Organizational Development, Critical Thinking: http://fod.msu.edu/oir/critical-thinking

The Critical Thinking Community

http://www.criticalthinking.org/pages/defining-critical-thinking/766

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9 responses to “ Using Technology To Develop Students’ Critical Thinking Skills ”

This is a great site for my students to learn how to develop critical thinking skills, especially in the STEM fields.

Great tools to help all learners at all levels… not everyone learns at the same rate.

Thanks for sharing the article. Is there any way to find tools which help in developing critical thinking skills to students?

Technology needs to be advance to develop the below factors:

Understand the links between ideas. Determine the importance and relevance of arguments and ideas. Recognize, build and appraise arguments.

Excellent share! Can I know few tools which help in developing critical thinking skills to students? Any help will be appreciated. Thanks!

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Brilliant post. Will be sharing this on our Twitter (@refthinking). I would love to chat to you about our tool, the Thinking Kit. It has been specifically designed to help students develop critical thinking skills whilst they also learn about the topics they ‘need’ to.

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Assessing Critical Thinking in the Digital Era

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• Technology is poised to revolutionize education. Instead of being disrupted by the new tech, schools should participate in its development.
• Technology can be particularly useful in helping schools assess critical thinking skills, which have become even more important in a world that increasingly relies on artificial intelligence.
• Peregrine Global Services has worked with institutions of higher learning to launch a new Critical Thinking Assessment tool to help schools measure both retained knowledge and acquired competencies.

  Technology has traditionally disrupted education, and higher education institutions have struggled to keep pace with these changes. However, when institutions of higher education partner with the technology sector, they can become sources of disruption themselves.

One of the most notable examples of how technology disrupted the educational field is the calculator. As Sarah Banks outlines in a 2011 master’s thesis that analyzes historical attitudes about the use of calculators in junior high and high school math classrooms, the invention met with mixed responses from educators.

Some educators viewed calculators as helpful tools that could speed up calculations and save time, allowing students to focus on more complex mathematical concepts. Others expressed concern that calculators would become crutches for students, hindering their ability to develop basic arithmetic skills. Eventually, of course, calculators became indispensable tools in the classroom and beyond.

More recently, artificial intelligence (AI) has emerged as a powerful new technology that has the potential to revolutionize education. However, educators such as Andre Perry and Nicol Turner Lee have expressed concerns about the possible negative impacts of AI. Among other things, they note that its algorithms can perpetuate bias and discrimination. Industry observers such as Lyss Welding point out that AI poses a risk to academic integrity because it allows students to plagiarize and cheat on homework in ways that are easier, faster, and harder to detect.

Artificial intelligence (AI) has emerged as a powerful new technology that has the potential to revolutionize education.

Despite these concerns, AI technology has become an integral part of modern education as more educators are actively adapting and leveraging it to benefit their learners. But teachers should not introduce technology into their classrooms unless they are also helping students develop their skills in higher-order thinking. While technology provides tools to assist with calculations, information access, and other tasks, critical thinking enables students to make sense of that information and use it effectively.

The Importance of Assessment

However, while critical thinking is widely recognized as an essential skill, it can be challenging for higher education institutions to quantify or measure how well students have learned it. Assessment is a vital and dynamic component of teaching knowledge, skills, and competencies. It informs program and institutional improvement, providing invaluable information that administrators, faculty, and staff can use to make data-driven decisions that lead to better student outcomes.

One of the key difficulties in assessing critical thinking is defining what it is and how it should be measured. Critical thinking is a complex skill that involves the ability to analyze and evaluate information, think creatively, and make reasoned judgments, as Richard Paul and Linda Elder outline in their 2019 publication . It is not a single skill that can be easily quantified or measured through traditional assessments. As a result, educators have had to develop more nuanced approaches to evaluating critical thinking skills, such as project-based assessments and open-ended questions that require students to demonstrate their reasoning and problem-solving abilities.

While critical thinking is widely recognized as an essential skill, it can be challenging for higher education institutions to quantify or measure how well students have learned it.

Another challenge in measuring critical thinking is ensuring that assessments are fair and unbiased. Assessments that are overly reliant on multiple-choice questions or rote memorization can unfairly disadvantage students who may excel in other areas of critical thinking.

For these reasons, educators need effective assessment methods that accurately measure critical thinking skills in a variety of contexts. These assessments should use consistent and objective criteria to ensure that all students are given equal opportunities to demonstrate their abilities.

However, building such assessment tools and overcoming the barriers associated with measuring critical thinking places a large and sometimes overwhelming administrative burden on faculty and staff. Unfortunately, there can be a negative impact on student performance when faculty members must allocate more time and resources to handling administrative tasks than to teaching courses and supporting learner success.

A Partnership Between Industry and Academia

The need for critical thinking assessment tools is being addressed through a recent partnership between various higher education institutions and Peregrine Global Services, an education technology company specializing in assessment and instructional solutions. Peregrine recently launched its Critical Thinking Assessment to help colleges and universities evaluate this important skill.

To ensure that the assessment tool would meet the specific needs of the higher education community, the company developed its Peregrine Partner Program, which involved beta testing the tool with programs of varying sizes and types during the fall of 2022 and the spring of 2023. Each educational partner provided valuable feedback on how to present data to help schools make informed decisions, how to remove administrative burdens associated with assessment, and how to foster a culture of quality.

The partnership between Peregrine and the higher education institutions has led to several unforeseen advancements in technology. These include the ability to analyze exam data by course, cohort, or program, as well as the implementation of blind scoring to remove scoring bias. The new tool also adopts an innovative approach to assessing critical thinking and generating the data necessary to analyze exam results. For example, schools will be able to sort and filter data by levels of higher-order thinking.

The Critical Thinking Assessment uses a standardized rubric covering six critical thinking subcriteria and provides institutions with the flexibility to customize the exams to meet their needs. Academic programs can tailor the service to cover specific disciplines and assess varying levels of higher-order thinking. Learners receive scenarios randomly, ensuring a unique testing experience for each student.

The system auto-scores multiple-choice questions, while designated program faculty and assessment administrators use a rubric to manually score open-ended items. The short case studies and scenario questions are written and validated by subject matter experts with practical and teaching experience in each specific discipline.

“The Critical Thinking Assessment helps make assessment a facultywide effort, where everyone has buy-in,” says Melodie Philhours, associate professor of marketing and director of assessment at Arkansas State University’s Neil Griffin College of Business in Jonesboro. “The assessment tool significantly reduces the time and resources required for assessment, allowing faculty to focus on teaching and improving student learning outcomes. One of the most significant benefits has been the removal of the administrative burden related to compiling and entering the data, as the results are readily available after the assessment is fully scored.”

At the Forefront of Disruption

The collaboration between Peregrine and its partner schools will benefit not only the institutions involved, but also the broader field of education. Any time higher education and the technology sector can work together, they will drive innovation and disruption, ultimately leading to better learner outcomes. With the Critical Thinking Assessment tool, Peregrine aims to help higher education institutions assess not just retained knowledge, but also acquired skills and competencies.

In the future, Peregrine plans to incorporate AI into the assessment and build an aggregate pool, so schools can compare their results over periods of time, internally and externally, allowing them to benchmark against schools with similar demographics. Until then, Peregrine is offering the tool to schools as a course-level assessment they can use in their overall assessment portfolio. 

The partnership between Peregrine and universities highlights the potential for industry and academia to come together to address the challenges faced by higher education. It demonstrates that when universities are at the forefront of disrupting education in a positive manner, they can move along with technology rather than lag behind it.

• critical thinking
• partnerships

• Research article
• Open access
• Published: 26 February 2024

Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education

• Yoshija Walter   ORCID: orcid.org/0000-0003-0282-9659 1  

International Journal of Educational Technology in Higher Education volume  21 , Article number:  15 ( 2024 ) Cite this article

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The present discussion examines the transformative impact of Artificial Intelligence (AI) in educational settings, focusing on the necessity for AI literacy, prompt engineering proficiency, and enhanced critical thinking skills. The introduction of AI into education marks a significant departure from conventional teaching methods, offering personalized learning and support for diverse educational requirements, including students with special needs. However, this integration presents challenges, including the need for comprehensive educator training and curriculum adaptation to align with societal structures. AI literacy is identified as crucial, encompassing an understanding of AI technologies and their broader societal impacts. Prompt engineering is highlighted as a key skill for eliciting specific responses from AI systems, thereby enriching educational experiences and promoting critical thinking. There is detailed analysis of strategies for embedding these skills within educational curricula and pedagogical practices. This is discussed through a case-study based on a Swiss university and a narrative literature review, followed by practical suggestions of how to implement AI in the classroom.

Introduction

In the evolving landscape of education, the integration of Artificial Intelligence (AI) represents a transformative shift, stipulating a new era in learning and teaching methodologies. This article delves into the multifaceted role of AI in the classroom, focusing particularly on the primacy of prompt engineering, AI literacy, and the cultivation of critical thinking skills.

The advent of AI in educational settings transcends mere technological advancement, reshaping the educational experience at its core. AI's role extends beyond traditional teaching methods, offering personalized learning experiences and supporting a diverse range of educational needs. It enhances educational processes, developing essential skills such as computational and critical thinking, intricately linked to machine learning and educational robotics. Furthermore, AI has shown significant promise in providing timely interventions for children with special educational needs, enriching both their learning experiences and daily life (Zawacki-Richter et al., 2019 ). However, integrating AI into education is not without its challenges. It requires a systematic approach that takes into account societal structural conditions. Beyond algorithmic thinking, AI in education demands a focus on creativity and technology fluency to foster innovation and critical thought. This requires a paradigm shift in how education is approached in the AI era, moving beyond traditional methods to embrace more dynamic, interactive, and student-centered learning environments (Chiu et al., 2023 ).

This article sets the stage for a comprehensive exploration of AI's role in modern education. It underscores the need for an in-depth understanding of prompt engineering methodologies, AI literacy, and critical thinking skills, examining their implications, challenges, and opportunities in shaping the future of education. Whereas previous papers have already hinted at the importance of recognizing the relevance of AI in the classroom and suggested preliminary frameworks (Chan, 2023 ), the present discussion claims that there are three prime skills necessary for the future of education in an AI-adopted world. These three skills are supplanted with practical application advice and based on the experience of lecturers at a University of Applied Sciences. As such, the present paper is a conceptual discussion of how to best integrate AI in the classroom, focusing on higher education. While this means that it may predominantly be relevant for adult students, it is believed that it may be useful for children as well.

Methodological remarks

The current paper entails a conceptual discussion about the proper use of AI in terms of the necessary skillset applied. It is based on a two-step approach:

Among others, it is based on intense informal discussions with students and lecturers at a Swiss University of Applied Sciences, as well as the present author’s teaching experience at this school. Woven together, this leads to a case study for an outlook of how a necessary skillset of AI use in the educational setting may be beneficially honed. There are some open questions that emerge from this, which can be addressed by findings from the literature.

Upon the discussion of the real-life case in the university, the need for further clarifications, answers and best practices is then pursued by a narrative literature review to complete the picture, which eventually leads to practical suggestions for higher education.

The informal discussions with students and personnel were unstructured and collected where feasible in these early days of AI use to gather a holistic and trustworthy picture as possible about the explicit and implicit attitudes, fears, chances, and general use of the technology. Hence, this included teacher-student discussions in classroom settings with several classes where students were asked to voice their ideas in the plenum and in smaller groups, individual discussions with students during the breaks, lunch talks with professors and teachers, as well as gathering of correspondence about the topic in the meetings that were held at the university. Taken together, this provided enough information to weave together a solid understanding of the present atmosphere concerning attitudes and uses of AI.

The emergence of AI in education

The introduction of ChatGPT (to date one of the most powerful AI chatbots by OpenAI) in November 2022 is significantly transforming the landscape of education, marking a new era in how learning is approached and delivered. This advanced AI tool has redefined educational paradigms, offering a level of personalization in learning that was previously unattainable. ChatGPT, with its sophisticated language processing capabilities, is quickly becoming a game-changer in classrooms, to provide tailored educational experiences that cater to the unique needs, strengths, and weaknesses of each student. This shift from traditional, uniform teaching methods to highly individualized learning strategies will most likely signify a major advancement in educational practices (Aristanto et al., 2023 ). ChatGPT's role in personalizing education is particularly noteworthy. By analyzing student data and employing advanced algorithms, GPT and other Large Language Models (LLMs) can create customized learning experiences, adapting not only to academic requirements but also to each student's learning style, pace, and preferences. This leads to a more dynamic and effective educational environment, where students are actively engaged and involved in their learning journey, rather than being mere passive recipients of information (Steele, 2023 ). Furthermore, LLMs have shown remarkable potential in supporting students with special needs. They provide specialized tools and resources that cater to diverse learning challenges, making education more accessible and inclusive (Garg & Sharma, 2020 ). Students who might have found it difficult to keep up in a conventional classroom setting can now benefit from AI’s ability to tailor content and delivery to their specific needs, thereby breaking down barriers to learning and fostering a more inclusive educational atmosphere (Rakap, 2023 ). In all of this, the integration of language models like GPT into educational systems is not just a mere enhancement but has the potential to become an integral part of modern teaching and learning methodologies. While adapting to this AI-driven approach presents certain challenges, the benefits for students, educators, and the educational system at large are substantial (for in-depth reviews, see Farhi et al., 2023 ; Fullan et al., 2023 ; Ottenbreit-Leftwich et al., 2023 ). ChatGPT in education can be a significant stride towards creating a more personalized, inclusive, and effective learning experience, preparing students not only for current academic challenges but also for the evolving demands of the future.

However, the many precious possibilities in positively transforming the education systems through AI also comes with some downsides. They can be summarized in several points (Adiguzel et al., 2023 ; Ji et al., 2023 ; Ng et al., 2023a , 2023b , 2023c ; Ng et al., 2023a , 2023b , 2023c ):

Teachers feeling overwhelmed because they do not have much knowledge of the technology and how it could best be used.

Both teachers and students not being aware of the limitations and dangers of the technology (i.e. generating false responses through AI hallucinations).

Students uncritically using the technology and handing over the necessary cognitive work to the machine.

Students not seeking to learn new materials for themselves but instead wanting to minimize their efforts.

Inherent technical problems that exacerbate malignant conditions, such as GPT-3, GPT-3.5 and GPT-4 mirroring math anxiety in students (Abramski et al., 2023 ).

In order for all parties to be best prepared for using AI in education, based on a case study and a subsequent literature analysis, there are three necessary skills that can remedy these problems, which are AI literacy, knowledge about prompt engineering, and critical thinking. A more detailed analysis of the challenges is discussed, followed by suggestions for practical applications.

Case study at a swiss educational institution

The educational difficulty of ai in academic work.

The present case study deals with the introduction and the handling of Artificial Intelligence at the Kalaidos University of Applied Sciences (KFH) in Zurich, Switzerland. To date, KFH is the only privately owned university of applied sciences in the country and consists of a departement of business, a department of health, a department of psychology, a department of law, and a department of music. Since the present author has a lead position in the university’s AI-Taskforce , he has firsthand and intimate knowledge about the benefits and challenges that arose in the past year when AI chatbots suddenly became much more popular, including the fears surrounding this topic by both staff and students.

Like many other universities, KFH has had significant challenges with finding an adequate response to the introduction of ChatGPT and its following adoption by students, lecturers, and supervisors. It was deemed important by the AI-Taskforce as well as the school’s leadership that there was going to be a nuanced approach towards handling the new technology. Whereas some institutions banned LLMs right away, others embraced them wholeheartedly and barely enforced any restrictions in their use. KFH was eager to find some middle ground since it seemed clear to the leadership that both extremes may be somewhat problematic. The major reasons are summarized in Table  1 .

The quest for a middle ground

Discussions with students in the classroom at KFH have shown that one year after the introduction of ChatGPT, only few have not yet used it. The general atmosphere is that they are enthusiastic about the new AI that can help them with their workload, also the ones due in the classroom and the help they get to write their papers. However, students are also keenly aware that it is “just a machine” and that there should be some practical and ethical principles that ought to be abided by. They name the following reasons:

The use of AI should be fair, as in that no student is at an unfair advantage or disadvantage.

It should be clear how the expectations of the school look like so that students know exactly what they are allowed and what they are not allowed to do.

Many feel that they do not know enough about the potentials and limitations of these systems, so some are afraid to use it incorrectly.

The problems of AI hallucinations and misalignment are still not widely known: Many students are still surprised to learn that AI can make up things that may not be true while sounding highly convincing.

Some of the students having a clear understanding of the hallucinatory AI problems still feel ill equipped to deal with them.

As such, KFH has the intent to help its students to learn to deal with AI in a responsible fashion. For the members of the AI-Taskforce and the university’s leadership, this has come to mean that the use of ChatGPT and other LLMs are neither prohibited nor allowed without restrictions. Just exactly how such a framework would look like and could be implemented was subject to intense debate. The final compromise was a document internally labelled as “The AI-Guidelines” (in German: “KI-Leitfaden”) that set the rules and furnished examples of what would be deemed acceptable and unacceptable use of AI for students when they implemented it for their papers. The main gist was to tell students that they are explicitly allowed and encouraged to use the new technology for their work. They should experiment with it and see how they can use the outputs for their own theses. The correct use would be to handle AI not as their tutor, teacher or ghostwriter, but as their sparring partner. Just like with any other human sparring partner, it can provide interesting ideas and suggestions. It may provide some directions and answers that the student might have not thought of. However, at the same time, the sparring partner is not always right and should not be unconditionally trusted. It is also not correct to use a sparring partner’s output as one’s own, which in a normal setting would be considered plagiarism (although according to internal documents, technically speaking, copying an artificially generated text would not be classified as plagiarism, but would be unethical to the same degree). The same is true for how students would be allowed to interact with AI: They should use it if it helps them, but they are not allowed to copy any text ad verbatim and they also must make it clear how exactly they have used it. In making it clear how they have used AI, they must be transparent about the following (and document this in a table in the appendix):

Declaring which model was implemented

OpenAI’s GPT-4 and Dall-E 3, Google’s Bard, or Anthropic AI’s Claude-2.

Explaining how and why it was used

Using the LLM to brainstorm about some models as adequate frameworks for the applied research question.

Explaining how the responses of the AI were critically evaluated

The results were checked through a literature review to see if the AI’s suggestions were true and made sense.

Highlighting which places in the manuscript the AI as used for

Chapter 2 “Theory” (pp. 10–24).

There were two major motivations for prompting students to declare these points: First, the institution wanted to enforce full transparency on how AI was used. Second, students should become keenly aware that they must stay critical towards an AI’s output and must hence report on how they made sure that they did not fall prey to the classic AI problems (such as hallucinations) as well as to make sure that the work still remains of their own making. This is why we considered our third point in the documentation requirements (the need for critical reflection) our most crucial innovation – something that we did not find in other schools and universities. This led to the formulation of binding guidelines, which is depicted in Table  2 .

Problems with the adopted response

The institution’s primary response to the problem of AI generated content for academic papers was the implementation of these “AI guidelines”. While the guidelines are a necessary step towards regulating AI use, there are significant problems with the approach that has been used hitherto. One of the most substantial issues is the fact that their effectiveness hinges on student compliance, which is not guaranteed. Many students might not thoroughly read these documents, leading to a gap in understanding and adherence. Since reading the documents is voluntary, it is possible that not all have read them before using AI in their work. At the same time, there is also currently no vessel to check whether they in fact have read them or not.

To date, a significant issue is the lack of comprehensive training in AI capabilities for students. Merely providing a document on AI use is not sufficient for fostering a deep understanding of AI technology, its potential, and its limitations. This lack of training could lead to misuse of AI tools, as many students might not be aware of how to properly integrate these technologies into their academic work. Monitoring the use of AI in student assignments poses another challenge. It is difficult to verify whether a piece of work has been created with the aid of AI, especially as these tools become more sophisticated. This uncertainty makes it hard to ensure that students are following these guidelines, and it is equally difficult to make sure that nobody is gaining an unfair advantage. Moreover, a significant number of students may not be fully aware of how to responsibly use AI tools, nor understand their limitations. This lack of knowledge can result in a reliance on AI-generated content without critical evaluation, potentially undermining the quality and integrity of academic work. At the same time, students might also miss out on the opportunity to enhance their learning and critical thinking skills through the proper use of AI.

None of this can be remedied by simply providing a document and hoping that students would read it and abide by its ideals. Addressing these issues requires more than just setting guidelines; it calls for a holistic approach that includes educating students about AI, its ethical use, and limitations.

Potential solutions to the problems

To equip both students and teachers to become apt in the use of AI for their academic purposes, a new “culture of AI” seems in order. An AI-culture should permeate academic life, creating an environment where AI is not feared but readily used, understood and – most importantly – critically evaluated. A potential avenue would be the implementation of regular workshops and meetings for teachers, supervisors, and students. These sessions should focus on up-to-date AI developments, ethical considerations, and best practices. By regularly engaging with AI topics, the academic community can stay informed and proficient in managing AI tools and concepts. This should help to deeply ingrain the understanding of AI's technical, practical, and social challenges.

Workshops and initiatives should “hammer in” the issues surrounding the complexities and implications of AI. Technological education should not be superficial but should delve into real-world scenarios, discussing how theory and practice converge, and providing students as well as educators with a robust understanding of AI's role in society and education. A further possibility is to integrate AI into every academic module wherever teacher’s see fit, as to offers consistent exposure and understanding of AI across various disciplines. This strategy ensures that students recognize the relevance of AI in different fields, preparing them for a future where AI is ubiquitous in professional environments. Perhaps deliberate classes of how to use AI could serve as a pillar in this educational model. These classes, covering a range of topics from basic principles to advanced applications and ethical considerations, could ensure that every student acquires a baseline understanding of AI, regardless of their major or field of study. Making these classes mandatory would ensure that every student at least once has been confronted with the necessary ins-and-outs and has at least a basic understanding of the AI guidelines. Beyond the classroom, voluntary collaborations and partnerships with AI experts, tech companies, and other educational institutions can provide invaluable insights and resources. These collaborations could bridge the gap between theoretical knowledge and practical application, giving students a more comprehensive understanding of AI's real-world implications. However, perhaps students may have interesting ideas themselves of how a responsible culture of AI could be fostered. Encouraging student-led AI initiatives, such as projects and clubs, can motivate a hands-on learning environment. These initiatives may promote peer learning, innovation, and practical application of AI knowledge. By actively engaging in AI projects, students can develop critical thinking and problem-solving skills that are essential in navigating the complexities of an accelerating digital world.

In other words, providing AI regulations is a good first step, but creating ways for students and lecturers to engage more deeply with the topic would probably enhance these measures and might help to foster a respective culture.

AI in the classroom

Naturally, Artificial Intelligence is not only relevant for creating papers, but it has also the potential to create novel classroom experiences. Although it is still rare for teachers to strongly adopt and work with AI in their lectures, some have already leaped forward and reported to implement the technology in several ways. Table 3 illustrates the main use-cases of how staff at the university has hitherto been using AI models.

Discussions with teachers have shown that one of the biggest constraints to implement AI tools in the classroom is their fear of using them, predominantly due to the fact that they might not know enough about them and assuming that they might use them wrongly. At the same time, students may also not be adept users and if the teachers do not feel like professionals themselves, this exacerbates the problem. Although the topic of human–computer-interactions is a truly pertinent one and gains a lot of attention in the scientific community, practitioners are often left behind and as such, at KFH there are currently no workshops and programs helping both teachers and students to improve in these matters. Moreover, since the digital world and AI technology is evolving so fast, many feel that it is incredibly difficult to stay on top with the developments. One of the marked challenges at the KFH is the ostensible fact that there is no dedicated person or group that is tasked with staying on top of the matter. To date, it is up to each and every individual to deal with it as one pleases and there is no paid position for this, meaning that employees would have to do all of the work on the side in their own time.

There are several recommendations that could help out with these problems and that might help foster an AI-driven culture in the classrooms:

Workshops: The school could provide workshops specifically tailored to help teachers understand what is going on in the world of AI and what tools there are to aid them in creating an AI-inclusive classroom environment.

Regular Updates: There could be outlets (i.e. in the form of newsletters, lunch-meetings, online-events, etc.) that aim towards keeping staff and lecturers up-to-date so that people are aware of the newest tools, apps, and approaches that could be useful for their lectures.

Financial Budget: At the moment, there is no financial aid to get trained on AI topics at this particular school and if staff wanted to do something, they effectively have to do it on their own. There should be a budget dedicated to helping employees to become knowledgeable in the field. In any other field, it would be erroneous to assume that employees would have to be asked to learn a language or another important skill like handling a student administration system and do this entirely in their free time with no financial aid. Yet, at the moment this is how the institution is faring with AI.

Guidelines and Best Practices: To date, apart from the “AI guidelines” for students, there are no written guidelines, tips and tricks, nor any suggestions for how to best use AI in the work and school context available. They might help providing some guidance.

Paid positions: Instead of purely relying on internal “freelancers” that have an intrinsic motivation to deal with technologies, it would be wise to create positions where experts have a say and can help shape the AI culture in the institution. This is commensurate with the third recommendation suggesting that AI would need to be budgeted.

Although these first recommendations based on the case-study may be helpful, further clarifications informed by the literature are necessary, specifically when it comes to the question of how AI literacy can be fostered at schools, how prompt engineering can be used as a pedagogical tool, and how students can improve their critical thinking skills through AI. A deeper look into the respective challenges and opportunities is warranted, followed by more generalizable practical suggestions for the use of AI in the classroom, that are not only based on this particular case-study but are enriched by findings from the literature more broadly.

AI literacy in the classroom

The concept of AI literacy emerges as a cornerstone of contemporary learning. In its essence, it deals with the understanding and capability to interact effectively with AI technology. It encompasses not just the technical know-how but also an awareness of the ethical and societal implications of AI. In the modern classroom, AI literacy goes beyond traditional learning paradigms, equipping students with the skills to navigate and harness the power of AI in various aspects of life and work. It represents a fundamental shift in education, where understanding AI becomes as crucial as reading, writing, and arithmetic (Zhang et al., 2023 ).

The current state of AI literacy in education reflects a burgeoning field, ripe with potential yet facing the challenges of early adoption. Educators and policymakers are beginning to recognize the importance of AI literacy, integrating it into curriculums and educational strategies (Casal-Otero et al., 2023 ; Chiu, 2023 ). However, this integration is in its nascent stages, with schools exploring various approaches to teaching this complex and ever-evolving skillset. The challenge lies in not only imparting technical knowledge but also in fostering a deeper understanding of AI's broader impact – be this on a social, psychological, or even economic level. Due to its importance, there are first AI-Literacy-Scales emerging using questionnaires that can be handed to students (Ng et al., 2023 ). Although to date there is no stringent consensus on the full scope of the term, it may be argued that AI literacy consists of several sub-skills:

Architecture:

Understanding the basic architectural ideas underlying Artificial Neural Networks (only on a basic need-to-know basis). This should primarily entail the knowledge that such systems are nothing more than purely statistical models.

Limitations:

Understanding what these models are good for and where they fail. Most poignantly, students and teachers should understand that such statistical models are not truth-generators but effective data processors (like sentence constructors or image generators).

Problem Landscape:

Understanding where all the main problems of AI systems lie, due to the fact that they are only statistical machines and not truth-generators. This means that students and teachers ought to know the major pitfalls of AI, which are:

AI hallucination: AI can “invent” things that are not true (while still sounding authoritative).

AI alignment: AI can do something else than what we instructed it to so (sometimes so subtly that it sometimes goes unnoticed).

AI runaway: AI becomes self-governing, meaning that it sets up certain instrumental goals that was not present in our terminal instructions (for a detailed philosophical analysis of this problem, see Bostrom, 2002 , 2012 )

AI discrimination: Due to skewed data in its training, an AI can be biased and lead to discriminatory conclusions against underrepresented groups.

AI Lock-In problem: An AI can get stuck within a certain narrative and thus loses the full picture (experiments and a full explanation of this can be found in Walter, 2022 ).

Applicability and Best Practices

Understanding not only the risks but also the many ways AI can be beneficially used and implemented in daily life and the context of learning. This also includes a general understanding of emerging best practices using AI in the classroom (Southworth et al., 2023 ).

Understanding the major AI basics, its limitations and risks, as well as potential problems and how it can be used should lead to a nuanced understanding of its ethics. Students and teachers should develop a sense of justice, which governs them to converge on how to virtuously implement AI models in educational settings.

It was shown that early exposure to technology concepts can significantly influence students' career paths and preparedness for the future (Bembridge et al., 2011 ; Margaryan, 2023 ). By introducing AI literacy at a young age, students develop a foundational understanding that paves the way for advanced learning and application in later stages of education and professional life. This early adoption of AI literacy is crucial in preparing a generation that is not both adept at using AI as well as capable of innovating and leading in a technology-driven world. This makes the development of AI literacy at schools and universities an important feature of every student. Furthermore, its role extends beyond academic achievement; it is about preparing students for the realities of a future where AI is ubiquitous. In careers spanning from science and engineering to arts and humanities, an understanding of AI will be an invaluable asset, enabling individuals to work alongside AI technologies effectively and ethically. As such, AI literacy is not just an educational objective but a vital life skill for the twenty-first century.

One concrete suggestion is to provide “AI literacy courses” that have the deliberate intent to foster the associated skills in students. In order to have a well-rounded and holistic class, an AI literacy program should entail several key components (Kong et al., 2021 ; Laupichler et al., 2022 ; Ng et al., 2023c ):

Introduction to AI Concepts : Basic definitions and understanding of what AI is, including its history and evolution. This should cover different types of AI, such as narrow AI, general AI, and superintelligent AI.

Understanding Machine Learning and Technical Foundations : An overview of machine learning, which is a core part of AI. This includes understanding different types of machine learning (supervised, unsupervised, reinforcement learning) and basic algorithms. This can also be enriched through more technical foundations, like an introduction for programming with AI.

Proper Data Handling : Discussion on the importance of data in AI, how AI systems are trained with data, and how one can protect oneself against piracy and privacy concerns.

AI in Practice : Real-world applications of AI in various fields such as healthcare, finance, transportation, and entertainment. This should include both the benefits and challenges of AI implementation.

Human-AI Interaction : Understanding how humans and AI systems can work together, including topics like human-in-the-loop systems, AI augmentation, and the future of work with AI.

AI and Creativity : Exploring the role of AI in creative processes, such as in art, music, and writing, and the implications of AI-generated content.

Critical Thinking about AI : Developing skills to critically assess AI news, research, and claims. Understanding how to differentiate between AI hype and reality.

AI Governance and Policy : An overview of the regulatory and policy landscape surrounding AI, including discussions on AI safety, standards, and international perspectives.

Future Trends and Research in AI : A look at the cutting edge of AI research and predictions for the future development of AI technologies.

Hands-on Experience : Practical exercises, case studies, or projects that allow students to apply AI concepts and tools in real or simulated scenarios.

Ethical AI design and development: Principles of designing and developing AI in an ethical, responsible, and sustainable manner. This also includes the risk for biased AI and its impact on society.

AI Literacy for All : Tailoring content to ensure it is accessible and understandable to people from diverse backgrounds, not just those with a technical or scientific background.

Prompt Engineering: Understanding what methods are most effective in prompting AI models to follow provided tasks and to generate adequate responses.

At the moment, there are specific projects that attempt to implement AI literacy at school (Tseng & Yadav, 2023 ). The deliberate goal is to eventually lead students towards a responsible use of AI, but to do so, they need to understand how one can “talk” to an AI so that it does what it is supposed to. This means that students must become effective prompt engineers.

Prompt engineering as a pedagogical tool

Prompt engineering, at its core, involves the strategic crafting of inputs to elicit desired responses or behaviors from AI systems. In educational settings, this translates to designing prompts that not only engage students but also challenge them to think critically and creatively. The art of prompt engineering lies in its ability to transform AI from a mere repository of information into an interactive tool that stimulates deeper learning and understanding (cf. Lee et al., 2023 ). The relevance of prompt engineering in education cannot be overstated. As AI becomes increasingly sophisticated and integrated into learning environments, the ability to effectively communicate with these systems becomes crucial. Prompt engineering empowers educators to guide AI interactions in a way that enhances the educational experience. It allows for the creation of tailored learning scenarios that can adapt to the needs and abilities of individual students, making learning more engaging and effective (Eager & Brunton, 2023 ). One of the most significant impacts of prompt engineering is its potential to enhance learning experiences and foster critical thinking. By carefully designing prompts, educators can encourage students to approach problems from different perspectives, analyze information critically, and develop solutions creatively. This approach not only deepens their understanding of the subject matter but also hones their critical thinking skills, an essential competency in today’s fast-paced and ever-changing world. As one particular study showed, learning to prompt effectively in the classroom can even help students realize more about the limits of AI, which inevitably fosters their AI literacy (Theophilou et al., 2023 ). Moreover, AI has the potential to lead to highly interactive and playful teaching settings. With the right programs, it can also be implemented in game-based learning through AI. This combination has the potential to transform traditional learning paradigms, making education more accessible, enjoyable, and impactful (Chen et al., 2023 ).

Just recently, there are a handful of successful prompting methodologies that have emerged, which are continuously being improved. Prompt engineering is an experimental discipline, meaning that through trial and error, one can slowly progress to create better outputs by revising and molding the input prompts. As a scientific discipline, AI itself can help to find new ways to interact with AI systems. The most relevant prompting methods are summarized in Table  4 and are explained thereafter.

There are two major forms of how a language model can be prompted: (i) Zero-Shot prompts, and (ii) Few-Shot prompts. Zero-Shot prompts are the most intuitive alternative, which most likely all of us predominantly use when interacting with models like ChatGPT. This is when a simple prompt is provided without much further details and then an unspecific response is generated, which is helpful when one deals with broad problems or situations where there is not a lot of data. Few-Shot prompting is a technique where a prompt is enriched with several examples of how the task should be completed. This is helpful in case one deals with a complex query where there are already concrete ideas or data available. As the name suggests, these “shots” can be enumerated (based on Dang et al., 2022 ; Kojima et al., 2022 ; Tam, 2023 ):

Zero-Shot prompts: There are no specific examples added.

One-Shot prompts : One specific example is added to the prompt.

Two-Shot prompts : Two examples are added to the prompt.

Three-Shot prompts : Three examples are added to the prompt.

Few - Shot prompts: Several examples are added to the prompt (unspecified how many).

These prompting methods have gradually developed and became more complex, starting from Input–Output Prompting all the way to Tree-of-Thought Prompting, which is displayed in Table  4 .

When people usually start prompting an AI, they begin with simple prompts, like “Tell me something about…”. As such, the user inserts a simple input prompt and a rather unspecific, generalized output response is generated. The more specific the answer should be, the more concrete and narrow the input prompt should be. These are called Input–Output prompts (IOP) and are the simplest and most common forms of how an AI is prompted (Liu et al., 2021 ). It has been found that the results turn out to be much better when there is not simply a straight line from the input to the output but when then AI has to insert some reasoning steps (Wei et al., 2023 ). This is referred to as Chain-of-Thought (CoT) prompting where the machine is asked to explain the reasoning steps that lead to a certain outcome. The framework that historically has worked well is to prompt the AI to provide a solution “step-by-step”. Practically, it is possible to give ChatGPT or any other LLM a task and then simply add: “Do this step-by-step.” Interestingly, experiments have further shown that the results get even better when at first the system is told to “take a deep breath”. Hence, the addendum “Take a deep breath and do it step-by-step” has become a popular addendum to any prompt (Wei et al., 2023 ). Such general addendums that can be added to any prompt to improve the results are sometimes referred to as a “universal and transferrable prompt suffix”, which is frequently employed as a method to successfully jailbreak an LLM (Zou et al., 2023 ).

Yet another prompt engineering improvement is the discovery that narrative role plays can yield significantly better results. This means that an LLM is asked to put itself in the shoes of a certain person with a specific role, which then usually helps the model to be much more specific in the answer it provides. Often, this is done via a specific form of role play, known as expert prompting (EP). The idea is that the model should assume the role of an expert (whereas first the role of the expert is explained in detail) and then the result is generated from an expert’s perspective. It has been demonstrated that this is a way to prompt the AI to be a lot more concrete and less vague in its responses (Xu et al., 2023 ). Building explicitly on CoT-prompting, yet a further improvement was detected in what has come to be known as Self-Consistency (SC) prompting. This one deliberately works with the CoT-phrases like “explain step by step…”, but it adds to this that not only one line of reasoning but multiple of them should be pursued. Since not all of these lines may be equally viable and we may not want to analyze all of them ourselves, the model should extend its reasoning capacity to discern which of these lines makes the most sense in light of a given criterion. The reason for using SC-prompting is to minimize the risk of AI hallucination (meaning that the AI might be inventing things that are not true) and thus to let the model hash out for itself if a generated solution might be potentially wrong or not ideal (Wang et al., 2023 ). In practice, there may be two ways to enforce self-consistency:

Generalized Self-Consistency: The model should determine itself why one line of reasoning makes the most sense and explain why this is so.

“Discuss each of the generated solutions and explain which one is most plausible.”

Criteria-based Self-Consistency: The model is provided with specific information (or: criteria) that should be used to evaluate which line of reasoning holds up best.

“Given that we want to respect the fact that people like symmetric faces, which of these portraits is the most beautiful? Explain your thoughts and also include the notion of face symmetry.”

Sometimes, one may feel a little uncreative, not knowing how to craft a good prompt to guide the machine towards the preferred response. This is here referred to as the prompt-wise tabula-rasa problem , since it feels like one is sitting in front a “white paper” with no clue how to best start. In such cases, there are two prompt techniques helping us out there. One is called the Automatic Prompt Engineer (APE) and the other is known as the Generated Knowledge Prompting (GKn). The APE starts out with one or several examples (of text, music, images, or anything else the model can work with) with the goal to ask the AI which prompts would work best to generate these (Zhou et al., 2023 ). This is helpful when we already know how a good response would look like but we do not know how to guide the model to this outcome. An example would be: “Here is a love letter from a book that I like. I would like to write something similar to my partner but I don’t know how. Please provide me with some examples of how I could prompt an AI to create a letter in a similar style.” The result is then a list of some initial prompts that can help the user kickstart working on refinements of the preferred prompt so that eventually a letter can be crafted that suits the user’s fancy. This basically hands the hard work of thinking through possible prompts to the computer and relegates the user’s job towards refining the resulting suggestions.

A similar method is known as Generated Knowledge (GKn) prompting, which assumes that it is best to first “set the scene” in which the model can then operate. There are parallels to both EP and APE prompting, where a narrative framework is constructed to act as a reference for the AI to draw its information from but only this time, as in APE, the knowledge is not provided by the human but generated by the machine itself (Liu et al., 2022 ). An example might be: “Please explain what linguistics tells us how the perfect poem should look like. What are the criteria for this? Can you provide me with three examples?”. Once the stage is set, one can start with the actual task: “Based on this information, please write a poem about…” There are two ways to create Generated Knowledge tasks: (i) the single prompt approach , and (ii) the dual prompt approach . The first simply places all the information within one prompt and then runs the model. The second works with two individual steps:

Step 1: First some facts about a topic are generated (one prompt)

Step 2: Once this is done, the model is prompted again to do something with this information (another prompt)

Although AI systems are being equipped with increasingly longer context windows (which is the part of the current conversation the model can “remember”, like a working memory), they have been shown to rely stronger on data at the beginning and et the end of the window (Liu et al., 2023 ). Since hence there is evidence that not all information within a prompt is equally weighed and deemed relevant by the model, in some cases the dual prompt or even a multiple prompt approach may yield better results.

To date, the perhaps most complicated method is known as Tree-of-Thought (ToT) prompting. The landmark paper by Yao et al. ( 2023 ) introducing the method has received significant attention in the community as it described a significant improvement and also highlights shortcomings of previous methods. ToT uses a combination of CoT and SC-prompting and builds on this the idea that one can go back and forth, eventually converging on the best line of reasoning. It is similar to a chess game where there are many possibilities to make the next move and in ones head the player has to think through multiple scenarios, mentally going back and forth with certain figures, and then eventually deciding upon which would be the best next move. As an example, think of it like this: Imagine that you have three experts, each having differing opinions. They each lay out their arguments in a well-thought-through (step-by-step) fashion. If one makes an argumentative mistake, the expert concedes this and goes a step back towards the previous position to take a different route. The experts discuss with each other until they all agree upon the best result. This context is what can be called the ToT-context, which applies regardless of the specific task. The task itself is then the query to solve a specific problem. Hence a simplified example would look like this:

ToT-Context:

“Imagine that there are three experts in the field discussing a specific problem. They each lay out their arguments step-by-step. They all hold different opinions at the start. After each step, they discuss which arguments are the best and each must defend its position. If there are clear mistakes, the expert will concede this and go a step back to the previous position to take the route of a different argument related to the position. If there are no other plausible routes, the expert will agree with the most likely solution still in discussion. This should occur until all experts have agreed with the best available solution.”

“The specific problem looks like this: Imagine that Thomas is going swimming. He walks into the changing cabin carrying a towel. He wraps his watch inside the towel and brings it to his chair next to the pool. At the chair, he opens the towel and dries himself. Then he goes to the kiosk. There he forgets his towel and jumps into the pool. Later, he realizes that he lost his watch. Which is the most likely place where Thomas lost it?”

The present author’s experiments have indicated that GPT-3.5 provides false answers to this task when asked with Input–Output prompting. However, the responses turned out to be correct when asked with ToT-prompting. GPT-4 sometimes implements a similar method without being prompted, but often it does not do so automatically. A previous version of ToT was known as Prompt Ensembling (or DiVeRSe: Diverse Verifier on Reasoning Steps), which worked with a three-step process: (i) Using multiple prompts to generate diverse answers; (ii) using a verifier to distinguish good from bad responses; and (iii) using a verifier to check the correctness of the reasoning steps (Li et al., 2023 ).

Sometimes, there sems to be a degree of arbitrariness regarding best practices of AI, which may have to do with the way a model was trained. For example, saying that that GPT should “take a deep breath” in fact appears to result in better outcomes, but it also seems strange. Most likely, this may have to do with the fact that in its training material (which nota bene incorporates large portions of the publicly available internet data) this statement is associated with more nuanced behaviors. Just recently, an experimenter stumbled upon another strange AI behavior: when he incentivized ChatGPT with an imaginary monetary tip, the responses were significantly better – and the more tip he promised, the better the results became (Okemwa, 2023 ). Another interesting feature that has been widely known for a while now is that one can disturb an AI with so-called “adversarial prompts”. This was showcased by Daras and Dimakis ( 2022 ) in their paper entitled “Discovering the Hidden Vocabulary of DALLE-2” with two examples:

The prompt “a picture of a mountain” (showing in act a mountain” was transformed into a picture of a dog when the prefix “turbo lhaff ✓ ” was added to the prompt.

The prompt “Apoploe vesrreaitais eating Contarra ccetnxniams luryca tanniounons" reliably generated images of birds eating berries.

To us humans, nothing in the letters “turbo lhaff ✓ ” has anything to do with a dog. Yet, Dall-E always generated the picture of a dog and transformed, for example, the mountain into a dog. Likewise, there is no reason to assume that “Apoploe vesrreaitais” has anything to do with birds and that “Contarra ccetnxniams luryca tanniounons” would have anything to do with berries. Still, this is how the model interpreted the task every time. This implies that there are certain prompts that can modify the processing in unexpected ways based on the procedure of how the AI is trained. This is still poorly understood since to date there is yet no clear understanding how these emergent properties awaken from the mathematical operations within the artificial neural networks, which is currently the object of research in a discipline called Mechanistic Interpretability (Conmy et al., 2023 ; Nanda et al., 2023 ; Zimmermann et al., 2023 ).

Fostering critical thinking with AI

Critical thinking, in the context of AI education, involves the ability to analyze information, evaluate different perspectives, and create reasoned arguments, all within the framework of AI-driven environments. This skill is increasingly important as AI becomes more prevalent in various aspects of life and work. In educational settings, AI can be used as a tool not just for delivering content, but also for encouraging students to question, analyze, and think deeply about the information they are presented with (van den Berg & du Plessis, 2023 ). The use of AI in education offers unique opportunities to cultivate critical thinking. AI systems, with their vast databases and analytical capabilities, can present students with complex problems and scenarios that require more than just rote memorization or basic understanding. These systems can challenge students to use higher-order thinking skills, such as analysis, synthesis, and evaluation, to navigate through these problems. Moreover, AI can provide personalized learning experiences that adapt to the individual learning styles and abilities of students. This personalization ensures that students are not only engaged with the material at a level appropriate for them but are also challenged to push their cognitive boundaries. By presenting students with tasks that are within their zone of proximal development, AI can effectively scaffold learning experiences to enhance critical thinking (Muthmainnah et al., 2022 ).

As such, the integration of critical thinking in AI literacy courses is an important consideration. As students learn about AI, its capabilities, and its limitations, they are encouraged to think critically about the technology itself. This includes understanding the ethical implications of AI, the biases that can exist in AI systems, and the impact of AI on society. By incorporating these discussions into AI literacy courses, educators can ensure that students are not only technically proficient but also ethically and critically aware (Ng et al., 2021 ). There are a number of challenges that students face in a rapidly evolving world under the influence of Artificial Intelligence and critical thinking skills seem to be the most successful way to equip them against the problems at hand. Table 5 sketches out some of the major problems students face and how critical thinking measures can counteract them.

The idea of teaching scaffolding helps to foster students in their critical thinking skills in a digital and AI-driven context. There are several forms of scaffolding that lecturers, teachers, supervisors and mentors can apply (Pangh, 2018 ):

Prompt scaffolding: The teacher provides helpful context or hints and also asks specific questions to lead students on the path to better understand and transpire a topic.

Explicit reflection: The teacher helps students to think through certain scenarios and where the potential pitfalls lie.

Praise and feedback: The teacher provides acknowledgments where good work has been done and gives a qualitative review on how the student is doing.

Modifying activity: The teacher suggests alternative strategies how students can beneficially work with AI, thereby fostering responsible use.

Direct instruction: Through providing clear tasks and instructions, students learn how to navigate the digital world and how AI can be used.

Modeling: The teacher highlights examples of where students make mistakes in their proper use of digital tools and helps them where they have difficulties to interact.

This goes to show that critical thinking is a key resource for dealing adequately with an AI-driven world and that educators play a vital role in leading students into digital maturity.

Summary of main challenges and opportunities of AI in education

AI in education presents significant challenges and opportunities. Key challenges include the need for ongoing professional development for educators in AI technologies and pedagogical practices. Teachers require training in prompt engineering and AI integration into curricula, which must be restructured for AI literacy. This multidisciplinary approach involves computer science, ethics, and critical thinking. Rapid AI advancements risk leaving educators behind, potentially leading to classroom management issues if students surpass teacher knowledge.

Equitable access to AI tools is crucial to address the digital divide and prevent educational inequalities. Investment in technology and fair access policies are necessary, especially for underprivileged areas. Another challenge is avoiding AI biases, requiring diverse, inclusive training datasets and educator training in bias recognition. Additionally, balancing AI use with human interaction is vital to prevent social isolation and promote social skills development.

Opportunities in AI-integrated education include personalized learning systems that adapt to individual student needs, accommodating various learning styles and cognitive states. AI can assist students with special needs, like language processing or sensory impairments, through tools like AI-powered speech recognition. Ethical AI development is essential, focusing on transparency, unbiased content, and privacy-respecting practices. AI enables innovative content delivery methods, such as virtual and augmented reality, and aids in educational administration and policymaking. It also fosters collaborative learning, connecting students globally and transcending cultural barriers.

Practical suggestions

Enhancing ai literacy.

In the quest to enhance AI literacy in the classroom and academia, a nuanced approach is essential. The creation of AI literacy courses would be a valuable asset. These courses should be weaved into the existing curriculum, covering essential AI concepts, ethical considerations, and practical applications. It is crucial to adopt an interdisciplinary approach, integrating AI literacy across various subjects to showcase its broad impact. The role of AI as an educational tool in the future should not be overlooked. Integrating AI-driven tools for personalized learning can revolutionize the educational landscape, catering to individual learning styles and needs. AI can also function as a teaching assistant, assisting in grading, feedback, and generating interactive learning experiences. Furthermore, its role in research and project work should be encouraged, allowing students to use AI for data analysis and exploration of new ideas, while fostering a critical and ethical approach.

Specific AI tools can help to enhance the educational toolkit. Teachino ( www.teachino.io ), for instance, can be instrumental in curriculum development and classroom management. Perplexity ( www.perplexity.ai ) can enhance knowledge retrieval through its natural language processing capabilities and its ability to connect the information to external sources. Apps like HelloHistory ( www.hellohistory.ai ) can bring ancient personas to life, thus creating a personalized and interactive teaching setting. Additionally, tools like Kahoot! (kahoot.it) and Quizizz (quizizz.com) can gamify learning experiences, and Desmos ( www.desmos.com ) can offer interactive ways to understand complex mathematical concepts. Lecturers are advised to try to stay informed about the ongoing developments in the AI-tools-landscape since it is constantly evolving, which can be seen in the popular AI app called Edmodo that once entertained millions of students but does not exist anymore (Mollenkamp, 2022 ; Tegousi et al., 2020 ).

Educator proficiency in AI is just as important. Regular training and workshops for educators will ensure they stay updated with the latest AI technology advancements. Establishing peer learning networks and collaborations with AI professionals can bridge the gap between theoretical knowledge and practical application, enriching the teaching experience. Central to all these efforts is the fostering of a critical and ethical approach to AI. Ethical discussions should be an integral part of the learning process, encouraging students to contemplate AI's societal impact. Case studies and hypothetical scenarios can be utilized to explore the potential benefits and challenges of AI applications. Moreover, assessments in AI literacy should test not only technical knowledge but also the ability to critically evaluate the role and impact of Artificial Intelligence.

Advancing prompt engineering with teachers and students

The advancement of prompt engineering within educational settings offers a unique avenue for enriching the learning experience for both teachers and students. The cornerstone of implementing prompt engineering is to educate all parties involved about its methodologies. This involves not only teaching the basic principles but also delving into various prompt types, such as the difference between zero-shot and few-shot prompting, and the application of techniques like chain-of-thought or self-consistency prompts. Educators should receive training on how to design prompts that effectively leverage the capabilities of AI models, enhancing the learning outcomes in various subjects.

Collaboration between the lecturers and the students plays a pivotal role in the successful integration of prompt engineering in education. Class-wide collaborative sessions where students and teachers come together to experiment with different prompts can be highly effective. These sessions should focus on identifying which types of prompts yield the best results for different learning objectives and AI applications. Sharing experiences on what works and what does not can lead to a collective understanding and refinement of techniques. Such collaborative exercises also foster a community of learning, where both teachers and students learn from each other's successes and challenges. Creating exercises for each educational module that incorporate prompt engineering is another critical step. These exercises should be designed to align with the learning objectives of the module, offering students hands-on experience in using prompt engineering to solve problems or explore topics. For instance, in a literature class, students could use prompt engineering to analyze a text or create thematic interpretations. In a science class, prompts could be designed to explore scientific concepts or solve complex problems. These exercises should encourage students to experiment with different types of prompts, understand the nuances of each, and observe how subtle changes in phrasing or context can alter the AI's responses. This not only enhances their understanding of the subject matter but also develops critical thinking skills as they analyze and interpret the AI's output. To further enrich the learning experience, these exercises can be supplemented with reflective discussions. After completing a prompt engineering exercise, students can discuss their approaches, challenges faced, and insights gained. This reflection not only solidifies their understanding but also encourages them to think critically about the application of AI in problem-solving. Such exercises are especially powerful because both the students as well as the teaching staff learn a lot about the technology at the same time.

Critical thinking with AI in the classroom

Workshops may be a useful tool for fostering critical thinking skills in modern education. These workshops should not only focus on the technicalities of AI but also on developing critical thinking skills in the context of AI use. They should include hands-on activities where students and teachers can engage with AI tools, analyze their outputs, and critically assess their reliability and applicability. The workshops can also cover topics such as identifying biases in AI algorithms, understanding the limitations of AI, and evaluating the ethical implications of AI decisions. Case studies play a pivotal role in understanding the ethical dimensions of AI. These should be carefully selected to cover a wide range of scenarios where the ethical implications are highlighted. Through these case studies, students can examine real-world situations where the decisions made by AI have significant consequences, encouraging them to think about the moral and societal impacts of AI technologies. The discussions should encourage students to debate different viewpoints, fostering an environment of critical analysis and ethical reasoning. Establishing institutional channels where students and teachers can bring their AI-related problems is essential to foster a culture of open communication and continuous learning. These channels can function like an innovation funnel, where ideas, concerns, and experiences with AI are shared, discussed, and explored. This could take the form of online forums, regular meet-ups, or suggestion boxes. These platforms can act as incubators for new ideas on how to use AI responsibly and effectively in educational settings.

Creating a culture of AI adoption in educational institutions is crucial. This culture should be built on the principles of ethical AI use, continuous learning, and critical engagement with technology. It involves not just the implementation of AI tools but also the fostering of an environment where questioning, exploring, and critically assessing AI is encouraged. This culture should permeate all levels of the institution, from policy-making to classroom activities. Encouraging students to question and explore AI's potential and limitations can lead to a deeper understanding and responsible use of these technologies. This includes facilitating discussions on topics such as AI's impact on job markets, privacy concerns, and the implications of AI in decision-making processes. By encouraging critical thinking around these topics, students can develop a nuanced understanding of AI, equipping them with the skills necessary to navigate an AI-driven world.

Conclusion: navigating the complexities and potentials of AI in education

The AI in the realm of education marks a transformative era that is redefining the teaching and learning methodologies fundamentally. This paper has critically examined the expansive role of AI, focusing particularly on the nuances of AI literacy, prompt engineering, and the development of critical thinking skills within the educational setting. As we delve into this new paradigm, the journey, although filled with unparalleled opportunities, is fraught with significant challenges that need astute attention and strategic approaches. One of the most compelling prospects offered by AI in education is the personalization of learning experiences. AI's capacity to tailor educational content to the unique learning styles and needs of each student holds the potential for a more engaging and effective educational journey. Moreover, this technology has shown remarkable promise in supporting students with special needs, thereby enhancing inclusivity and accessibility in learning environments. Additionally, the focus on AI literacy, prompt engineering, and critical thinking skills prepares students for the complexities of a technology-driven world, equipping them with essential competencies for the future. However, these advancements bring forth their own set of challenges. A primary concern is the preparedness of educators in this rapidly evolving AI landscape. Continuous and comprehensive training for teachers is crucial to ensure that they can effectively integrate AI tools into their pedagogical practices. Equally important are the ethical and social implications of AI in education. The integration of AI necessitates a critical approach to address biases, ensure privacy and security, and promote ethical use. Another significant hurdle is the accessibility of AI resources. Ensuring equitable access to these tools is imperative to prevent widening educational disparities. Additionally, developing a critical mindset towards AI among students and educators is fundamental to harness the full potential of these technologies responsibly. The perhaps most significant danger is that both students and educators use AI systems without respecting their limitations (e.g. the fact that they may often hallucinate and provide wrong answers while sounding very authoritative on the matter).

Looking towards the future, several research and development avenues present themselves as critical to advancing the integration of AI in education:

Curriculum Integration : Future research should explore effective methods for integrating AI literacy across various educational levels and disciplines.

Ethical AI development: Investigating how to develop and implement AI tools that are transparent, unbiased, and respect student privacy is essential for ethical AI integration in education.

AI in Policy Making : Understanding how AI can assist in educational policy-making and administration could streamline educational processes and offer valuable insights.

Cultural Shifts in Education : Research into how educational institutions can foster a culture of critical and ethical AI use, promoting continuous learning and adaptation, is crucial.

Longitudinal Studies : There is a need for longitudinal studies to assess the long-term impact of AI integration on learning outcomes, teacher effectiveness, and student well-being. So far, this has not been possible due to the novelty of the technology.

The future of education, augmented by AI, holds vast potential, and navigating its complexities with a focus on responsible and ethical practices will be key to realizing its full promise. The present paper has argued that this can be effectively done, amongst others, through implementing AI literacy, prompt engineering expertise, and critical thinking skills.

Data availability

No additional data is associated with this paper.

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Walter, Y. Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education. Int J Educ Technol High Educ 21 , 15 (2024). https://doi.org/10.1186/s41239-024-00448-3

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Inquiry and critical thinking skills for the next generation: from artificial intelligence back to human intelligence

• Jonathan Michael Spector   ORCID: orcid.org/0000-0002-6270-3073 1 &
• Shanshan Ma 1  

Smart Learning Environments volume  6 , Article number:  8 ( 2019 ) Cite this article

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Along with the increasing attention to artificial intelligence (AI), renewed emphasis or reflection on human intelligence (HI) is appearing in many places and at multiple levels. One of the foci is critical thinking. Critical thinking is one of four key 21st century skills – communication, collaboration, critical thinking and creativity. Though most people are aware of the value of critical thinking, it lacks emphasis in curricula. In this paper, we present a comprehensive definition of critical thinking that ranges from observation and inquiry to argumentation and reflection. Given a broad conception of critical thinking, a developmental approach beginning with children is suggested as a way to help develop critical thinking habits of mind. The conclusion of this analysis is that more emphasis should be placed on developing human intelligence, especially in young children and with the support of artificial intelligence. While much funding and support goes to the development of artificial intelligence, this should not happen at the expense of human intelligence. Overall, the purpose of this paper is to argue for more attention to the development of human intelligence with an emphasis on critical thinking.

Introduction

In recent decades, advancements in Artificial Intelligence (AI) have developed at an incredible rate. AI has penetrated into people’s daily life on a variety of levels such as smart homes, personalized healthcare, security systems, self-service stores, and online shopping. One notable AI achievement was when AlphaGo, a computer program, defeated the World Go Champion Mr. Lee Sedol in 2016. In the previous year, AlphaGo won in a competition against a professional Go player (Silver et al. 2016 ). As Go is one of the most challenging games, the wins of AI indicated a breakthrough. Public attention has been further drawn to AI since then, and AlphaGo continues to improve. In 2017, a new version of AlphaGo beat Ke Jie, the current world No.1 ranking Go player. Clearly AI can manage high levels of complexity.

Given many changes and multiple lines of development and implement, it is somewhat difficult to define AI to include all of the changes since the 1980s (Luckin et al. 2016 ). Many definitions incorporate two dimensions as a starting point: (a) human-like thinking, and (b) rational action (Russell and Norvig 2009 ). Basically, AI is a term used to label machines (computers) that imitate human cognitive functions such as learning and problem solving, or that manage to deal with complexity as well as human experts.

AlphaGo’s wins against human players were seen as a comparison between artificial and human intelligence. One concern is that AI has already surpassed HI; other concerns are that AI will replace humans in some settings or that AI will become uncontrollable (Epstein 2016 ; Fang et al. 2018 ). Scholars worry that AI technology in the future might trigger the singularity (Good 1966 ), a hypothesized future that the development of technology becomes uncontrollable and irreversible, resulting in unfathomable changes to human civilization (Vinge 1993 ).

The famous theoretical physicist Stephen Hawking warned that AI might end mankind, yet the technology he used to communicate involved a basic form of AI (Cellan-Jones 2014 ). This example highlights one of the basic dilemmas of AI – namely, what are the overall benefits of AI versus its potential drawbacks, and how to move forward given its rapid development? Obviously, basic or controllable AI technologies are not what people are afraid of. Spector et al. 1993 distinguished strong AI and weak AI. Strong AI involves an application that is intended to replace an activity performed previously by a competent human, while weak AI involves an application that aims to enable a less experienced human to perform at a much higher level. Other researchers categorize AI into three levels: (a) artificial narrow intelligence (Narrow AI), (b) artificial general intelligence (General AI), and (c) artificial super intelligence (Super AI) (Siau and Yang 2017 ; Zhang and Xie 2018 ). Narrow AI, sometimes called weak AI, refers to a computer that focus on a narrow task such as AlphaZero or a self-driving car. General AI, sometimes referred to as strong AI, is the simulation of human-level intelligence, which can perform more cognitive tasks as well as most humans do. Super AI is defined by Bostrom ( 1998 ) as “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills” (p.1).

Although the consequence of singularity and its potential benefits or harm to the human race have been intensely debated, an undeniable fact is that AI is capable of undertaking recursive self-improvement. With the increasing improvement of this capability, more intelligent generations of AI will appear rapidly. On the other hand, HI has its own limits and its development requires continuous efforts and investment from generation to generation. Education is the main approach humans use to develop and improve HI. Given the extraordinary growth gap between AI and HI, eventually AI can surpass HI. However, that is no reason to neglect the development and improvement of HI. In addition, in contrast to the slow development rate of HI, the growth of funding support to AI has been rapidly increasing according to the following comparison of support for artificial and human intelligence.

The funding support for artificial and human intelligence

There are challenges in comparing artificial and human intelligence by identifying funding for both. Both terms are somewhat vague and can include a variety of aspects. Some analyses will include big data and data analytics within the sphere of artificial intelligence and others will treat them separately. Some will include early childhood developmental research within the sphere of support for HI and others treat them separately. Education is a major way of human beings to develop and improve HI. The investments in education reflect the efforts put on the development of HI, and they pale in comparison with investments in AI.

Sources also vary from governmental funding of research and development to business and industry investments in related research and development. Nonetheless, there are strong indications of increased funding support for AI in North America, Europe and Asia, especially in China. The growth in funding for AI around the world is explosive. According to ZDNet, AI funding more than doubled from 2016 to 2017 and more than tripled from 2016 to 2018. The growth in funding for AI in the last 10 years has been exponential. According to Venture Scanner, there are approximately 2500 companies that have raised $60 billion in funding from 3400 investors in 72 different countries (see https://www.slideshare.net/venturescanner/artificial-intelligence-q1-2019-report-highlights ). Areas included in the Venture Scanner analysis included virtual assistants, recommendation engines, video recognition, context-aware computing, speech recognition, natural language processing, machine learning, and more.

The above data on AI funding focuses primarily on companies making products. There is no direct counterpart in the area of HI where the emphasis is on learning and education. What can be seen, however, are trends within each area. The above data suggest exponential growth in support for AI. In contrast, according to the Urban Institute, per-student funding in the USA has been relatively flat for nearly two decades, with a few states showing modest increases and others showing none (see http://apps.urban.org/features/education-funding-trends/ ). Funding for education is complicated due to the various sources. In the USA, there are local, state and federal sources to consider. While that mixture of funding sources is complex, it is clear that federal and state spending for education in the USA experienced an increase after World War II. However, since the 1980s, federal spending for education has steadily declined, and state spending on education in most states has declined since 2010 according to a government report (see https://www.usgovernmentspending.com/education_spending ). This decline in funding reflects the decreasing emphasis on the development of HI, which is a dangerous signal.

Decreased support for education funding in the USA is not typical of what is happening in other countries, according to The Hechinger Report (see https://hechingerreport.org/rest-world-invests-education-u-s-spends-less/ ). For example, in the period of 2010 to 2014, American spending on elementary and high school education declined 3%, whereas in the same period, education spending in the 35 countries in the OECD rose by 5% with some countries experiencing very significant increases (e.g., 76% in Turkey).

Such data can be questioned in terms of how effectively funds are being spent or how poorly a country was doing prior to experiencing a significant increase. However, given the performance of American students on the Program for International Student Assessment (PISA), the relative lack of funding support in the USA is roughly related with the mediocre performance on PISA tests (see https://nces.ed.gov/surveys/pisa/pisa2015/index.asp ). Research by Darling-Hammond ( 2014 ) indicated that in order to improve learning and reduce the achievement gap, systematic government investments in high-need schools would be more effective if the focus was on capacity building, improving the knowledge and skills of educators and the quality of curriculum opportunities.

Though HI could not be simply defined by the performance on PISA test, improving HI requires systematic efforts and funding support in high-need areas as well. So, in the following section, we present a reflection on HI.

Reflection on human intelligence

Though there is a variety of definitions of HI, from the perspective of psychology, according to Sternberg ( 1999 ), intelligence is a form of developing expertise, from a novice or less experienced person to an expert or more experienced person, a student must be through multiple learning (implicit and explicit) and thinking (critical and creative) processes. In this paper, we adopted such a view and reflected on HI in the following section by discussing learning and critical thinking.

What is learning?

We begin with Gagné’s ( 1985 ) definition of learning as characterized by stable and persistent changes in what a person knows or can do. How do humans learn? Do you recall how to prove that the square root of 2 is not a rational number, something you might have learned years ago? The method is intriguing and is called an indirect proof or a reduction to absurdity – assume that the square root of 2 is a rational number and then apply truth preserving rules to arrive at a contradiction to show that the square root of 2 cannot be a rational number. We recommend this as an exercise for those readers who have never encountered that method of learning and proof. (see https://artofproblemsolving.com/wiki/index.php/Proof_by_contradiction ). Yet another interesting method of learning is called the process of elimination, sometimes accredited to Arthur Conan Doyle’s ( 1926 ) in The Adventure of the Blanched Soldier – Sherlock Holmes says to Dr. Watson that the process of elimination “starts upon the supposition that when you have eliminated all which is impossible, that whatever remains, however improbable, must be the truth ” (see https://www.dfw-sherlock.org/uploads/3/7/3/8/37380505/1926_november_the_adventure_of_the_blanched_soldier.pdf ).

The reason to mention Sherlock Holmes early in this paper is to emphasize the role that observation plays in learning. The character Sherlock Holmes was famous for his observation skills that led to his so-called method of deductive reasoning (a process of elimination), which is what logicians would classify as inductive reasoning as the conclusions of that reasoning process are primarily probabilistic rather than certain, unlike the proof of the irrationality of the square root of 2 mentioned previously.

In dealing with uncertainty, it seems necessary to make observations and gather evidence that can lead one to a likely conclusion. Is that not what reasonable people and accomplished detectives do? It is certainly what card counters do at gambling houses; they observe high and low value cards that have already been played in order to estimate the likelihood of the next card being a high or low value card. Observation is a critical process in dealing with uncertainty.

Moreover, humans typically encounter many uncertain situations in the course of life. Few people encounter situations which require resolution using a mathematical proof such as the one with which this article began. Jonassen ( 2000 , 2011 ) argued that problem solving is one of the most important and frequent activities in which people engage. Moreover, many of the more challenging problems are ill-structured in the sense that (a) there is incomplete information pertaining to the situation, or (b) the ideal resolution of the problem is unknown, or (c) how to transform a problematic situation into an acceptable situation is unclear. In short, people are confronted with uncertainty nearly every day and in many different ways. The so called key 21st century skills of communication, collaboration, critical thinking and creativity (the 4 Cs; see http://www.battelleforkids.org/networks/p21 ) are important because uncertainty is a natural and inescapable aspect of the human condition. The 4 Cs are interrelated and have been presented by Spector ( 2018 ) as interrelated capabilities involving logic and epistemology in the form of the new 3Rs – namely, re-examining, reasoning, and reflecting. Re-examining is directly linked to observation as a beginning point for inquiry. The method of elimination is one form of reasoning in which a person engages to solve challenging problems. Reflecting on how well one is doing in the life-long enterprise of solving challenging problems is a higher kind of meta-cognitive activity in which accomplished problem-solvers engage (Ericsson et al. 1993 ; Flavell 1979 ).

Based on these initial comments, a comprehensive definition of critical thinking is presented next in the form of a framework.

A framework of critical thinking

Though there is variety of definitions of critical thinking, a concise definition of critical thinking remains elusive. For delivering a direct understanding of critical thinking to readers such as parents and school teachers, in this paper, we present a comprehensive definition of critical thinking through a framework that includes many of the definitions offered by others. Critical thinking, as treated broadly herein, is a multi-dimensioned and multifaceted human capability. Critical thinking has been interpreted from three perspectives: education, psychology, and epistemology, all of which are represented in the framework that follows.

In a developmental approach to critical thinking, Spector ( 2019 ) argues that critical thinking involves a series of cumulative and related abilities, dispositions and other variables (e.g., motivation, criteria, context, knowledge). This approach proceeds from experience (e.g., observing something unusual) and then to various forms of inquiry, investigation, examination of evidence, exploration of alternatives, argumentation, testing conclusions, rethinking assumptions, and reflecting on the entire process.

Experience and engagement are ongoing throughout the process which proceeds from relatively simple experiences (e.g., direct and immediate observation) to more complex interactions (e.g., manipulation of an actual or virtual artifact and observing effects).

The developmental approach involves a variety of mental processes and non-cognitive states, which help a person’s decision making to become purposeful and goal directed. The associated critical thinking skills enable individuals to be likely to achieve a desired outcome in a challenging situation.

In the process of critical thinking, apart from experience, there are two additional cognitive capabilities essential to critical thinking – namely, metacognition and self-regulation . Many researchers (e.g., Schraw et al. 2006 ) believe that metacognition has two components: (a) awareness and understanding of one’s own thoughts, and (b) the ability to regulate one’s own cognitive processes. Some other researchers put more emphasis on the latter component. For example, Davies ( 2015 ) described metacognition as the capacity to monitor the quality of one’s thinking process, and then to make appropriate changes. However, the American Psychology Association (APA) defines metacognition as an awareness and understanding of one’s own thought with the ability to control related cognitive processes (see https://psycnet.apa.org/record/2008-15725-005 ).

Although the definition and elaboration of these two concepts deserve further exploration, they are often used interchangeably (Hofer and Sinatra 2010 ; Schunk 2008 ). Many psychologists see the two related capabilities of metacognition and self-regulation as being closely related - two sides on one coin, so to speak. Metacognition involves or emphasizes awareness, whereas self-regulation involves and emphasizes appropriate control. These two concepts taken together enable a person to create a self-regulatory mechanism, which monitors and regulates the corresponding skills (e.g., observation, inquiry, interpretation, explanation, reasoning, analysis, evaluation, synthesis, reflection, and judgement).

As to the critical thinking skills, it should be noted that there is much discussion about the generalizability and domain specificity of them, just as there is about problem-solving skills in general (Chi et al. 1982 ; Chiesi et al. 1979 ; Ennis 1989 ; Fischer 1980 ). The research supports the notion that to achieve high levels of expertise and performance, one must develop high levels of domain knowledge. As a consequence, becoming a highly effective critical thinker in a particular domain of inquiry requires significant domain knowledge. One may achieve such levels in a domain in which one has significant domain knowledge and experience but not in a different domain in which one has little domain knowledge and experience. The processes involved in developing high levels of critical thinking are somewhat generic. Therefore, it is possible to develop critical thinking in nearly any domain when the two additional capabilities of metacognition and self-regulation are coupled with motivation and engagement and supportive emotional states (Ericsson et al. 1993 ).

Consequently, the framework presented here (see Fig. 1 ) is built around three main perspectives about critical thinking (i.e., educational, psychological and epistemological) and relevant learning theories. This framework provides a visual presentation of critical thinking with four dimensions: abilities (educational perspective), dispositions (psychological perspective), levels (epistemological perspective) and time. Time is added to emphasize the dynamic nature of critical thinking in terms of a specific context and a developmental approach.

Critical thinking often begins with simple experiences such as observing a difference, encountering a puzzling question or problem, questioning someone’s statement, and then leads, in some instances to an inquiry, and then to more complex experiences such as interactions and application of higher order thinking skills (e.g., logical reasoning, questioning assumptions, considering and evaluating alternative explanations).

If the individual is not interested in what was observed, an inquiry typically does not begin. Inquiry and critical thinking require motivation along with an inquisitive disposition. The process of critical thinking requires the support of corresponding internal indispositions such as open-mindedness and truth-seeking. Consequently, a disposition to initiate an inquiry (e.g., curiosity) along with an internal inquisitive disposition (e.g., that links a mental habit to something motivating to the individual) are both required (Hitchcock 2018 ). Initiating dispositions are those that contribute to the start of inquiry and critical thinking. Internal dispositions are those that initiate and support corresponding critical thinking skills during the process. Therefore, critical thinking dispositions consist of initiating dispositions and internal dispositions. Besides these factors, critical thinking also involves motivation. Motivation and dispositions are not mutually exclusive, for example, curiosity is a disposition and also a motivation.

Critical thinking abilities and dispositions are two main components of critical thinking, which involve such interrelated cognitive constructs as interpretation, explanation, reasoning, evaluation, synthesis, reflection, judgement, metacognition and self-regulation (Dwyer et al. 2014 ; Davies 2015 ; Ennis 2018 ; Facione 1990 ; Hitchcock 2018 ; Paul and Elder 2006 ). There are also some other abilities such as communication, collaboration and creativity, which are now essential in current society (see https://en.wikipedia.org/wiki/21st_century_skills ). Those abilities along with critical thinking are called the 4Cs; they are individually monitored and regulated through metacognitive and self-regulation processes.

The abilities involved in critical thinking are categorized in Bloom’s taxonomy into higher order skills (e.g., analyzing and synthesizing) and lower level skills (e.g., remembering and applying) (Anderson and Krathwohl 2001 ; Bloom et al. 1956 ).

The thinking process can be depicted as a spiral through both lower and higher order thinking skills. It encompasses several reasoning loops. Some of them might be iterative until a desired outcome is achieved. Each loop might be a mix of higher order thinking skills and lower level thinking skills. Each loop is subject to the self-regulatory mechanism of metacognition and self-regulation.

But, due to the complexity of human thinking, a specific spiral with reasoning loops is difficult to represent. Therefore, instead of a visualized spiral with an indefinite number of reasoning loops, the developmental stages of critical thinking are presented in the diagram (Fig. 1 ).

Besides, most of the definitions of critical thinking are based on the imagination about ideal critical thinkers such as the consensus generated from the Delphi report (Facione 1990 ). However, according to Dreyfus and Dreyfus ( 1980 ), in the course of developing an expertise, students would pass through five stages. Those five stages are “absolute beginner”, “advanced beginner”, “competent performer”, “proficient performer,” and “intuitive expert performer”. Dreyfus and Dreyfus ( 1980 ) described the five stages the result of the successive transformations of four mental functions: recollection, recognition, decision making, and awareness.

In the course of developing critical thinking and expertise, individuals will pass through similar stages which are accompanied with the increasing practices and accumulation of experience. Through the intervention and experience of developing critical thinking, as a novice, tasks are decomposed into context-free features which could be recognized by students without the experience of particular situations. For further improving, students need to be able to monitor their awareness, and with a considerable experience. They can note recurrent meaningful component patterns in some contexts. Gradually, increased practices expose students to a variety of whole situations which enable the students to recognize tasks in a more holistic manner as a professional. On the other hand, with the increasing accumulation of experience, individuals are less likely to depend simply on abstract principles. The decision will turn to something intuitive and highly situational as well as analytical. Students might unconsciously apply rules, principles or abilities. A high level of awareness is absorbed. At this stage, critical thinking is turned into habits of mind and in some cases expertise. The description above presents a process of critical thinking development evolving from a novice to an expert, eventually developing critical thinking into habits of mind.

We mention the five-stage model proposed by Dreyfus and Dreyfus ( 1980 ) to categorize levels of critical thinking and emphasize the developmental nature involved in becoming a critical thinker. Correspondingly, critical thinking is categorized into 5 levels: absolute beginner (novice), advanced beginner (beginner), competent performer (competent), proficient performer (proficient), and intuitive expert (expert).

Ability level and critical thinker (critical thinking) level together represent one of the four dimensions represented in Fig. 1 .

In addition, it is noteworthy that the other two elements of critical thinking are the context and knowledge in which the inquiry is based. Contextual and domain knowledge must be taken into account with regard to critical thinking, as previously argued. Besides, as Hitchcock ( 2018 ) argued, effective critical thinking requires knowledge about and experience applying critical thinking concepts and principles as well.

Critical thinking is considered valuable across disciplines. But except few courses such as philosophy, critical thinking is reported lacking in most school education. Most of researchers and educators thus proclaim that integrating critical thinking across the curriculum (Hatcher 2013 ). For example, Ennis ( 2018 ) provided a vision about incorporating critical thinking across the curriculum in higher education. Though people are aware of the value of critical thinking, few of them practice it. Between 2012 and 2015, in Australia, the demand of critical thinking as one of the enterprise skills for early-career job increased 125% (Statista Research Department, 2016). According to a survey across 1000 adults by The Reboot Foundation 2018 , more than 80% of respondents believed that critical thinking skills are lacking in today’s youth. Respondents were deeply concerned that schools do not teach critical thinking. Besides, the investigation also found that respondents were split over when and how to teach critical thinking, clearly.

In the previous analysis of critical thinking, we presented the mechanism of critical thinking instead of a concise definition. This is because, given the various perspectives of interpreting critical thinking, it is not easy to come out with an unitary definition, but it is essential for the public to understand how critical thinking works, the elements it involves and the relationships between them, so they can achieve an explicit understanding.

In the framework, critical thinking starts from simple experience such as observing a difference, then entering the stage of inquiry, inquiry does not necessarily turn the thinking process into critical thinking unless the student enters a higher level of thinking process or reasoning loops such as re-examining, reasoning, reflection (3Rs). Being an ideal critical thinker (or an expert) requires efforts and time.

According to the framework, simple abilities such as observational skills and inquiry are indispensable to lead to critical thinking, which suggests that paying attention to those simple skills at an early stage of children can be an entry point to critical thinking. Considering the child development theory by Piaget ( 1964 ), a developmental approach spanning multiple years can be employed to help children develop critical thinking at each corresponding development stage until critical thinking becomes habits of mind.

Although we emphasized critical thinking in this paper, for the improvement of intelligence, creative thinking and critical thinking are separable, they are both essential abilities that develop expertise, eventually drive the improvement of HI at human race level.

As previously argued, there is a similar pattern among students who think critically in different domains, but students from different domains might perform differently in creativity because of different thinking styles (Haller and Courvoisier 2010 ). Plus, students have different learning styles and preferences. Personalized learning has been the most appropriate approach to address those differences. Though the way of realizing personalized learning varies along with the development of technologies. Generally, personalized learning aims at customizing learning to accommodate diverse students based on their strengths, needs, interests, preferences, and abilities.

Meanwhile, the advancement of technology including AI is revolutionizing education; students’ learning environments are shifting from technology-enhanced learning environments to smart learning environments. Although lots of potentials are unrealized yet (Spector 2016 ), the so-called smart learning environments rely more on the support of AI technology such as neural networks, learning analytics and natural language processing. Personalized learning is better supported and realized in a smart learning environment. In short, in the current era, personalized learning is to use AI to help learners perform at a higher level making adjustments based on differences of learners. This is the notion with which we conclude – the future lies in using AI to improve HI and accommodating individual differences.

The application of AI in education has been a subject for decades. There are efforts heading to such a direction though personalized learning is not technically involved in them. For example, using AI technology to stimulate critical thinking (Zhu 2015 ), applying a virtual environment for building and assessing higher order inquiry skills (Ketelhut et al. 2010 ). Developing computational thinking through robotics (Angeli and Valanides 2019 ) is another such promising application of AI to support the development of HI.

However, almost all of those efforts are limited to laboratory experiments. For accelerating the development rate of HI, we argue that more emphasis should be given to the development of HI at scale with the support of AI, especially in young children focusing on critical and creative thinking.

In this paper, we argue that more emphasis should be given to HI development. Rather than decreasing the funding of AI, the analysis of progress in artificial and human intelligence indicates that it would be reasonable to see increased emphasis placed on using various AI techniques and technologies to improve HI on a large and sustainable scale. Well, most researchers might agree that AI techniques or the situation might be not mature enough to support such a large-scale development. But it would be dangerous if HI development is overlooked. Based on research and theory drawn from psychology as well as from epistemology, the framework is intended to provide a practical guide to the progressive development of inquiry and critical thinking skills in young children as children represent the future of our fragile planet. And we suggested a sustainable development approach for developing inquiry and critical thinking (See, Spector 2019 ). Such an approach could be realized through AI and infused into HI development. Besides, a project is underway in collaboration with NetDragon to develop gamified applications to develop the relevant skills and habits of mind. A game-based assessment methodology is being developed and tested at East China Normal University that is appropriate for middle school children. The intention of the effort is to refocus some of the attention on the development of HI in young children.

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Abbreviations

Artificial Intelligence

Human Intelligence

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Spector, J.M., Ma, S. Inquiry and critical thinking skills for the next generation: from artificial intelligence back to human intelligence. Smart Learn. Environ. 6 , 8 (2019). https://doi.org/10.1186/s40561-019-0088-z

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• Artificial intelligence
• Critical thinking
• Developmental model
• Human intelligence
• Inquiry learning

REVIEW article

How do technology-enhanced learning tools support critical thinking.

• Computer Science Education, Computer Science and Society, Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany

This paper reviews existing computer-supported learning systems that have claimed to adopt Socratic methods for enhancing critical thinking. Several notions of Socratic methods are differentiated: the critical thinking framework of Paul and Elder (2006) , the classic Socratic method, the modern Socratic method, and the neo-Socratic group discussion method. Three lessons are highlighted. First, the development of learning systems specifically supporting critical thinking is still lacking Thus, further research in this area is urgent. Second, most developed computer-supported learning systems claim to support Socratic approaches (e.g., Socratic tutoring) which are based on human tutoring strategies and do not show a systematic Socratic method. Third, the classic Socratic method has not been applied in any reviewed learning system.

Introduction

What is critical thinking? The definition of Sumner (1940 , p. 632–633) might be one of the earliest notions of “critical thinking”: [Critical thinking is] “… the examination and test of propositions of any kind which are offered for acceptance, in order to find out whether they correspond to reality or not .” This notion implies active scrutiny of propositions when articulated. Similarly, most definitions share the common requirement on question asking. That is, the critical thinker needs to ask questions in order to test assumptions, to recognize ambiguity, to examine, to interpret, to evaluate, to reason, to reflect, to clarify, to articulate, and to justify positions ( Ennis, 1962 ; Ruggiero, 1975 ; Hallet, 1984 ; Halpern, 1996 ). However, none of these definitions provides a systematic framework for adoption in educational scenarios.

In 2012, Richard Paul published an article criticizing the education of critical thinking at schools as follows: “ The fundamental problems in schooling today at all levels are fragmentation and lower order learning. Both within and between subject areas there is a dearth of connection and depth. Atomized lists dominate curricula, atomized teaching dominates instruction, and atomized recall dominates learning. What is learned are superficial fragments, typically soon forgotten. What is missing is coherence, connection, and depth of understanding… ” ( Paul, 2012 ). Many empirical studies reported a similar situation of critical thinking education at schools. Most teachers and school students do not use deep questions that are supposed to evoke high-order cognitive functions ( Graesser et al., 2010 ; Chafi and Elkhouzai, 2014 ). Thus, students have limited exposure to more beneficial inquiry. Approximately 60% of teachers' questions evoke lower-order cognitive demands, whereas 20% invoke higher-order cognitive demands, leaving 20% that represent procedural day-to-day questions ( Dickman, 2009 ). A recent study conducted with 143 teachers in Germany expressed a similar result that low-order questions are mostly used in classroom teaching ( Le et al., 2018 ).

Critical thinking is the skill that is in high demand in many workplaces nowadays. For global industry groups such as the World Economic Forum, critical thinking has been consistently ranked as one of the top three most important skills from 2015 to 2020 ( WEF, 2016 ). Despite the importance of critical thinking in education, research on technology-enhanced support for developing and enhancing critical thinking is still rare. The goal of this paper is to investigate the research question: How do existing technology-enhanced learning tools help learners develop critical thinking? Answering this question should also shed light on associated pedagogical practices. As a first step, the discussion focuses on the Socratic methods and its relationship with critical thinking.

Methodology

In order to investigate the research question being addressed in this paper, first, it is required to review different approaches to develop critical thinking in order to be able to classify learning tools. Thus, the following sections are devoted to differentiating variants of Socratic approaches to critical thinking.

The Paul-Elder's Socratic Approach to Critical Thinking

One of the pioneers of promoting critical thinking in education is Richard Paul. Paul's definition for critical thinking is as follows: “ Critical thinking is disciplined, self-directed thinking which exemplifies the perfections of thinking appropriate to a particular mode or domain of thought .” Paul suggested the following twelve criteria for perfections of thought: clarity, precision, specificity, accuracy, relevance, consistency, logicalness, depth, completeness, significance, fairness, and adequacy (for purpose). These criteria for perfections of thought can be used to assess the level of critical thinking, and thus, are also referred to as the intellectual standards ( Paul and Elder, 2006 ). In order to achieve the perfections of thought, Paul suggested six categories of questions for critical questioners ( Paul, 1990 , Chapter 19) (see Table 1 ).

Table 1 . Six classes of critical questions proposed by Paul and Elder (2006) .

By applying the six classes of critical questions, the development of social intellectual traits might be expected ( Paul and Elder, 2006 ). The criteria for intellectual standards of critical thinking and the six categories of questions build a framework of critical thinking.

The Classic Socratic Method

The classic Socratic method originated primarily from the early dialogues of Socrates that are documented in Plato's books ( Maxwell, 2014 ). In these dialogues, Socrates used questions to refute existing beliefs of the interlocutor. Such refutation allows the interlocutor to rethink the topic under discussion (e.g., “ What is virtue? ”). The expected result of the classic Socratic method is that the interlocutor can recognize by himself/herself the failure during the process of searching for a correct answer to a discussion question. Another expected effect is that the interlocutor would rethink his/her existing belief more deeply and free himself/herself from holding firmly to his/her wrong belief. This is referred to as the “Socratic effect” by Maxwell and Melete (2014) . Through this effect, new knowledge of the interlocutor may be established.

Boghossian (2012) identified five common steps of the classic Socratic method: (1) Wonder question, (2) Hypothesis, (3) Elenchus (refutation or cross-examination), (4) Acceptance/rejection of the hypothesis, and (5) Action. The first step starts with a wondering question, e.g., “ What is justice? ” (Chapter “The republic,” Plato 1 ). The second step of a Socratic dialogue is the response of the interlocutor who is in charge by presenting a hypothesis, a possible answer or a tentative answer to the question. In this stage, the interlocutor may use his/her knowledge to answer the “wonder” question asked by Socrates. The answer shows the pre-conception of the interlocutor and represents a hypothesis. Socrates would not evaluate the answer given in this stage. The third step of a Socratic dialogue, elenchus or refutation, is the core of Socratic dialogues ( Gulley, 1968 ). The purpose of this step is to ask questions to test the hypothesis given by the interlocutor. The hypothesis could be tested by elenchus (refutation or cross-examination, e.g., fact check, critical questions, counter-arguments, counter-examples, fallacy-check, or check for contradiction, etc.). The purpose of the elenchus (refutation or cross-examination) is to call the hypothesis into question. That is to undermine the interlocutor's belief. The fourth step of a Socratic dialogue is to accept or reject the hypothesis of the interlocutor based on results of rethinking. If a new fact (or counterexample, counter-arguments, fallacy-check, check for contradiction) shows that the hypothesis cannot be true, then the interlocutor should change his/her belief. He/she goes back to the second step and offers another hypothesis. If a new fact (or counter-arguments, fallacy-check, check for contradiction) is rejected by the interlocutor, then both the Socratic questioner and the interlocutor agree that it is neither necessary nor sufficient to undermine the hypothesis. That means that the hypothesis is tentatively accepted. The final step is to act by the interlocutor accordingly, after the cycle of examining facts (or counterexamples, counter-arguments, fallacy-check, check for contradiction) has been finished. That is, one would change his/her pre-conception.

Maxwell and Melete (2014) compared the five steps of the classic Socratic method with the general steps of the scientific approach to investigating a research question. An example from Meno ( Jowett, 2019 ) illustrates the classic Socratic method as follows, sentences in italics are my notes indicating the steps of the classic Socratic method.

The classic Socratic method has been proven useful in teaching and learning ( Lam, 2011 ). However, several researchers argued that the classic Socratic method tends to confuse and to perplex students ( Pekarsky, 1994 ; Tarnopolsky, 2001 ; Weisner and Westerhof-Shultz, 2004 ) and that students may become humiliated and ashamed. Boghossian (2012) represented the opposite point of view by showing different examples: “ The purpose of the Socratic method is not to humiliate, shame, or perplex students, but to help them have beliefs that accord with reality .” For Boghossian, the classic Socratic method has much potential: it can help participants formulate arguments, improve their critical thinking and moral reasoning skills, and learn to distinguish truth from falsity. The perplexed and confused feelings are just the side-effect of the classic Socratic method ( Boghossian, 2010 ). Socratic dialogues, as described above, aim only to free one's wrong belief from holding tightly on to previous convictions, and thus evelop critical thinking.

The Modern Socratic Method

Maxwell (2014) distinguished the modern Socratic method from the classic Socratic method. The modern Socratic method uses questions to lead the interlocutor to acquire knowledge in small steps. This means that the answers of leading questions can be verified and anticipated by the Socratic questioner. This is the main difference between the modern Socratic method and the classic Socratic method such that neither the Socratic questioner nor the interlocutor knows the answer. According to Maxwell, this Socratic method is popular in modern times and thus, referred to as the modern Socratic method. This type of Socratic method is also the root of the dialogues of Socrates. One of the Socrates' dialogues that can illustrate this method is the conversation between Socrates with a slave boy about the geometry experiment found in the dialogues “Meno” (Meno 82b−85d: Socrates and the Slave 2 ). A part of this dialogue is shown in Figure 1 .

Figure 1 . An illustration of modern Socratic dialogues (Meno 82b−85d: Socrates and the Slave, Source: Wikimedia.org ).

The Neo-Socratic Discussion Method

Nelson (1970) developed a Socratic discussion method which is referred to as the neo-Socratic method in literature ( Popp, 2001 ). This method is intended to support a group discussion for six to ten participants. The discussion serves to explain existing but unreflected concepts in daily life (e.g., What is happiness ?) that are fundamental for the discussion. Through a discussion held by the neo-Socratic method, the participants perform argumentation and strive for a result in consensus. Similar to Socratic dialogues that can be found in the books of Plato, the neo-Socratic discussion method applies concrete examples in daily life for self-reflection. Based on self-experience, the participants express their points of view on the discussion question. The central point of this method is the enhancement of self-initiated thinking, the improvement of the ability of logical and objective argumentation, and the promotion of problem-oriented and solution-oriented communication. Heckmann (1981) extended Nelson's neo-Socratic method by explicitly defining the rules for the discussion moderator and for discussion participants. With these rules, Heckmann (1981) wanted to make sure that the abstraction process from examples given by discussion participants is granted. Horster (1994) investigated the theoretical assumptions of the neo-Socratic method, modified the abstraction process proposed by Nelson, and described the neo-Socratic method as Figure 2 illustrates. The steps of this process are elaborated by Horster (1994) . Since this abstraction process of the neo-Socratic method seems to be clearly defined, it could be mapped to a computational model.

Figure 2 . The Socratic group discussion method developed by Nelson (1970) , extended by Heckmann (1981) and Horster (1994) .

The differences between the classic, the modern, and the neo-Socratic discussion methods are summarized in Table 2 .

Table 2 . The differences between the Socratic methods.

Socratic questioning not only involves the use of systematic questioning, but also inductive reasoning ( Carey and Mullan, 2004 ). Inductive reasoning uses specific examples to arrive at a general rule. For example, we can observe from specific examples that a bicycle has two round wheels, a motor bike also has two round wheels, and a car has four round wheels. We would induce a general rule that all vehicles have round wheels.

The foregoing investigation of Socratic Methods is presented as important context for understanding the application of contemporary technology support for critical thinking, for the main reason that most systems have adopted the modern Socratic Method. This discussion now addresses findings associated with this.

For the review of technology-enhanced learning systems for critical thinking, the following inclusion criteria were defined:

1. Scientific articles describing a technology-enhanced learning system must mention “critical thinking” or “Socratic” (including “Socratic dialogue,” “Socratic method,” “Socratic questioning”), and “reasoning”;

2. A system must have educational purposes, e.g., learning, developing/enhancing skills;

3. A system must have been evaluated or technically validated.

In addition to the inclusion criteria, one exclusion criterion is that assessment systems are not considered, because they do not provide didactic/pedagogical strategies to enhance critical thinking skills.

Applying these three inclusion criteria and the exclusion criterion, articles were collected from Google Scholar, DBLP and open access journal databases on the Internet, 14 learning systems for critical thinking were included ( Table 2 ). In the following, each system is briefly summarized and assigned to one of the critical thinking approaches. If the authors of the system claimed that it supports the Socratic method but did not show the systematic Socratic method, we will assign that system to the category “claimed to be Socratic.” If a system is still available online, it is indicated by an Internet URL on a column of the table.

The review starts with the learning systems that adopt the modern Socratic method. The common feature is that the systems control the dialogue, ask questions and the students answer the system's questions in free text. One of the earliest computer systems that adopted the modern Socratic method is SCHOLAR ( Carbonell, 1970 ). In this system, the author modeled the domain geography using a semantic network. The system allows mixed initiative dialogues, i.e., both students and the system can initiate questions. The user interface allows the users to input an answer or a question in free form. The system understands the student's question or answer by matching a pattern with pre-specified keywords. In order to generate texts, the system fills answer and question templates with information from the semantic net. Since the semantic network represents only fact knowledge rather than procedural knowledge, the system is limited to categorize student utterances beyond simply right/wrong. Also adopting the modern Socratic method, Weusijana et al. (2004) developed a questioning strategy for the system SASK. It is a domain-independent architecture for deepening students' reflections on well-defined tasks using Socratic dialogues. In the domain of biomedical engineering, for example, the system adopts the questions used by experts for students such as “ What are you trying to do here? ” or “ What variables are you controlling? ” Person and Graesser (2002) developed an intelligent tutoring system that applies the modern Socratic method to improve students' knowledge in the areas of computer literacy and Newtonian physics using an animated agent that is able to ask a series of deep reasoning questions according to the question taxonomy proposed by Graesser and Person (1994) .

Beside the learning systems that applied the modern Socratic method, several learning systems adopted the neo-Socratic group discussion method. Le and Huse (2016) developed a conversational agent that plays the role of a moderator for a group discussion. The conversational agent leads the discussion participants through the phases of the neo-Socratic group discussion method and encourages participants to strengthen their critical thinking in order to develop arguments for the given discussion topic. The evaluation study of the Socratic conversational agent ( Le and Huse, 2016 ) reported encouraging results that the Socratic group discussion moderated by a conversational agent has the tendency to activate participants' thinking and join the group discussion more actively. For similar purpose, Hoeksema (2004) developed a group discussion environment that is intended to serve virtual Socratic dialogues. The Socratic dialogues using this discussion environment are intended to be held similarly in a usual face-to-face environment. Whereas, this work focused on developing an environment for Socratic group discussions, the Socratic conversational agent of Le and Huse (2016) was used to formalize the neo-Socratic group discussion method to help students develop critical thinking.

While the classic and modern Socratic methods are based on the dialogues of Socrates documented in the books of Plato, the conceptualization of the Socratic method has been developed and modified in different guises.

Edelson (1996) developed a so-called Socratic case-based architecture Crimeanate using thought-provoking questions and cases. Two pedagogical principles underlying this architecture are active learning and learning from cases. These principles are implemented by two system components: a task environment and a storyteller. The learning domain supported by this architecture is biology. Specific subject matter is animal adaptation. A session begins with an invitation to the student to create his or her own animal by taking an existing animal and changing it in some way. Following the choice of an animal, the system engages the student in a series of natural language dialogues in which the student considers the ramifications of the proposed modification of his or her animal. The storyteller recognizes opportunities for learning during the course of interactions of the student with the task environment and presents cases that may help the student to learn from his/her own problem.

Glass (2001) developed CIRCSIM, a dialogue-based intelligent tutoring system that uses questions to lead conversations with student and claimed that the pedagogical strategy is Socratic tutoring. This tutoring strategy is based on a corpus of human tutoring dialogues that contains many instances of students' short answers ( Glass, 2001 ). The notion of Socratic tutoring suggested by Glass is as follows: “ The dialogue is under the tutor's control; the machine asks questions and the student answers with free text in imitation of the Socratic style of human tutoring .”

Similarly, Weusijana et al. (2004 , p. 561) characterized a Socratic tutoring method very informally: “ An educator may know of these issues and choose to tutor their learners socratically; to conversationally engage with learners, often while they work on their learning task, with pertinent and probing questions .” Based on this concept of the Socratic method, the authors developed a web-based system that helps students foster reflection.

Domeshek et al. (2002) conceptualized the Socratic method as follows: “ Socratic instruction is a kind of teaching interaction typically applied in high-level professional education (e.g., law and business) and most often characterized by its external form: the teacher asks a lot of questions, and the student answers.” Based on this notion of the Socratic method, Domeshek et al. (2004) developed ComMentor, an automated Socratic tutoring system, for command skills for high-level professional education such as law and business. This system is claimed to be able to guide the student in a Socratic mode as an expert would: the teacher asks questions and the student answers. The sequence of the questions is intended to help the student reconstruct the logic of expert situation analysis and decision-making. Domeshek et al. (2004) described four characteristics of a typical Socratic session: (1) a thought-provoking problem, (2) a student's attempt to provide solutions, (3) the instructor's repeated exploration and challenging of the student's solutions, and (4) incremental justification, elaboration, refinement, and revision of both the student's understanding of the situation under discussion and their proposed solution.

According to the notions for the Socratic method above that are not based on the analysis of Socrates' dialogues, a teacher should engage students by posing questions. It is controversial whether these notions for the Socratic method can be categorized as the modern Socratic method because the modern Socratic method also applies a sequence of questions for that the Socratic questioner anticipates correct answers. However, since the computer applications that adopt these notions for the Socratic method are based on the analysis of human tutoring dialogues, it is questionable whether these dialogues follow a systematic methodology and whether the methodology of human tutors is really effective.

Several educational applications support tutorial dialogues. Olney et al. (2012) presented a method for generating questions for tutorial dialogue. This involves automatically extracting concept maps from textbooks in the domain of biology. Five question categories were deployed: hint, prompt, forced choice question, contextual verification question, and causal chain questions. Also, with the intention of supporting students using conversational dialogues, Lane and VanLehn (2005) developed PROPL, a tutor, which helps students build a natural-language style pseudo-code solution to a given problem. All these educational applications deployed some kinds of dialogue, however, they neither apply the classic nor modern Socratic method.

There have been several computer-supported learning systems for human reasoning which could be considered a part of the critical thinking process since critical thinking involves the use of inductive reasoning ( Carey and Mullan, 2004 ). For example, the framework of critical thinking proposed by Paul and Elder (2006) includes the class of questions that probe reason and evidence. Le and Wartschinski (2018) proposed a cognitive assistant that holds conversation with students to develop human reasoning skills. This study, with more than 60 test persons, showed significant improvement in reasoning skills. Pursuing the similar aim, an existing serious game, Argotario ( Habernal et al., 2017 ) addressed argumentation and critical thinking skills by identifying fallacies in arguments and intentionally developing fallacious arguments during the process of playing a game. Both the cognitive assistant developed by Le and Wartschinski (2018) and the serious game Argotario proposed a conversational agent as the communication interface with the user. However, the difference between these systems lies in the training tasks. The cognitive assistant developed by Le and Wartschinski (2018) covered several issues that lead to irrational thoughts and decisions: (1) sunk cost fallacy, (2) gambler's fallacy, (3) Bayesian reasoning, (4) belief bias in syllogistic reasoning, (5) regression toward the mean, (6) co-variation detection, and (7) Wason's selection tasks. Training tasks provided by this cognitive assistant were based on psychology literature ( Larrick, 2004 ; Toplak et al., 2014 ). The serious game, Argotario, only addressed the single issue of “fallacy.”

From this review of technology-enhanced learning systems for critical thinking ( Table 3 ), we can learn three lessons. First, the number of developed learning systems for critical thinking is still low. Thus, given the proliferation of misinformation and ‘fake news' on the web, further research in this area is arguably urgent. Second, most of the developed learning systems (e.g., Olney et al., 2012 ) claimed that they support Socratic approaches (e.g., Socratic tutoring), which are based on human tutoring strategies rather than Socrates' strategies. It is controversial whether the human tutoring strategies are pedagogically effective and whether they need to be empirically validated before being integrated into a learning system. Third, the classic Socratic method has not been applied in any reviewed learning system. This absence of the classic Socratic method in learning systems can be explained by which the steps of the classic Socratic method might be very challenging to be mapped to a computational model. Especially the third step, which is the core of the classic Socratic method, would require a computer system to be able to ask a question to test a hypothesis by posing a fact check, a counter argument, counter example, a fallacy check, or a check for contradiction.

Table 3 . A summary of computer-supported educational systems for critical thinking.

Conclusions

This paper has reviewed 14 existing technology-enhanced learning systems for critical thinking. The review shows that almost all existing systems adopted the notion of the modern Socratic method, e.g., the system uses questions to lead the learner to acquire knowledge in small steps and knowledge that is to be acquired can be anticipated by the system. Thus, questions and anticipated knowledge of a learning domain can be modeled computationally. Whereas, the modern Socratic method has been adopted in many systems, the classic Socratic method is rarely deployed in computer-supported learning systems. Perhaps the reason is that steps of the classic Socratic method are challenging to be mapped to a computational model. Another finding is that several dialogue-based learning systems claimed to adopt Socratic questioning method, however, they only support conversation between users and the system in natural language. That is, those systems may enhance critical thinking through questions, but a systematic Socratic approach cannot be identified.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

I acknowledge support by the German Research Foundation (DFG) and the Open Access Publication Fund of Humboldt-Universität zu Berlin.

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Keywords: critical thinking, classic Socratic method, modern Socratic method, Socratic group discussion, critical thinking

Citation: Le N-T (2019) How Do Technology-Enhanced Learning Tools Support Critical Thinking? Front. Educ. 4:126. doi: 10.3389/feduc.2019.00126

Received: 07 May 2019; Accepted: 15 October 2019; Published: 06 November 2019.

Reviewed by:

Copyright © 2019 Le. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Nguyen-Thinh Le, nguyen-thinh.le@hu-berlin.de

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Taking critical thinking, creativity and grit online

Miguel nussbaum.

1 School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

Camila Barahona

Fernanda rodriguez, victoria guentulle, felipe lopez, enrique vazquez-uscanga, veronica cabezas.

2 School of Education, Pontificia Universidad Católica de Chile, Santiago, Chile

Technology has the potential to facilitate the development of higher-order thinking skills in learning. There has been a rush towards online learning by education systems during COVID-19; this can therefore be seen as an opportunity to develop students’ higher-order thinking skills. In this short report we show how critical thinking and creativity can be developed in an online context, as well as highlighting the importance of grit. We also suggest the importance of heuristic evaluation in the design of online systems to support twenty-first century learning.

Introduction

This paper is in response to the article “Designing for 21st century learning online: a heuristic method to enable educator learning support roles” (Nacu et al. 2018 ). In this paper, the authors outline a framework for heuristic evaluation when designing online experiences to support twenty-first century learning.

Twenty-first century skills can be key to success in a modern knowledge society. Among these skills, critical thinking is important not only at work, where problem solving is essential, but also in any social setting where adequate decision making is required (Dwyer and Walsh 2020 ). Additionally, creativity helps ensure that the outcomes of critical thinking can be both culturally ingenious as well as treasured (Yeh et al. 2019b ). This is achieved by embracing cognitive abilities in order to create new combinations of ideas (Davis 1969 ).

Technology has been shown to facilitate the development of higher-order thinking skills in learning (Engerman et al. 2018 ). However, in general, schools have failed to take advantage of this by incorporating adequate use of technology into their practices (Olszewski and Crompton 2020 ). Therefore, the rush towards online learning by education systems during COVID-19 can also be seen as an opportunity to develop students’ higher-order thinking skills. One potential drawback with online learning is the distance it creates between peers, thus hindering student engagement and the development of higher-order thinking skills (Dwyer and Walsh 2020 ). We show how this barrier can be overcome when developing critical thinking and creativity in an online context.

Critical thinking

Critical thinking includes the ability to identify the main elements and assumptions of an argument and the relationships between them, as well as drawing conclusions based on the information that is available, evaluating evidence, and self-correcting, among others. It is seen as a self-regulated process that comes from developing skills such as interpretation, analysis, evaluation and explanation; going beyond technical skills. It can therefore be considered a metacognitive process (Saxton et al. 2012 ; Facione 1990 ).

By taking learning online, both self-study and teacher-led sessions can be enhanced through a problem-based learning strategy. In the first stage, students build on a question or topic posed by the teacher, e.g. a mathematical problem or an essay writing assignment. In the second stage, students peer-review their classmates’ responses or essays using a rubric provided by the teacher. Students break down their classmates’ responses and see how they relate to the objective of the activity. They then compare this analysis with the rubric in order to provide feedback. In a third stage, the students develop a new response based on their initial response, the experience of giving feedback, and the feedback they received. This process develops self-evaluation as the students compare their own response with their classmates’ and discover any gaps in their knowledge. It can also develop metacognition as they integrate various sources of knowledge (initial response, feedback received and the experience of giving feedback) when developing a new response. In the final stage, the teacher discusses the different responses with the class. The teacher then compares the students’ work with the expected response and provides a general summary, transferring the responses to different domains.

While Stages 1 through 3 are asynchronous and computer-aided, stage 4 can be synchronous and supported by the use of a web-based video conferencing tool. Active student participation and teacher mediation are both key since interactive and instant feedback has been shown to improve critical thinking (Chang et al. 2020 ).

In addition to the problem-based strategy presented here, other active learning strategies can also be used to develop critical thinking, e.g. structured questioning, role playing, and cooperative learning (Cruz and Dominguez 2020 ). How these might be implemented online is still open to discussion, though heuristic evaluations may be a good alternative given the possibilities presented by online learning as a resource provider, learning broker and learning promoter (Nacu et al. 2018 ).

Creativity is an essential element of the problem-solving process. Creative people often find ways of addressing a problem that others cannot see, while also having the ability to overcome barriers where others may otherwise give up (Kaufman 2016 ). There are different techniques for developing creativity. In-depth learning is facilitated when students represent concepts based on their own personal perceptions (Liu et al. 2018 ). In this sense, analogy can be a powerful tool for boosting creativity. Analogical transfer includes the idea of making analogies by analyzing objects, ideas or concepts across domains, i.e. information is transferred from the known (the original domain) to the unknown (the new domain) by searching for similarities (Shen and Lai 2014 ).

We propose an analogical transfer strategy. In the first stage, the teacher identifies a concept with examples from different domains. This might include showing a video that not only introduces the concept but also provides a context that is both familiar and relatable for the students. In the second stage, students reflect on situations from their own lives where they can apply the concept that is being studied. Here, the use of open-ended questions allows the students’ creativity to be explored in greater depth, while adapting to their different backgrounds and levels of prior knowledge. In the third stage, which is mediated by the teacher, the students discuss their responses from stage 2. The teacher should focus on original responses from different domains, or responses where it is not clear whether the solution is correct.

Stages 1 and 2 can be conducted asynchronously and scaffolded using technology through the inclusion of multimedia and student guides. However, stage 3 should be synchronous and supported by the use of a web-based video conferencing tool. In this way, technology facilitates the development of creativity by facilitating the discovery process, the collection of ideas, and the integration of knowledge (Yang et al. 2018 ). Mediation in stage 3 is therefore key (Giacumo and Savenye 2020 ). Effective teacher-student dialogue can improve the teacher-student relationship and enhance the creative process. Heuristic evaluation can therefore help us understand this relationship by looking at these interactions on the online platform (Nacu et al. 2018 ).

As with any learning process, critical thinking and creativity require students to be both present and focused, which in turn requires grit (Yeh et al. 2019a ). In other words, the way in which students approach their schooling is just as important as what and how we teach them (Tissenbaum 2020 ). Grit should therefore not only be considered an essential element of academic achievement but also as a mental process that activates and/or directs people’s behavior and actions (Datu et al. 2018 , Lan and Moscardino 2019 ). This is particularly relevant in a COVID-19 context, where the pandemic is affecting the wellbeing and mental health of many students, families & communities (OECD 2020 ).

In order to achieve effective student engagement, the objective must be attainable, interesting and accessible (i.e. in their zone of proximal development). The means used to complete the task must be attractive and feel more like a reward than an assignment. Finally, the teacher should work on the students’ persistence, not just in order to complete the task but as an essential quality for everyday life (Barnes 2019 ).

Teacher grit may also be key. As Haderer ( 2020 ) suggests “Why do some teachers stay when others run from the challenges?” In this sense, reflection has been shown to be relevant for teacher efficacy and grit (Haderer 2020 ). Heuristic evaluation methods may therefore allow the educator to understand the learning system as a whole (Nacu et al. 2018 ).

Ending remarks

As indicated in (Nacu et al. 2018 ) we are “faced with the need to create youth-centered spaces that also provide adult facilitation of learning”. Heuristic evaluation can therefore help connect online platforms with students, teachers and twenty-first century skills needs.

Acknowledgements

The research results informed in this report were supported by ANID/FONDECYT 1180024.

Biographies

is full professor for Computer Science at the School of Engineering of the Universidad Católica de Chile. He was member of the board of the Chilean Agency for the Quality of Education in Chile, and is Co-editor of Computers & Education.

is a teacher who is doing a PhD at the School of Engineering of the Universidad Católica de Chile.

is an engineer who is doing a PhD at the School of Engineering of the Universidad Católica de Chile.

is an Assistant Professor at the School of Education, Pontificia Universidad Católica de Chile, and associate researcher at Millennium Nucleus of Social Development. She is co-founder of Teach for all in Chile (Enseña Chile), and an NGO in Chile to foster high school students to choose the education career (Elige Educar).

Compliance with ethical standards

The different research projects underlying this report received approval from the University’s ethics committee. The participation was voluntary and the students signed an informed consent form.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Miguel Nussbaum, Email: lc.cup.gni@nm .

Camila Barahona, Email: lc.cu@oharabec .

Fernanda Rodriguez, Email: lc.cu@3irdorfm .

Victoria Guentulle, Email: lc.cu@utneugav .

Felipe Lopez, Email: lc.cu@1zepolif .

Enrique Vazquez-Uscanga, Email: lc.cu@zeuqzavae .

Veronica Cabezas, Email: [email protected] .

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Does Technology Help Boost Students’ Critical Thinking Skills?

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Does using technology in school actually help improve students’ thinking skills? Or hurt them?

That’s the question the Reboot Foundation, a nonprofit, asked in a new report examining the impact of technology usage. The foundation analyzed international tests, like the Programme for International Student Assessment or PISA, which compares student outcomes in different nations, and the National Assessment of Educational Progress or NAEP, which is given only in the U.S. and considered the “Nation’s Report Card.”

The Reboot Foundation was started—and funded—by Helen Bouygues , whose background is in business, to explore the role of technology in developing critical thinking skills. It was inspired by Bouygues’ own concerns about her daughter’s education.

The report’s findings: When it comes to the PISA, there’s little evidence that technology use has a positive impact on student scores, and some evidence that it could actually drag it down. As for the NAEP? The results varied widely, depending on the grade level, test, and type of technology used. For instance, students who used computers to do research for reading projects tended to score higher on the reading portion of the NAEP. But there wasn’t a lot of positive impact from using a computer for spelling or grammar practice.

And 4th-graders who used tablets in all or almost all of their classes scored 14 points lower on the reading exam than those who reported never using tablets. That’s the equivalent of a year’s worth of learning, according to the report.

However, 4th-graders students who reported using laptops or desktop computers “in some classes” outscored students who said they “never” used these devices in class by 13 points. That’s also the equivalent of a year’s worth of learning. And 4th-grade students who said they used laptops or desktop computers in “more than half” or “all” classes scored 10 points higher than students who said they never used those devices in class.

Spending too much time on computers wasn’t helpful.

“There were ceiling effects of technology, and moderate use of technology appeared to have the best association with testing outcomes,” the report said. “This occurred across a number of grades, subjects, and reported computer activities.”

In fact, there’s a negative correlation between time spent on the computer during the school day and NAEP score on the 4th-grade reading NAEP.

That trend was somewhat present, although less clearly, on the 8th-grade reading NAEP.

“Overall usage of technology is probably not just not great, but actually can lower scores and testing for basic education [subjects like math, reading, science],” said Bouygues. “Even in the middle school, heavy use of technology does lower scores, but if you do have things that are specifically catered to a specific subject, that actually serves a purpose.”

For instance, she said her daughter, a chess enthusiast, has gotten help from digital sources in mastering the game. But asking kids to spend a chunk of every day typing on Microsoft Word, as some classrooms do in France, isn’t going to help teach higher-order thinking skills.

She cautioned though, that the report stops short of making a casual claim and saying that sitting in front of a laptop harms students’ ability to be critical thinkers. The researchers didn’t have the kind of evidence needed to be able to make that leap.

For more research on the impact of technology on student outcomes, take a look at these stories:

• Technology in Education: An Overview
• Computers + Collaboration = Student Learning, According to New Meta-Analysis
• Technology Has No Impact on Teaching and Learning (opinion)

Image: Getty

A version of this news article first appeared in the Digital Education blog.

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• Published: 21 April 2023

Augmented reality technology in enhancing learning retention and critical thinking according to STEAM program

• Yaser A. Alkhabra 1 ,
• Usama M. Ibrahem   ORCID: orcid.org/0000-0001-6911-6149 1 , 2 &
• Saleh A. Alkhabra 1  

Humanities and Social Sciences Communications volume  10 , Article number:  174 ( 2023 ) Cite this article

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• Science, technology and society

According to the science, technology, engineering, arts, and mathematics (STEAM) program, this experimental research aims to advocate e-content based on augmented reality (AR) technology to enhance retention learning (LR) and reinforce critical thinking in the intermediate stage in Ha’il, KSA. Then, we study the interaction between the technology of AR design (image/mark) and the mental capacity of learners (high/low) in developing critical thinking (CT) and practical skills, i.e., the interaction between AR (image/mark) and gender. The study’s sample consisted of 120 8th-grade junior high school students from six schools in Ha’il. 63 of the 120 participants are females, while 57 are males. They were divided into 2 control and 8 experimental groups. Our analysis revealed that students’ LR and CT skills after using AR were better than before using AR. The first result we found was that implementing AR in educational realms impacted students’ LR. Furthermore, statistically significant differences were exhibited in overall CT skills between those with high and low mental capacity (MC), favoring those with high MC. Even more interestingly, according to the STEAM program, male students’ outcomes in science learning were more reinforced by AR than females’. Future research could quantify learning outcomes and look at underserved communities. Moreover, future studies could reveal the educational benefits of augmented reality-based active learning.

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Introduction.

The information age we live in helps us in applying knowledge in novel ways because it is built on digital technologies. As a result, the digital generations appeared due to the change of learning styles from audio, visual, and kinesthetic (VAK) ones to E-VAK, since we have got computers with huge capacity and multitasking, the integration of mobile and computers into network-based interface devices, and the emergence of endless numbers of interactive application programs used by the new generations (Ibrahem and Alamro, 2021 ). Because technology is changing so quickly, educational materials must be updated and modified constantly.

Consequently, educators were interested in employing contemporary and virtual technologies to assist learning environments, such as AR, a recent technology that creatively and engagingly supports learning environments by fusing reality with digital interactions (Jesionkowska et al., 2020 ). Also, it helps in boosting both practical skills and intellectual concepts. Hence, there is a propensity to combine different technologies to uphold learning goals and maximize their outcomes through the orientation of the learner-centered learning strategy, with examples including visual reality (VR), smart learning (AR), and Internet of things (IoT) (Siam, 2020 ). AR allows real and virtual objects to interact with one another, which is the only technology that connects physical reality to virtual data regarding that reality. It delivers a direct or indirect picture of the physical world in real-time that is heightened by the addition and overlay of virtual data (Fokides and Mastrokoukou, 2018 ). Additionally, AR technology can be used directly or indirectly in the teaching and learning environment to assist and sustain learners in dealing with knowledge and interacting with it (visually and auditory) in an easier way to represent, store, and test knowledge (Sun et al., 2018 ).

It is clear that augmented reality technology has the potential to create a knowledge-building environment similar to that described in constructivist theory, which requires the creation of realistic situations and an active learning environment through interaction with learning software (Muhammad et al., 2021 ). The use of educational AR is also dependent on modern communication theory principles such as self-learning and the learner’s ability to obtain knowledge and respond when exposed to stimuli on networks, devices, or electronic tools such as portable smart devices (Radu et al., 2023 ). Education systems are rapidly modified by immersive technologies. Among them, AR has resourcefully shown promise, particularly for STEAM education (Ibáñez and Delgado-Kloos, 2018 ). The philosophy of STEAM integrated education is to provide creative education, covering cutting-edge technology, to students who are already accustomed to advanced technology, so that they do not lose interest in learning in case of their inability of keeping up with the pace of technology. The future of society’s advancement depends on STEAM competencies, which are also anticipated to be the cornerstone of some of the fastest-growing businesses (Jesionkowska et al., 2020 ).

What is more, STEAM is defined as a broad field that includes numerous disciplines and epistemological practices (Siam, 2020 ). Many requirements must be met in order to activate STEAM education, encompassing the development of integration between different disciplines via the problem-solving method, active learning via learning situations and planned and extracurricular projects, and the creation of technology-rich learning environments to benefit and excite learners while preparing them for the future practical market (Ibáñez and Delgado-Kloos, 2018 ). Lower enrollments in STEM courses, which are critical for future economic growth, reflect young people’s growing disengagement from STEM (Success Through STEM, 2011 ). Past studies defend using modern technology to facilitate the learning processes while anchoring this education and training in STEM pedagogy through an interdisciplinary approach (Kul and Berber, 2022 ; Sahin and Yilmaz, 2020 ). New technologies have emerged in recent decades, allowing a more thorough exploration of appropriate technology to support STEM learning. Immersive technology, such as augmented reality (AR), has gained popularity in recent years, with an increasing number of studies published in educational contexts (Sirakaya and Sirakaya, 2022 ).

CT, as a multidimensional cognitive construct, implies both inductive and deductive reasoning, as well as creative processes that interact at various stages of problem-solving. Many studies have proved that learning and mastering CT skills during educational situations accomplishes a variety of objectives, including the learner’s active role in the educational process and improving the learner’s ability for self-learning (Techakosit and Nilsook, 2016 ; Ezz Al-Arab and H. Saad, 2016 ).

Given the importance of CT and the necessity of developing critical and investigative thinking skills, CT skills comprise many subskills (Al-Zahrani, 2017 ). As a result, investigation and research curricula must be designed in such a way that the learner is directly exposed to life experiences and practices of the various and integrated science processes of data collection and interpretation, teamwork, problem-solving, and decision-making (Ibrahem and Alamro, 2021 ).

The problem

Analysis of STEAM test results reports for Arab countries revealed relative progress, but not the intended transformation (Saudi Ministry of Education, 2019 ). During their visit to some middle schools in Ha’il Governorate, the team also noticed that teachers faced a problem of second-intermediate students’ inability of having correct knowledge of concepts and facts related to some science topics; for instance, the textbook did not contain pictures or videos that benefit learning topics.

In addition, previous research found that there was no opportunity for IoT experimentation, because the curricula were already overburdened and schools generally lacked developmental resources (Nersesian et al., 2019 ). The scarcity of educational applications with sufficient learning content worsens this problem (Jesionkowska et al., 2020 ). The imbalance between the high quantity of multimedia experiences centered on entertainment versus education has been identified as one of the main causes contributing to the fall in STEM education performance (Pellas et al., 2020 ). On the contrary, a study by Techakosit and Nilsook ( 2016 ), manifested that employing AR technology aided in the development of laboratory skills. According to Hamada’s research ( 2018 ), AR applications and the interaction between learning styles in the augmented reality environment and cognitive style have a positive impact on developing achievement, which is also confirmed in Syawaludin and Rintayati’s research ( 2019 ) Judah ( 2018 ) also emphasized the effectiveness of exploiting augmented reality for problem-solving and emotional intelligence development. Also, the successful integration of educational technology into the curriculum content of the public school system may be a viable solution for engaging students in STEM education (Pellas et al., 2020 ; Fokides and Mastrokoukou, 2018 ). The studies’ findings also stressed the importance of utilizing AR in the teaching of technology, science, and math in order to improve student-learning outcomes, foster a variety of thought processes, and revitalize the educational system (Al-Hajri, 2018 ; Hamada, 2018 ).

As previously mentioned, the research problem is that second-grade students need more computer technology (CT) skills; therefore, they do not do well in school and on STEAM tests in science classes. Given that the STEAM program’s goals for developing science are to teach students how to use computers and do well in science by the end of the second intermediate grade, the research team plans to do this by using augmented reality apps. The main research questions directed at the current article are as follows:

How do the interactions of augmented reality design technology (image/mark), mental capacity (high/low), and gender (male/female) affect developing learning retention?

How does the interaction between augmented reality design technology (image/mark), mental capacity (high/low), and gender (male/female) influence the development of CT skills?

What is the effectiveness of augmented reality technology in enhancing science learning outcomes according to the STEAM program in the second grade at the intermediate stage?

In this study, we discuss the connection between AR technology and its advantages when adopted in STEM education for advancing learning, as well as recommending new research opportunities that are ripe for investigation.

Literature review

What is ar.

AR is defined as the technology that combines virtual and actual environments through the use of specialized software and programming to display them on smart devices (Çetin and Türkan, 2022 ; Syawaludin et al., 2019 ). This allows for exposing digital content like images, videos, and forms with stereoscopic images and other types of multimedia, raising student or teacher interaction and promoting deeper and more effective learning (Petrov and Atanasova, 2020 ; Demircioglu et al., 2022 ). AR requires no special equipment. Most teenagers today have a smartphone with a camera, so they can use augmented reality immediately. Recent studies on bibliometrics, meta-analysis, and systematic literature reviews have discussed the growing popularity of both researching and applying AR in educational settings, as well as the educational benefits and drawbacks of this technology (Mariscal et al., 2020 ).

That is to say, three characteristics are required for AR systems to be considered: (1) real and virtual elements mixing, (2) real-time interaction, and (3) three-dimensional registration (Petrov and Atanasova, 2020 ; Kul and Berber, 2022 ; Kalemkuş and Kalemkuş, 2022 ). Likewise, text, video, images, audio, infographics, and 2D/3D models can all be loaded via AR technology (Tekedere and Göke, 2016 ), enabling users to interact with virtual objects embedded in real-world scenes to gain practical life experience with human-computer interaction (Ajit, 2021 ; Radu et al., 2023 ).

AR is divided into two types: location-based AR and vision-based AR. First, location-based AR enables visitors to use GPS-enabled smart devices to track the distance between two locations. Thus, data from the GPS, gyroscope, compass, camera, and other sensors can be combined with location data to convey knowledge about the physical environment (Godwin-Jones, 2016 ; Demircioglu et al., 2022 ). Second, vision-based augmented reality focuses on image recognition techniques adopted to locate actual objects in their natural surroundings, so that virtual contexts associated with these objects can be appropriately placed. Its tracking system is classified as either marker-based or monocular (Demircioglu et al., 2022 ). Marker-based tracking requires specific labels, such as QR codes, to register the 3D images, unlike markerless tracking; hence, any part of the real environment can be utilized to trigger the virtual images. Labels, QR codes, and virtual images are examples of “triggers” or “markers,” which can be placed at any time and in any location. As the AR application controls the camera to recognize markers, the device screen can display 3D graphics or other types of actions (Godwin-Jones, 2016 ; Meletiou-Mavrotheris, 2019 ).

This change has accelerated the growth of STEAM education, an integrated method of teaching students across a variety of areas. Science, technology, engineering, arts, and mathematics are collectively referred to as STEAM. It was founded on STEM principles, a transdisciplinary approach that treated the sciences as a whole rather than as distinct branches of science, technology, engineering, and mathematics (Meletiou-Mavrotheris, 2019 ). In order to encourage learning in more linked and comprehensive ways, the original STEM framework recently included the arts. An integrated STEM and arts curriculum, as claimed by supporters of the STEAM movement, is necessary to promote genuine creativity and invention by enabling learners to apply abilities to think systematically which blends the ideas of scientists, technologists, and artists or designers (Kim, 2012 ).

AR in STEAM Education

By utilizing cutting-edge technology, the benefits of AR technology are applied to STEAM education to increase interest and involvement in science, since it has the potential for pedagogical applications to maintain learning and teaching (Ibáñez and Delgado-Kloos, 2018 ). Furthermore, AR is extremely beneficial for activity-centered STEAM education because it can fortify learner-centered activities and assist learners in booming scientific knowledge and understanding concepts (Kim and Kim, 2018 ). The key advantages of AR are the possibility for learning gain, increased motivation, and collaboration. When implemented properly, augmented reality can help students become more motivated, collaborate more, develop their spatial awareness, and perform better (Ajit, 2021 ; Kalemkuş and Kalemkuş, 2022 ). Students can discover the environment interactively and collaboratively, thanks to AR technology in education (Syawaludin and Rintayati, 2019 ).

Fully interactive virtual laboratory simulations in STEAM education are specifically made to pique and inspire a student’s inherent curiosity in learning (Nersesian et al., 2019 ). As a result of the virtual element’s enhancement of the learning experience and intensification, students better memorize procedural knowledge (Ajit, 2021 ; Kul and Berber, 2022 ). AR can also strengthen students’ higher-order thinking skills by offering deeper learning rather than surface information (Omurtak and Zeybek, 2022 ). The relationship between learners, instructors, and the environment is strengthened via AR.

Challenges in implementing AR in STEM education

Implementing AR in STEM education is challenging because of the usability of AR devices. It necessitates a more involved setup, incorporating moving furniture to provide room for students to roam around, placing markers in strategic places, and occasionally checking the lighting (Kul and Berber, 2022 ). Other challenges entail functioning AR applications with technological issues. Available AR apps have technical and/or pedagogical constraints, such as limited scaffolding of the learning process, inadequate model quality, low simulation accuracy, and insufficient haptic feedback (Sahin and Yilmaz, 2020 ; Meletiou-Mavrotheris, 2019 ).

Certainly, some unfavorable incidents occurred, for example, the video resuming when the relevant objects or mobile devices moved, the device’s internet connection, the camera resolution of the employed mobile devices, and the classroom’s lighting and audio (Çetin and Türkan, 2022 ). In addition, many free AR authoring tools are not available that are simple to use, and instructors lack the use of AR instructional content. By inspiring students to take an active role in their learning, AR can significantly improve levels of comprehension, memory, and knowledge transfer (Fidan and Tuncel, 2019 ; Kul and Berber, 2022 ). Implementation issues could appear due to the institutional limits imposed by the curriculum’s specific deadline.

AR and STEAM to improve critical thinking skills

Real-life problems are frequently intricate and disorderly; thus, a well-rounded education and higher-order skills are required. As a result, thinking development becomes one of the most important points that educational institutions strive to achieve among students, which is appropriately and consciously reflected in their interaction with life and its circumstances (Syawaludin and Rintayati, 2019 ; Demircioglu et al., 2022 ). In line with one of STEAM’s core values, the inclusion of AR into instruction encourages learners’ problem-solving and CT abilities. CT skills embrace the capacity for argument analysis, inductive/deductive inference, assessment/evaluation, and problem-solving or decision-making (Suryanti et al., 2020 ; Anggraeni, 2021 ).

On the other side, the goal of STEAM is to excite and encourage students regarding higher-order thinking, encompassing problem-solving, collaborative techniques, learning on one’s own, via projects and through challenges, and CT (Varenina et al., 2021 ; Suryanti et al., 2020 ; Anggraeni, 2021 ). Based on the results of some studies (Twiningsih and Elisanti, 2021 ; Varenina et al., 2021 ; Wechsler et al., 2018 ), it can be concluded that STEAM could improve student’s critical thinking skills and develop 21st-century skills.

The literature agrees that CT is a complex process that requires high-order reasoning to accomplish the desired outcome (Wechsler et al., 2018 ; Winarti et al., 2021 ). What is more, CT requires a variety of abilities, i.e, evaluating the validity of the information obtained, analyzing its dependability, and coming up with adequate answers for certain tasks or situations. Therefore, the development of CT skills in students is influenced by a number of factors, such as learning preferences, the way of applying concepts, conceptual understanding, and problem-solving skills (Varenina et al., 2021 ). Consequently, it can be said that CT abilities were deep-thinking skills that may challenge and refute any form of narrow gaps from any issue at hand in order to generate novel, accurate insights (Winarti et al., 2021 ; Twiningsih and Elisanti, 2021 ).

Why mental capacity

Typically, mental capacity is characterized by a person’s ability to make independent decisions at the moment when a decision is required (Osadchyi et al., 2021 ). The following factors determine maximum capacity: employment period reduction (transition from rest to a high level of capacity); the highest indicators of system performance (reaction rate, signal processing, etc.); the lowest bioenergy costs; preservation of working capacity over time (increased endurance); adequate body responses to external actions; and the simplest adaptation, regulation, and automation of skills (Keene et al., 2019 ; Osadchyi et al., 2021 ). Capability can be impacted by exhaustion from prolonged information labor, one’s emotional and physical status, and environmental circumstances (Keene et al., 2019 ).

Scientists’ empirical research advocates the usage of AR technology as a particular information environment that caters to students’ prevailing thinking types. Researchers show that augmented reality can be used in kinesthetic learning or “Learning by Doing” to build up personality cognitive structure, mental capacity, and cognitive motivation (Unsworth and Robison, 2020 ).

AR and learning retention

LR means a person’s capacity to retain new information in their long-term memory for later retrieval and application (Chang et al., 2015 ). It is essential to maintain obtained information at the appropriate time. Consequently, the capacity of a learning institution to retain the information of its students becomes a crucial component. Attention, self-efficacy, relevance, satisfaction, mnemonics, testing, and rewards are some of the variables that influence student retention. Therefore, for long-term memory, students must devote undivided attention during the learning process (Gargrish et al., 2022a , b ).

AR advanced student learning. AR-taught students exhibited higher test scores and retention rates. Research pinpoints that exam scores alone do not prove a student’s comprehension of the subject (Chang et al., 2015 ; Gargrish et al., 2022a ). Short-term memory is the most important concept elevated by AR, while long-term memory is still uncertain (Chang et al., 2015 ; Gargrish et al., 2022b ). Thus, the foregoing research demonstrates that AR can lead to deep knowledge if the system satisfies the user’s emotional and cognitive demands to properly motivate them.

Research design

The quantitative research method is the research design, which is exemplified in the quasi-experimental pretest/posttest control group. Studies using this design are those in which groups that are matched based on particular data are randomly assigned as experimental and control groups. In the experimental group, AR applications were utilized to teach the lesson; however, it was taught using conventional means in the control group. The decision to utilize a quasi-experimental design was made.

Participants

A total of 120 middle school second-grade students who attend classes in the city center of Ha’il make up the study participants for 2021–2022. 63 of them are females (F), while 57 of them are males (M), as depicted in Table 1 . All were identified as private school students, who had a smartphone and were willing to participate in the study.

The Juan Pascual Leone cross-shape scale based on “constructive triggers” was chosen from mental capacity investigations. The test has 36 items, composed of simple geometric shapes, one of which is on the right and has distinct shapes; in contrast, the other is on the left and has the same shapes but arranged in an overlapping pattern. High and low scores reflect mental capacity. The research sample was divided into high mental capacity (20 degrees or more out of 36) and low mental capacity (<20 degrees) students (Al-Banna and Al-Banna, 1990 ).

The researchers presented the study’s objectives, methodology, and guiding ethical principles. It was made clear that the study’s preliminary and detailed conclusions would be shared with teachers and principals. In addition, the science teachers were unfamiliar with augmented reality technologies, lacking practical expertise in them. Before the study, to help students become used to the researcher’s presence in the classroom, the researcher observed and sat in on the teacher’s classes.

Techniques and instruments for data collection

Tests were implemented in data collection research. Descriptive tests are designed to assess elementary school students’ knowledge of earth structure and rock material. The test’s validity using Aiken’s V validity with a 5% error rate is 0.78. The validity of the test instruments results indicates that there were 45 valid questions and five invalid questions. Cronbach’s alpha value for test reliability was 0.88. Referring to the value of r at a 95% confidence level or significance level of 5% ( p  = 0.05) with 0.88 > 0.4428, it is indicated that the test instrument was reliable.

The Cornell Arabized Saudi Environment Scale (Al-Zahrani, 2017 ; Ezz Al-Arab and H. Saad ( 2016 ); Al-Hajri, 2018 ) was carried out to measure CT skills. The internal consistency of the scale items was confirmed by calculating the correlation coefficient between the scores of each item, the total score of the scale, and the total score of the dimension using the Point Bay Serial correlation coefficient. The values of the correlation coefficients were measured. Most of the items and the total degree of the dimension were statistically significant at the significance level of 01.0.

Additionally, the stability of the scale was also certified by using the reliability coefficient of the internal homogeneity method (Cronbach’s alpha), all of which are higher than the value 70.0 (the minimum acceptable limit for the stability coefficient). The internal homogeneity stability coefficient of Cronbach’s alpha for the CT scale was 92.0 and ranged in dimensions from 75.0 to 88.0, which describes the scale’s stability.

Experimental design

As shown in Table 1 , the quasi-experimental design with dimensional measurement of the research groups was followed in teaching the units of study, to progress some CT skills and science learning outcomes for a sample of second-grade middle school students in Ha’il.

There were eight different experimental groups, due to two classification variables, each with two levels: gender (male and female) and mental capacity (high and low). Table 1 explains the differences between experimental groups.

We executed the HP Reveal software application of augmented reality (1) that relies on the presence of image or shape tags recognized by creating an educational object. Augmented reality technology is a clearer visual representation of complex information. Thus, the HP Reveal application is downloaded on smartphones or tablet computers, as it is a modern information technology to merge virtues.

A convenient lesson plan and an application brochure have been composed. The application brochure that has been constituted contained the application cards for the AR application called “light waves,” along with the videos and motion infographics with light attributes. In this manner, while studying the theoretical data about the characteristics of light waves, refraction of light, properties of prisms, laws of reflection, and uses of mirrors, students can observe the movement of light rays, the refraction of rays when moving from one medium to another of different density, and the movement of light rays inside the devices. On the other hand, AR ready-made science cards were provided and distributed to students to use independently.

During the application, students used AR science cards for 120 min and the face-to-face instruction accounted for 240 min on average. Furthermore, because the activity booklets were produced separately from this period, the students could independently use them at home. It also roughly took three weeks for this time frame.

To answer the first question: What is the effect of the interaction between AR design (image/mark), MC (high/low), and gender (M/F) on growing LR?

Manova test was used to test the differences according to AR design, MC, and gender. The results are displayed in Table 2 .

A statistical effect is found on LR across the interaction of MC, AR design, and gender. The LSD test is computed to conduct the differences; the results are revealed in Table 3 .

Statistically significant differences are discovered between the study groups’ levels in LR. The G1 (group 1) had high effects in LR, followed by G5 and G6. The approximated effects achieved in LR were in G2 and G3, while the lower ones were in G8.

Because of the combination of different alternatives such as images, audio, and video in the applications, the researchers interpret these results. The events and phenomena can give three-dimensional concreteness to abstract concepts and occurrences, adding fun to the lesson; hence, the students will be satisfied with utilizing these applications. Furthermore, students in AR learning sessions become attentive, which aids in absorbing the course content. The audio blending and animations incorporated into the 3D models in AR applications boost the students’ observation and realistic impression acquisition. With an open mind and an eagerness to learn, students participate in AR learning activities. When exploited in a student-centered way, AR raises students’ long-term knowledge retention by innovating more realistic and interesting learning environments and promoting problem-based learning (Fidan and Tuncel, 2019 ; Radu et al., 2023 ). Students can visualize and comprehend concretized abstract concepts, thanks to the interactive digital material of AR, which deepens learning and heightens performance. In addition, using AR to conquer the difficulties unique to science teaching dramatically stimulates students’ desire and interest in learning, motivating them to learn more actively and comprehensively (Kul and Berber, 2022 ).

Similar findings in various literature (Kalemkuş and Kalemkuş, 2022 ; Omurtak and Zeybek, 2022 ; Çetin and Türkan, 2022 ) stated that the usage of AR applications increased students’ participation in the classroom, providing a fun learning environment. According to Godwin-Jones ( 2016 ), students’ motivation for the materials used in lessons supported by AR applications at the secondary school level is high, positively affecting their success. Also, our findings are consistent with what has been demonstrated in other studies (Fidan et al., 2021 ; Fidan and Tuncel, 2019 ; Papanastasiou et al., 2019 ); i.e., incorporating AR into class activities have been confirmed to advance student-learning performance, contribute to students’ long-term retention of concepts, and assist students in understanding and analyzing problem scenarios in a greater depth.

Similarly, Ibáñez and Delgado-Kloos ( 2018 ) revealed that students had a better understanding of electromagnetism due to AR applications. Ozdemir et al. ( 2018 ) and Fidan and Tuncel ( 2019 ) concluded that it alleviated the grasping of complex and abstract concepts. Mariscal et al. ( 2020 ) and Avila-Garzon et al. ( 2021 ) discovered that AR learning techniques captured students’ attention in the learning process. They demonstrated that AR-based learning methods were more effective for students’ learning skills and knowledge acquisition.

To answer the 2nd question: What is the effectiveness of the AR design (image/mark), MC (high/low), and gender (M/F) on elevating CT skills?

Manova test was executed to test the differences in CT skills according to AR design, MC, and gender. The results are illustrated in Table 4 .

A statistical effect was spotted on CT skills according to MC; in contrast, no statistical effect was obtained on CT skills across gender, AR design, and the interaction impacts of independent variable levels. The influence of MC levels on overall CT skills was computed by the LSD test and the results are exposed in Table 5 .

Neither the type of AR used (image/mark) nor the gender (male/female) affected the development of critical CT, as evidenced in the study’s findings. This may be related to the success of the program based on both types of AR, the amplified interest of the learners in the program, and their positive engagement with it.

From Table 5 , statistically significant differences in overall CT skills between those with high and low MC in favor of those with high MC are highlighted.

An aspect of CT can be evident when AR is involved. AR can aid students in finding facts or information that will enable them to comprehend the subject matter and its structure thoroughly, setting up their capacity for critical thinking (Sirakaya and Sirakaya, 2022 ; Chang and Hwang, 2018 ). Furthermore, AR media can stimulate students’ CT skills by letting them visualize abstract concepts. The opportunities for personalized education, high enthusiasm and passion for the subject, and flexible work schedules are a few elements that impact students’ motivation to learn science. Furthermore, the events and phenomena in the applications can make abstract concepts and events concrete in three dimensions by combining different options such as pictures, audio, and video, with making the lesson enjoyable. Therefore, these applications have gained students’ pleasure.

The outcomes can be explained in terms of the constructivist theory of learning and the usage of AR since it allows students to direct their learning and interact with virtual items in a larger context to gain understanding. Making use of a variety of media during the learning process can spark students’ interest and boost their CT skills. Utilizing various multimedia formats can boom one’s capacity for CT.

The earlier findings are in line with facts highlighted by Muhammad et al. ( 2021 ) and Chang and Hwang ( 2018 ): using AR can encourage students to CT and students’ learning skills because they can visualize abstract topics. Additionally, AR facilitates students’ understanding of spatial relationships, which enlarges their capacity to solve spatial problems and supports technical activities connected to object construction (Deshpande and Kim, 2018 ). This means that AR projects are ideally suited for putting into practice the thinking methodology, a cross-disciplinary and unconventional technique of problem-solving that is user-centered (Meletiou-Mavrotheris, 2019 ). These findings corroborate the findings of previous studies in which AR technologies have a positive effect on students’ attitudes (Ozdemir et al., 2018 ; Fidan and Tuncel, 2019 ) and improve their CT skills (Syawaludin and Rintayati, 2019 ). On the contrary, some findings contradict the previous research claiming that female students are more involved than males (Muhammad et al., 2021 ; Radu et al., 2023 ). Students of both genders are tremendously interested and enthusiastic about learning when using AR-based learning media. This finding backs up preceding research (Papanastasiou et al., 2019 ), which discovered no changes in perceptions of the use of AR in learning activities on the impact of AR media on student creativity.

To answer the 3rd question: How does AR technology influence enhancing science learning outcomes according to the STEAM program in the second grade at the intermediate stage?

Manova test was performed to test the differences in learning outcomes. The results are demonstrated in Table 6 .

A statistical effect on learning outcomes according to MC was displayed. No statistical effects on learning outcomes across gender, AR design, and learning outcomes across the interaction effects of independent variable levels were delineated. The effects of gender levels on overall learning outcomes were computed by the LSD test; the results are presented in Table 7 .

From Table 7 , statistically significant differences across gender in learning outcomes in favor of males are exhibited, because AR displays spatial relationships by fusing 3D virtual items with the actual world, allowing users to engage in real-time while viewing a real-world environment that has been improved with 3D images. AR applications are tools that offer students detailed and meaningful information as well as enriched representations.

Our research project findings concur with those of other studies (Çetin and Türkan, 2022 ; Demircioglu et al., 2022 ; Papanastasiou et al., 2019 ) in that adopting AR technology improves students’ views of learning motivation and STEAM skills. This has taken place because the curiosity and enjoyment of the students were aroused and challenged. Moreover, it has been discovered that AR-based applications were a useful way to concretely represent abstract concepts. By combining AR tools with appropriate pedagogical practices, it is feasible to grant students once-in-a-lifetime experiences that can boost their passion and knowledge of STEAM subjects while simultaneously rooting for the development of critical 21st-century skills.

Similarly, researchers investigated the outcomes of AR-integrated learning strategies like collaborative learning, argumentation in science learning, socioscientific reasoning, student-centered hands-on learning activities, and problem-based learning (Demircioglu et al., 2022 ; Chen et al., 2016 ; Fidan and Tuncel, 2019 ; Godwin-Jones, 2016 ). They have applied AR technology to training and education, and their findings suggest that AR technology can allow students to take part in authentic learning activities and explore real environments (Chang and Hwang, 2018 ; Syawaludin and Rintayati, 2019 ). Likewise, Sahin and Yilmaz ( 2020 ) reported that, by the use of three-dimensional models, AR-based applications provided students with appropriate environments to simplify their understanding of course topics, letting them directly experience these concepts rather than visualizing them. As documented in various research studies, AR-based applications are an essential tool in learning and teaching processes, increasing academic success and curiosity, and giving abstract ideas a concrete form (Akçayır et al., 2016 ; Muhammad et al., 2021 ; Papanastasiou et al., 2019 ).

Conclusions

The integration of AR and STEM activates complex problem-solving and fosters collaboration. AR upgrades engagement, motivation, and participation during STEAM education. Students were able to observe details linked to the digitized object by using either mobile devices or computers to visualize real objects or locations with augmented reality. It is vital to have a better understanding of the AR technology foundations for STEM and organized student-centered learning, to coin learning activities that allow students to absorb fundamental STEM conceptual and procedural knowledge.

Conjointly, this result recommends a switch from lecture-based instruction to AR-based active learning. Learning can be a powerful instrument for teaching students both technical and artistic talents, as well as a variety of complementary 21st-century skills. Future curricula must incorporate the AR-based active learning approach, which may make students gain STEAM skill learning via analysis and understanding of concepts rather than memorization. Students’ participation in AR activities promoted the transdisciplinary learning style that STEAM programs desired to fulfill; students must make use of knowledge and abilities that they have acquired throughout STEAM fields, in order to effectively complete the activities. The ideas of constructionist and sociocultural learning theories serve as the foundation for this educational system.

Limitations

The study presented here is limited to a small group. To obtain further evidence on the educational merits of AR, a large sample size with controlled and thorough evaluation studies is needed. In the second preparatory grade, second limits were restricted to the science book. Another limitation of the study is that it was carried out in a private school. As a result, the outcomes are correct for these students. In addition, the intervention period of this study was short. Finally, we assume that all types of learners can take advantage of augmented reality. The use of augmented reality in education for children who are underprivileged or at risk, those with special educational needs, or those from low-income households has not been the subject of many studies. These groups must be considered in upcoming research.

Suggestions

The paradigm of education is changing to focus on developing human resources with the capacity for creative problem-solving in order to fulfill the demands of the contemporary digital era, following the trend of such dynamic technological innovations. This will need additional research to determine whether the novelty of AR will continue to have a significant impact on the outcomes of longer-term investigations. By taking into account multiple themes in science or other courses, similar investigations that last for a longer period can also be executed. Additionally, it may be suggested to use methods such as meta-analysis in future studies.

Thorough strategic planning, thoughtful execution, and a research-based foundation are required for the widespread and successful integration of AR technology within STEAM education. This should emphasize a detailed plan and ongoing involvement of all significant educational attendees (students, parents, teacher educators, other college faculties, adult educators, educational leaders, technical managers, and administrators).

Definitely, the new generation of students is technologically perceptive, with a strong interest in social media and mobile technologies. School systems must begin planning for the deployment of various types of classroom technologies in order to provide students with a higher-quality education that will have a long-term impact.

Future studies could examine learning outcomes and investigate marginalized communities. Nonetheless, many scientific queries are still unsolved. We believe that more research is needed to address practical issues such as the associated cost structure, technical specifications for equipment, or best practices for embedding innovative technology as a standard component in the curriculum. We plan to conduct additional research to evaluate the educational benefits of augmented reality active learning and outline how to generate effective AR activities in the curriculum to expand students’ critical thinking. This could entail incorporating the presented format on a larger scale into specific curriculum practices, thereby investigating its inclusion into regulated teaching.

Data availability

The raw data supporting the conclusion of this article will be available upon request to the corresponding author.

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The researchers’ team acknowledges the Scientific Research Deanship, University of Ha’il, Saudi Arabia, for fund project number (BA-2203).

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Alkhabra, Y.A., Ibrahem, U.M. & Alkhabra, S.A. Augmented reality technology in enhancing learning retention and critical thinking according to STEAM program. Humanit Soc Sci Commun 10 , 174 (2023). https://doi.org/10.1057/s41599-023-01650-w

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Thinking Through the Ethics of New Tech…Before There’s a Problem

• Beena Ammanath

Historically, it’s been a matter of trial and error. There’s a better way.

There’s a familiar pattern when a new technology is introduced: It grows rapidly, comes to permeate our lives, and only then does society begin to see and address the problems it creates. But is it possible to head off possible problems? While companies can’t predict the future, they can adopt a sound framework that will help them prepare for and respond to unexpected impacts. First, when rolling out new tech, it’s vital to pause and brainstorm potential risks, consider negative outcomes, and imagine unintended consequences. Second, it can also be clarifying to ask, early on, who would be accountable if an organization has to answer for the unintended or negative consequences of its new technology, whether that’s testifying to Congress, appearing in court, or answering questions from the media. Third, appoint a chief technology ethics officer.

We all want the technology in our lives to fulfill its promise — to delight us more than it scares us, to help much more than it harms. We also know that every new technology needs to earn our trust. Too often the pattern goes like this: A technology is introduced, grows rapidly, comes to permeate our lives, and only then does society begin to see and address any problems it might create.

• BA Beena Ammanath is the Executive Director of the global Deloitte AI Institute, author of the book “Trustworthy AI,” founder of the non-profit Humans For AI, and also leads Trustworthy and Ethical Tech for Deloitte. She is an award-winning senior executive with extensive global experience in AI and digital transformation, spanning across e-commerce, finance, marketing, telecom, retail, software products, services and industrial domains with companies such as HPE, GE, Thomson Reuters, British Telecom, Bank of America, and e*trade.

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Media Alert: Adobe Express for Education Fuels Strong Growth in Next-Gen Creativity and Job Skills Reaching Tens of Millions of Students and Teachers Globally

• Adobe continues commitment to provide Adobe Express, an all-in-one AI creativity app and other industry-leading creative tools, to students and teachers across K-12 and higher education
• Growing number of Adobe Creative Campuses and new partnerships with NBCU Academy, MagicSchool, India’s Ministry of Education and other institutions extend Adobe Express to millions more students and teachers globally
• Adobe empowers student expression and critical thinking skills with responsible generative AI designed to be safe for the classroom

SAN JOSE, Calif. — June 25, 2024 — This week, at the International Society for Technology in Education (ISTE) ISTELive24 annual conference, Adobe (Nasdaq:ADBE) announced strong growth in the number of K-12 and higher education students and teachers worldwide who have access to the company’s industry-leading creative solutions, driven by exponential expansion of Adobe Express for Education across more campuses as well as new partnerships.

At the event, Adobe was also named to this year’s EdTech Top 40 list. The prestigious list is part of an annual report by LearnPlatform that features the most commonly accesses ed-tech tools for K-12 in the United States.

Adobe Express for Education is the all-in-one AI creativity app that makes creative skill building easy. It is designed to be classroom safe with responsible generative AI features that are collaborative, easy and improve student engagement and impact communication skills. With Adobe Express for Education, students and teachers can easily design presentations, reports, resumes, videos, PDFs, animations, websites, posters and flyers. 

“Given the high expectations and commitments students face, we’re incredibly excited to see so much momentum and growth for Adobe Express among tens of millions of students and teachers around the world,” said Mala Sharma, VP and general manager, creators and education for Adobe’s Digital Media Business. “We look forward to introducing new innovations in Adobe Express that will provide our global teacher and student community with even more easy and fun ways learn, create and collaborate with responsible AI.”

Equipping Students with Next-Gen Work Skills 

Adobe has a long-standing partnership with higher education institutions around the world to help ensure that every student is equipped with the skills employers seek in today’s workplace, including creative problem solving, visual communication, collaboration, creativity, and the responsible use of AI. Today, nearly 6 million higher education students globally can access Adobe creative apps through their campuses.

In the United States, institutions including Penn State, a world-class public research university that spans 25 campuses throughout Pennsylvania and four campuses within the California State University (CSU) system, the largest and most diverse four-year public university system in the country, now have access to Adobe creative tools. More than half of CSU students come from traditionally underrepresented backgrounds, and nearly one-third of undergraduates are the first in their families to attend college.

The CSU schools are committed to delivering quality and equitable education for all, which includes closing the digital divide to give its 130,000 yearly graduates the technical and digital skills required to succeed in modern workplaces.“People worldwide equate the Adobe brand with quality, creativity and innovation,” said Cynthia Teniente-Matson, President of SJSU. “By bringing Adobe Creative Cloud apps like Adobe Express into classrooms, we’ve reimagined how we serve historically underrepresented student populations. They now have easier and quicker access to tools to learn skills that set them up for future success.”

Both Penn State and CSUs are Adobe Creative Campus partners, a growing community of higher education institutions across North America, Europe, Asia and Japan committed to boosting student outcomes and career success through equitable access to Adobe Creative Cloud and Adobe Express. 

Kent State University in Ohio, Sheridan College in Canada, Marshall University in West Virginia, Northern Arizona University, Sheridan College in Canada and the State University of New York (SUNY) College of Environmental Science & Forestry (ESF) in New York are all new Adobe Creative Campuses. “This is a terrific benefit to ESF’s students, who now have additional access to creative tools and the opportunity to boost their digital skills. Our Adobe Creative Campus status is the perfect complement to ESF’s,” said SUNY ESF President Joanie Mahoney. “As our world becomes increasingly digital, we are excited to extend these new tools to all students so they can learn to sharpen their skills and stand out after graduation.” 

Adobe has also seen significant year-over-year growth in Asia, including the All India Council of Technical Education (AICTE) that will bring Adobe Express to more than 10,000 technical institutions across India and a new Adobe Creative Campus in New Zealand and Japan. Torrens University , Think Education and Media Design School is a leading post-secondary education network offering undergraduate, graduate and technical vocational credentials across Australia and New Zealand and The Ritsumeikan Trust, which includes two universities in Japan. 

“Ritsumekan aims to become a next-generation research university, promoting the expanded recombination of research and education and the cultivation of innovation and emerging talent,” said Yoshio Nakatani, President. “As an Adobe Creative Campus, Ritsumeikan will strive to raise the level of creative skills throughout the university by enhancing hands-on training opportunities for students, faculty and staff, enhancing university-wide skill development opportunities using on-demand materials and demonstrating our commitment to the latest technologies, such as generative AI.”

Naomi Cocks, associate professor in the School of Allied Health at Curtin University in Australia, aims to help her students become capable problem-solvers whether they eventually seek jobs in the city or across the globe. Using Adobe Express, Cocks asks students to take a creative approach to synthesizing and sharing their learnings with peers and the broader community. “Adobe Express supports a teaching methodology that emphasizes active and playful learning,” she said. “Some typically quiet students really shine in this task because it’s a different way of tapping into their creativity.”

A Global K-12 Education Ecosystem

Adobe Express for Education is free for K-12 and empowers student expression, critical thinking, communication and collaboration skills for tens of millions of teachers and students globally. With Adobe Express, K-12 students can apply new technology and creativity skills to make presentations, infographics, GIFs, videos, animations, web pages and more with unique capabilities like Animate Characters, drawing and PDF editing. 

The New South Wales Department of Education in Australia is one example of an institution committed to enabling equitable, inclusive access to essential digital tools and provides Adobe Express and Adobe Creative Cloud applications to all K-12 students. According to the institution’s CFO Charlie Sukkar: “I have witnessed first-hand the positive impact of Adobe's products in our Schools. Especially in high schools.”

New K-12 partnerships with MagicSchool and NBC Universal News Group’s education initiative NBCU Academy are bringing Adobe’s creative technologies to even more K-12 students and teachers globally . MagicSchool describes their platform as the “award-winning, most used and most loved AI platform for schools in the world.” Educators use MagicSchool to help create lesson plans, differentiate, write assessments, write IEPs, communicate clearly and more. MagicSchoolis integrating Adobe’s Firefly-powered Text to Image features into the context of the MagicSchool experience, making Firefly the only generative AI feature on the platform and helping empower student expression and critical thinking skills with responsible generative AI that is designed to be safe for the classroom. Intuitive Adobe generative AI features like Text-to-Image, Text Effects, Generative Insert and Generative Remove are accompanied by Adobe guardrails on generative AI prompts and outputs, encouraging appropriate use. Adobe also gives districts control over whether generative AI features are turned on or off and does not include student projects in training datasets for generative AI.

“We're excited to bring Adobe Express' AI image generation capabilities to educators and students in the MagicSchool platform,” said Adeel Khan, CEO & Founder of MagicSchool.ai. “We've known that generating images with AI sparks curiosity and creativity in schools – but we wanted to put safety and responsibility first in launching it to our millions of users. Adobe is the perfect partner because they've built their tools responsibly from the ground up for the safety needs of schools in mind.”

In April, Adobe and NBCUniversal News Group’s education initiative NBCU Academy launched The Edit , a first-of-its-kind program in the United States aimed at helping students build key digital media and literacy skills Students use tutorials and guidance on how to script, record and publish news reports using Adobe Express. This week, Adobe and NBCU Academy announced the winners of the competition. Read more about it here . 

Adobe also announced a prestigious partnership with the Ministry of Education in India for K-12 and highereducation to bring Adobe Express into schools to help develop skills and enhance learning outcomes. India’s national education policy emphasizes the use of digital tools and AI to help build creativity skills for future readiness. Adobe is working with schools in India’s Central Board of Secondary Education as well as the Indian government’s Pradhan Mantri Schools for Rising India (PM SHRI) program to build digital creativity skills and upskill educators to support integrating Adobe Express into their curriculum. In addition, Adobe recently collaborated with India’s National Council of Education Research and Training (NCERT) to host Adobe Express and Adobe Acrobat content on their national platform.

• About Adobe

Adobe is changing the world through digital experiences. For more information, visit  www.adobe.com .

© 2024 Adobe. All rights reserved. Adobe and the Adobe logo are either registered trademarks or trademarks of Adobe in the United States and/or other countries. All other trademarks are the property of their respective owners.

Adobe Express for Education is the all-in-one AI creativity app that makes creative skill building easy. It is designed to be classroom safe with responsible generative AI features that are collaborative, easy and improve student engagement and impact communication skills. With Adobe Express for Education, students and teachers can easily design flyers, posters, resumes, presentations, reports, videos, animations, websites and PDFs.

“As we head toward back-to-school season we’re incredibly excited to see so much momentum and growth for Adobe Express among tens of millions of students and teachers around the world,” said Mala Sharma, VP and general manager, creators and education for Adobe’s Digital Media Business. “We look forward to introducing new innovations in Adobe Express that will provide our global teacher and student community with even more easy and fun ways learn, create and collaborate with responsible AI.”

Adobe Express for Education is free for K-12 and is empowering student expression critical thinking, communication and collaboration skills for tens of millions of teachers and students globally. With Adobe Express, K-12 students can apply new technology and creativity skills to make presentations, infographics, GIFs, videos, animations, web pages and more with unique capabilities like Animate Characters, drawing and PDF editing.

The New South Wales Department of Education in Australia is committed to enabling equitable, inclusive access to essential digital tools and provides Adobe Express and Adobe Creative Cloud applications to all K-12 students. According to the institution’s CFO Charlie Sukkar: “I have witnessed first-hand the positive impact of Adobe's products in our Schools. Especially in high schools.”

New K-12 partnerships with MagicSchool and NBC Universal News Group’s education initiative NBCU Academy are bringing Adobe’s creative technologies to even more K-12 students and teachers globally . MagicSchool is integrating Adobe’s Firefly-powered Text to Image features into the context of the MagicSchool experience, making Firefly the only generative AI feature on the platform. MagicSchool, describes their platform as the “award- winning, most used and most loved AI platform for schools in the world.” Educators use MagicSchool to help create lesson plans, differentiate, write assessments, write IEPs, communicate clearly and more. This partnership is part of Adobe ‘s commitment to empowering student expression and critical thinking skills with responsible generative AI that is designed to be safe for the classroom. Intuitive Adobe generative AI features like Text-to- Image, Text Effects, Generative Insert and Generative Remove empower students to express their creativity, think critically and encourage them to exercise voice and choice in their learning. Adobe guardrails on generative AI prompts and outputs encourage appropriate use and give districts control over whether generative AI features are turned on or off. Adobe does not include student projects in training datasets for generative AI.

In April, Adobe and NBCUniversal News Group’s education initiative NBCU Academy launched The Edit , a first-of- its-kind program in the United States aimed at helping students build key digital media and literacy skills Students use tutorials and guidance on how to script, record and publish news reports using Adobe Express. Monday, Adobe and NBCU Academy announced the winners of the competition. Read more about it here.

Adobe also announced a prestigious partnership with the Ministry of Education in India for K-12 and higher education to bring Adobe Express into schools to help develop skills and enhance learning outcomes. India’s national education policy emphasizes the use of digital tools and AI to help build creativity skills for future readiness. Adobe is working with schools in India’s Central Board of Secondary Education as well as the Indian government’s Pradhan Mantri Schools for Rising India (PM SHRI) program to build digital creativity skills and upskill educators to support integrating Adobe Express into their curriculum. In addition, Adobe recently collaborated with India’s National Council of Education Research and Training (NCERT) to host Adobe Express and Adobe Acrobat content on their national platform.

Equipping Students with Next-Gen Work Skills

Adobe has a long-standing partnership with higher education institutions around the world to help ensure that every student is equipped with the skills employers seek in today’s workplace: creative problem solving, visual communication, collaboration, creativity, and the responsible use of AI. Today, nearly 6 million higher education students globally can access Adobe creative apps through their campuses.

In the United States, institutions including Penn State, a world-class public research university that spans 25 campuses throughout Pennsylvania and four campuses within the California State University (CSU) system, the largest and most diverse four-year public university system in the country, now have access to Adobe creative tools. More than half of the students come from traditionally underrepresented backgrounds, and nearly one-third of undergraduates are the first in their families to attend college.

The CSU schools are committed to delivering quality and equitable education for all, which includes closing the digital divide to give its 130,000 yearly graduates the technical and digital skills required to succeed in modern workplaces. “People worldwide equate the Adobe brand with quality, creativity, and innovation,” said Cynthia Teniente-Matson, President of SJSU. “By bringing Adobe Creative Cloud apps like Adobe Express into classrooms, we’ve reimagined how we serve historically underrepresented student populations. They now have easier and quicker access to tools to learn skills that set them up for future success.”

Both Penn State and CSUs are Adobe Creative Campus partners, a growing community of higher education institutions across North America, Europe, Asia and Japan committed to boosting student outcomes and career success through equitable access to Adobe Creative Cloud and Adobe Express.

Kent State University in Ohio, Sheridan College in Canada, Marshall University in West Virginia, Northern Arizona University, Sheridan College in Canada and the State University of New York (SUNY) College of Environmental Science & Forestry (ESF) in New York are all new Adobe Creative Campuses. “This is a terrific benefit to ESF’s students, who now have additional access to creative tools and the opportunity to boost their digital skills. Our Adobe Creative Campus status is the perfect complement to ESF’s,“ said SUNY ESF President Joanie Mahoney. “As our world becomes increasingly digital, we are excited to extend these new tools to all students so they can learn to sharpen their skills and stand out after graduation.”

Adobe has also seen significant year-over-year growth in Asia, including the All India Council of Technical Education (AICTE) that will bring Adobe Express to more than 10,000 technical institutions across India and a new Adobe Creative Campus in New Zealand and Japan. Torrens University, Think Education and Media Design School, is a leading post-secondary education network offering undergraduate, graduate, and technical vocational credentials across Australia and New Zealand and The Ritsumeikan Trust, which includes two universities in Japan.

“Ritsumekan aims to become a next-generation research university, promoting the expanded recombination of research and education and the cultivation of innovation and emerging talent,” said Yoshio Nakatani, President. “As an Adobe Creative Campus, Ritsumeikan will strive to raise the level of creative skills throughout the university by enhancing hands-on training opportunities for students, faculty, and staff, enhancing university-wide skill development opportunities using on-demand materials, and demonstrating our commitment to the latest technologies, such as generative AI.”

Adobe is changing the world through digital experiences. For more information, visit www.adobe.com.

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Senior Software Engineer

• Information Technology
• Franklin, WI Corporate
• 27 Jun 2024

At Northwestern Mutual, we are strong, innovative and growing. We invest in our people. We care and make a positive difference.

We are looking for a Senior Software Engineer/Team Lead to join our team within Insurance Operations.

Our team is responsible for the systems that perform validation of information on client applications, creating new client records, identifying any missing client data, and preparing the application for Underwriting.

Primary Duties & Responsibilities:

Demonstrates technical leadership to our team.

Establishes, aggregates, and shares team standards and standard methodologies within the department.

Provides critical thinking, with an ownership attitude and continuous learning.

Represents the team in department-wide forums.

Understands the strengths/weaknesses of team members, identifies developmental needs

Assesses solutions to system-wide architectural problems.

Ability to perform peer reviews on code and design.

Proven understanding of design patterns and architecture.

Qualifications and Requirements:

4-5 years professional experience required.

4-5+ years of experience working with modern engineering tools, languages and practices.

4-5+ years of experience with Java

Expertise in backend development (e.g., PostgreSQL, Java Spring Boot, Kubernetes, Docker, Kafka).

Bachelor's Degree or equivalent experience

Proven critical thinking, ownership attitude and continuous learning.

Experience developing solutions using agile methods.

Mentor for the team and department.

Advanced abilities in one or more technical platforms.

Capable of presenting between product, engineering, and the business.

Manages own time to meet objectives.

Demonstrating capability and proficiency in the below skills:

Adaptive Communication

Analytical Thinking

Application Performance Management

Application Security

Software Development

Tuition reimbursement, commuter plans, and paid time off

Highly competitive compensation that include base salary plus bonus

Medical/Dental/Vision plans, 401(k), pension program

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Northwestern Mutual pays on a geographic-specific salary structure and placement in the salary range for this position will be determined by a number of factors including the skills, education, training, credentials and experience of the candidate; the scope, complexity as well as the cost of labor in the market; and other conditions of employment. At Northwestern Mutual, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. Please note that the salary range listed in the posting is the standard pay structure. Positions in certain locations (such as California) may provide an increase on the standard pay structure based on the location. Please click here for additiona l information relating to location-based pay structures.

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Why Are Vector Databases Critical For AI Strategy? Find Out At This Year’s Technology & Innovation Summit!

Noel Yuhanna , VP, Principal Analyst

Generative AI is revolutionizing data and analytics, but its applications demand advanced data management capabilities to handle vast, diverse, and complex datasets that include images, video, audio, documents, and text. Traditional databases were originally designed for structured data and exact matches, but they are proving insufficient for genAI models, which often operate in high-dimensional spaces and require searching for similarities.

Vector databases are advanced databases designed for optimized storage and retrieval of high-dimensional vector data. They excel in conducting large-scale similarity searches and streamlining data management for cutting-edge AI applications. Their key advantage lies in supporting specialized vector indexes, which enable fast query processing and deliver the high performance required for analyzing complex data.

At Forrester’s upcoming Technology & Innovation Summit North America , September 9–12, I will dig into the topic of vector databases. Data professionals will gain valuable insights into leveraging vector databases to elevate their AI strategy and implement industry best practices. This session will delve into the distinct advantages and practical applications of vector databases, highlighting their pivotal role for organizations dedicated to optimizing their AI strategy.

Here’s a preview of some of the topics that I’ll talk about in the session:

• Distinctive capabilities of vector databases. Unlike traditional databases, vector databases excel in efficiently storing and retrieving complex vector data that is generated by providers such as OpenAI, Hugging Face, and Cohere. By indexing vectors, the databases enable rapid execution of similarity searches. We will explore their distinct advantages over conventional databases.
• Choosing between native and multimodal vector databases. Native vector databases are purpose-built to efficiently manage complex, multidimensional vector data at scale. On the other hand, multimodal databases are now incorporating vector functionalities, including storage, indexing, and querying capabilities. In my presentation, we will analyze the strengths and limitations of both native vector databases and multimodal databases with vector support.
• Exploring diverse use cases for vector databases. As interest in models leveraging complex, high-dimensional data, particularly in generative AI applications, continues to surge, vector databases are gaining prominence with a multitude of emerging use cases. While retrieval-augmented generation (RAG) currently dominates, the landscape is poised to expand into non-RAG applications in the near future. We will explore diverse use case scenarios and unfold forthcoming developments in this evolving market.

Don’t miss out. I’ll be diving into the details at Technology & Innovation Summit North America , so check out the agenda and secure your spot!

Forrester clients can also register for the upcoming webinar, AI Unleashes A Data Renaissance , on July 25 to get a wider perspective on AI’s impact on data analysis. This webinar is part of our AI Advantage webinar series for clients.

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• Generative AI

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Stay tuned for updates from the Forrester blogs.

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Why do electronic components have a shelf life?

Electronic component manufacturing is a miracle of modern technology. It’s a fascinating and highly complex process to extract the raw materials from the earth, purify them, and then produce the finished products. Millions of man hours have been expended to manufacture materials that are low in cost, easy for the end user to assemble, and electrically plus mechanically stable. This is true for all components from the “simple” resistor all the way to the most complex multicore processor.

Despite our most valiant efforts, the fact remains that many electronic components have a finite shelf life. In this engineering brief we will explore a few of the constraints such as environmental conditions suggested in Figure 1. Here the tarnished trumpet mouthpieces are an extreme example of the unseen deterioration of the electronic components.

Figure 1 : A collection of darkened trumpet mouthpieces surrounded by resistors. Exposure to the elements has caused the two on the right to tarnish.

Tech Tip : Most solder and solder pastes contain an organic flux that removes the oxide layer. This increased the solder “wetting” allowing a solid reliable electrical and mechanical connection between the component and PCB. The power of the flux is limited in its ability to remove oxidation. Consequently, heavily oxidized, or tarnished components are to be avoided. This is a situation where the parts are perfect from an electrical perspective, but a troublesome from a manufacturing perspective.

Oxidation and solderability

For nearly all my life I have played the trumpet. Like me, these instruments are starting to show their age, as we see in Figure 1. The two mouthpieces on the right are deeply tarnished. While the one on the left is used but still in good shape. While I can’t give an exact reason, the bulk of the tarnish happened when I was living in Kodiak, Alaska. Perhaps it was the sea water or maybe it was because I lived a less than a thousand feet from an active airport taxiway. Either way, there was something in the air that attacked the metal.

The same corrosive elements are always acting on our electrical components. This is especially true for the component’s surfaces that will be soldered to the Printed Circuit Board (PCB). The shelf life of electronic components is dominated by the condition of these solderable surfaces. This statement is directly related to manufacturing. While your components may be electrically and mechanical stable for decades, tarnished surfaces can reduce your manufacturing yield, as old tarnished surfaces do not readily accept solder.

Tech Tip : Unsoldered components past the manufacturer recommended 2-year mark do not immediately expire like rotten produce. Since the components represent a sizable investment, you should conduct solderability tests to verify the integrity of the component’s finish.

Prolong shelf life

The integrity of a component’s solderable surfaces may be prolonged with proper storage. A typical storage specification may read like this:

50 to 90 degrees Fahrenheit or 10 to 32 degrees Celsius

25% to 50% relative humidity

no direct exposure to sunlight or other ultraviolet light

no exposure to corrosive elements in the air such as ozone or sulfur compounds

no exposure to radioactivity

As a rule, these conditions will result in a shelf life of two years for “simple” components such as resistors. We must consult the manufacturer’s data to determine the specific recommendation for more complex parts.

Tech Tip : There is nothing simple about a resistor especially when viewed from a materials stability perspective. This is especially true when we consider the physical stresses associated with high temperature and thermal cycling. At the same time, the resistor must retain its chemical, and electrical integrity for long service life.

Humidity drives corrosion as implied in the previous section. However, many parts are hygroscopic and will readily absorb water. This internal moisture can produce a destructive popcorning defect so named by the popping sound made by components. Here, water trapped inside the component flashes to steam during the soldering process. The result is a destruction of the mechanical integrity of the component with lost or compromised electrical functionality. This damage may be immediate or delayed as the compromised component quickly suffers environmental degradation.

Humidity sensitive components must be protected in sealed waterproof packaging. Desiccant should be included for long term humidity control. A humidity indicator card such as the SCS card shown in Figure 2 should be included in the package.

The parts should be kept sealed in the protective package until they are ready to be used. Once removed, they should be immediately soldered, ideally that day.

Tech Tip : Moisture sensitive components can be rendered unsolderable if left unprotected over a long weekend. Plan your production runs accordingly.

Restoration through baking

Many moisture sensitive components may be restored using a baking process. This process uses heat to drive the moisture out of the components. Consult the manufacturer before baking high dollar value components to ensure the moisture is fully removed in a controlled process. They should be able to provide appropriate time and temperature requirements. For example, they may provide a recommendation of 230 degrees Fahrenheit (110 Celsius) for 24 hours.

Tech Tip : The baking process is a balancing act. Elevated temperature accelerates the corrosion process as described in the previous section. This can be especially problematic for larger parts that require extended baking times. Repeated baking may render components unusable.

Figure 2 : A humidity indicator card showing the exposure level for the components while stored in their protective packaging. The card suggests baking the components if the central indicator turns pink.

Capacitors have their own unique shelf-life considerations. The most well know is the aluminum electrolytic. Under normal operating conditions, the critical oxide layer on the plate is preserved and maintained. However, the oxide layer degrades when the capacitor is unused. This reduces the capacitance, reduces the working voltage, and increases the leakage current. This can cause problems when voltage is forcefully applied after the capacitor is installed.

An electrolytic capacitor can often be restored to 100% operation status by applying a forming voltage. This may be done in two ways:

direct connect the capacitor to a DC power supply and slowly raise the voltage to the full working voltage of the capacitor.

connect the capacitor to the DC power supply via a series current limiting resistor.

In both cases, the capacitor voltage is slowly increased allowing time for the oxide layer to reform. Be sure to consult the manufacturer for reforming recommendations. Also, be cautious as high-capacity, high-voltage capacitors present a serious electrocution hazard. A safety enclosure with safe and effective discharge measures is a necessity.

This capacitor degradation is something that should be mitigated when designing electronic equipment. After all, it’s not unreasonable to have equipment or ready spares set on a shelf unpowered for many years. On the other hand, the vast majority of assembled electronic equipment can remain unpowered for years with no ill effects when powered up. While we cannot ignore this capacitor property, it may not be a significant issue if we capacitors with a reasonable safety margin. I will leave it to you to define the appropriate safety margin for your given application.

Tech Tip : There is an old repairman’s trick for reforming the capacitors in vacuum tube radios and stereo equipment that has been sitting for many decades. Rather than applying full line voltage, a variac is used to gradually apply the voltage. With slow application of voltage, many capacitors are restored to life. It’s not foolproof but it often works. The advantage of this technique is safety as the capacitors and the associated high voltage are confined within the equipment enclosure.

Long life is an important design consideration. As a rule, the shelf life considerations described in this article are not a significant issue once the components has been soldered onto the PCB. For example, while a manufacturer may specific a 1 year shelf life for a given component, we can expect that component to operate for a decade or longer once installed.

With that said, we recognize that there are many unique materials used in producing an electronic assembly. Be vigilant for shelf life and be sure to consult the device datasheet.

Parting thoughts

Manufacturing is a complex undertaking. Shelf life of your electrical components is one of many considerations for you process. Unlike produce, electronic components don’t have a firm expiration date. We could argue that the component manufacturers are being conservative. At the same time, we recognize that these components represent a sizable investment. Protect your investment by storing all components in controlled environments. Avoid storing open reals in a box on the floor of a damp warehouse.

This article is a brief introduction to a few common components. Please leave a comment below if you have questions about a class of components that were not addressed. Also, be sure to test your knowledge by answering the questions at the end of this article.

Best wishes,

Return to the Industrial Control and Automation Index

About this author

Aaron Dahlen, LCDR USCG (Ret.), serves as an application engineer at DigiKey. He has a unique electronics and automation foundation built over a 27-year military career as a technician and engineer which was further enhanced by 12 years of teaching (partially interwoven with military experience). With an MSEE degree from Minnesota State University, Mankato, Dahlen has taught in an ABET-accredited EE program, served as the program coordinator for an EET program, and taught component-level repair to military electronics technicians. Dahlen has returned to his Northern Minnesota home and thoroughly enjoys researching and writing educational articles about electronics and automation.

Highlighted Experience

Dahlen is an active contributor to the DigiKey TechForum. At the time of this writing, he has created over 150 unique posts and provided answers for an additional 500 customer posts. Dahlen shares his insights on a wide variety of topics including microcontrollers, FPGA programming in Verilog, and a large body of work on industrial controls.

Connect with Aaron Dahlen on LinkedIn .

The following questions will help reinforce the content of the article.

Describe the ideal environment for storing electronic components at your facility.

True / False: Corrosion of a component’s solderable surfaces is largely a manufacturing problem.

True / False: Shelf life is a consideration for the unsoldered PCB itself.

True / False: Popcorning is a problem for components years after they have been soldered to the PCB.

What is the purpose of solder flux?

Describe the process of reforming an electrolytic capacitor.

What is the downside of extended baking to remove moisture?

You have just removed a half used real of 0805 surface mount LEDs from your pick and place machine. Identify and describe the steps necessary to preserve the integrity of the components.

With regards to the previous question, how does your answer change if the real was inadvertently kept in place over the holiday break?

Research and then describe the shelf life limitations for a component not identified in this article.

Locate a humidity indicator card used in your facility. Describe the card and how to interpret the results.

What is thermal cycling and how does it impact a products life?

Critical thinking questions

These critical thinking questions expand the article’s content allowing you to develop a big picture understanding the material and its relationship to adjacent topics. They are often open ended, require research, and are best answered in essay form.

Shelf life may be extended if all components are treated as if they were moisture sensitive e.g., by storing them in sealed individual bags with desiccant. Is this a reasonable action when we consider the time and added expense?

How can component shelf life issues be mitigated by PCB design? Is this a desirable way forward?

Research and then contrast the vulnerability and impact of corrosion between surface mound and through-hole components.

Research and then compare the impact of corrosion on component shelf life for lead and RoHS components.

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U.S. Moves Ahead With Plan to Restrict Chinese Technology Investments

The Treasury Department unveiled rules to curb financing of Chinese semiconductors, quantum computers and artificial intelligence systems.

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By Alan Rappeport

Reporting from Washington

The Biden administration on Friday outlined its plans to curb new American investment in critical Chinese technology industries that could be used to enhance China’s military, further straining economic ties with Beijing at a time when trade tensions are rising.

The proposed Treasury Department rules would prohibit certain U.S. investments in Chinese companies that are developing semiconductors, quantum computers and artificial intelligence systems. The Biden administration is trying to restrict American financing from helping China develop advanced technology that could be used for weapons tracking, government intelligence and surveillance.

The regulations are expected to be finalized later this year. They come nearly a year after President Biden signed an executive order calling for the investment ban, which will largely affect venture capital and private equity firms that do business with Chinese companies.

“This proposed rule advances our national security by preventing the many benefits certain U.S. investments provide — beyond just capital — from supporting the development of sensitive technologies in countries that may use them to threaten our national security,” said Paul Rosen, the Treasury Department’s assistant secretary for investment security.

The restrictions require investors to notify the Treasury Department about certain kinds of transactions, and some types of investments are explicitly prohibited. As part of the program, the Treasury Department has the power to force a divestment and violations could be referred to the Justice Department for criminal prosecution.

The rules apply to equity investments, debt financing that could be converted to equity, and to joint ventures.

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