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The Journal of Artificial Intelligence Research (JAIR) is dedicated to the rapid dissemination of important research results to the global artificial intelligence (AI) community. The journal’s scope encompasses all areas of AI, including agents and multi-agent systems, automated reasoning, constraint processing and search, knowledge representation, machine learning, natural language, planning and scheduling, robotics and vision, and uncertainty in AI.

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Vol. 80 (2024)

Published: 2024-05-10

Individual Fairness, Base Rate Tracking and the Lipschitz Condition

Simulating counterfactuals, counting complexity for reasoning in abstract argumentation, robust average-reward reinforcement learning, using constraint propagation to bound linear programs, the toad system for totally ordered htn planning, methods for recovering conditional independence graphs: a survey, best of both worlds: agents with entitlements, computing unsatisfiable cores for ltlf specifications, general policies, subgoal structure, and planning width, mitigating value hallucination in dyna-style planning via multistep predecessor models, exploiting contextual target attributes for target sentiment classification, similarity-based adaptation for task-aware and task-free continual learning, scalable primal heuristics using graph neural networks for combinatorial optimization, on the trade-off between redundancy and cohesiveness in extractive summarization, understanding sample generation strategies for learning heuristic functions in classical planning, block domain knowledge-driven learning of chain graphs structure, expressing and exploiting subgoal structure in classical planning using sketches, effectiveness of tree-based ensembles for anomaly discovery: insights, batch and streaming active learning, experimental design of extractive question-answering systems: influence of error scores and answer length, estimating agent skill in continuous action domains, computing pareto-optimal and almost envy-free allocations of indivisible goods.

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Join the community, search results, facechain: a playground for human-centric artificial intelligence generated content.

1 code implementation • 28 Aug 2023

In this paper, we present FaceChain, a personalized portrait generation framework that combines a series of customized image-generation model and a rich set of face-related perceptual understanding models (\eg, face detection, deep face embedding extraction, and facial attribute recognition), to tackle aforementioned challenges and to generate truthful personalized portraits, with only a handful of portrait images as input.

Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI)

1 code implementation • 15 Feb 2022

A method for obtaining appropriate reaction coordinates is required to identify transition states distinguishing product and reactant in complex molecular systems.

Chemical Physics Soft Condensed Matter

Data-centric Artificial Intelligence: A Survey

10 code implementations • 17 Mar 2023

Artificial Intelligence (AI) is making a profound impact in almost every domain.

Sparks of Artificial General Intelligence: Early experiments with GPT-4

2 code implementations • 22 Mar 2023

We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models.

Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

2 code implementations • 12 Feb 2020

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline.

MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence

3 code implementations • 2 Dec 2017

Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents.

DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

1 code implementation • 6 Jan 2017

Poker is the quintessential game of imperfect information, and a longstanding challenge problem in artificial intelligence.

A System for Accessible Artificial Intelligence

2 code implementations • 1 May 2017

While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them.

Adversarial attacks and defenses in explainable artificial intelligence: A survey

1 code implementation • 6 Jun 2023

Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging and trusting statistical and deep learning models, as well as interpreting their predictions.

The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence

1 code implementation • 22 Sep 2021

We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from 2, 000 distinct simulated universes at several cosmic times.

THE AI INDEX REPORT

Measuring trends in AI

ai iNDEX anNUAL rEPORT

Welcome to the 2024 AI Index Report

Welcome to the seventh edition of the AI Index report. The 2024 Index is our most comprehensive to date and arrives at an important moment when AI’s influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its development. Featuring more original data than ever before, this edition introduces new estimates on AI training costs, detailed analyses of the responsible AI landscape, and an entirely new chapter dedicated to AI’s impact on science and medicine. The AI Index report tracks, collates, distills, and visualizes data related to artificial intelligence (AI). Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI.

TOP TAKEAWAYS

1. a i beats humans on some tasks, but not on all..

AI has surpassed human performance on several benchmarks, including some in image classification, visual reasoning, and English understanding. Yet it trails behind on more complex tasks like competition-level mathematics, visual commonsense reasoning and planning.

2. Industry continues to dominate frontier AI research .

 In 2023, industry produced 51 notable machine learning models, while academia contributed only 15. There were also 21 notable models resulting from industry-academia collaborations in 2023, a new high.

3. Frontier models get way more expensive .

According to AI Index estimates, the training costs of state-of-the-art AI models have reached unprecedented levels. For example, OpenAI’s GPT-4 used an estimated $78 million worth of compute to train, while Google’s Gemini Ultra cost $191 million for compute.

• 4. The United States leads China, the EU, and the U.K. as the leading source of top AI models.

 In 2023, 61 notable AI models originated from U.S.-based institutions, far outpacing the European Union’s 21 and China’s 15.

5. Robust and standardized evaluations for LLM responsibility are seriously lacking.

New research from the AI Index reveals a significant lack of standardization in responsible AI reporting. Leading developers, including OpenAI, Google, and Anthropic, primarily test their models against different responsible AI benchmarks. This practice complicates efforts to systematically compare the risks and limitations of top AI models.

6. Generative AI investment skyrockets.

Despite a decline in overall AI private investment last year, funding for generative AI surged, nearly octupling from 2022 to reach $25.2 billion. Major players in the generative AI space, including OpenAI, Anthropic, Hugging Face, and Inflection, reported substantial fundraising rounds.

7. The data is in: AI makes workers more productive and leads to higher quality work.

In 2023, several studies assessed AI’s impact on labor, suggesting that AI enables workers to complete tasks more quickly and to improve the quality of their output. These studies also demonstrated AI’s potential to bridge the skill gap between low- and high-skilled workers. Still other studies caution that using AI without proper oversight can lead to diminished performance.

8. Scientific progress accelerates even further, thanks to AI.

In 2022, AI began to advance scientific discovery. 2023, however, saw the launch of even more significant science-related AI applications—from AlphaDev, which makes algorithmic sorting more efficient, to GNoME, which facilitates the process of materials discovery.

9. The number of AI regulations in the United States sharply increases.

The number of AI-related regulations in the U.S. has risen significantly in the past year and over the last five years. In 2023, there were 25 AI-related regulations, up from just one in 2016. Last year alone, the total number of AI-related regulations grew by 56.3%.

10. People across the globe are more cognizant of AI’s potential impact—and more nervous.

A survey from Ipsos shows that, over the last year, the proportion of those who think AI will dramatically affect their lives in the next three to five years has increased from 60% to 66%. Moreover, 52% express nervousness toward AI products and services, marking a 13 percentage point rise from 2022. In America, Pew data suggests that 52% of Americans report feeling more concerned than excited about AI, rising from 38% in 2022.

Chapter 1: Research and Development

This chapter studies trends in AI research and development. It begins by examining trends in AI publications and patents, and then examines trends in notable AI systems and foundation models. It concludes by analyzing AI conference attendance and open-source AI software projects.

• 1. Industry continues to dominate frontier AI research.
• 2. More foundation models and more open foundation models.
• 3. Frontier models get way more expensive.
• 5. The number of AI patents skyrockets.
• 6. China dominates AI patents.
• 7. Open-source AI research explodes.
• 8. The number of AI publications continues to rise.

Chapter 2: Technical Performance

The technical performance section of this year’s AI Index offers a comprehensive overview of AI advancements in 2023. It starts with a high-level overview of AI technical performance, tracing its broad evolution over time. The chapter then examines the current state of a wide range of AI capabilities, including language processing, coding, computer vision (image and video analysis), reasoning, audio processing, autonomous agents, robotics, and reinforcement learning. It also shines a spotlight on notable AI research breakthroughs from the past year, exploring methods for improving LLMs through prompting, optimization, and fine-tuning, and wraps up with an exploration of AI systems’ environmental footprint.

• 1. AI beats humans on some tasks, but not on all.
• 2. Here comes multimodal AI.
• 3. Harder benchmarks emerge.
• 4. Better AI means better data which means … even better AI.
• 5. Human evaluation is in.
• 6. Thanks to LLMs, robots have become more flexible.
• 7. More technical research in agentic AI.
• 8. Closed LLMs significantly outperform open ones.

Chapter 3: Responsible AI

AI is increasingly woven into nearly every facet of our lives. This integration is occurring in sectors such as education, finance, and healthcare, where critical decisions are often based on algorithmic insights. This trend promises to bring many advantages; however, it also introduces potential risks. Consequently, in the past year, there has been a significant focus on the responsible development and deployment of AI systems. The AI community has also become more concerned with assessing the impact of AI systems and mitigating risks for those affected. This chapter explores key trends in responsible AI by examining metrics, research, and benchmarks in four key responsible AI areas: privacy and data governance, transparency and explainability, security and safety, and fairness. Given that 4 billion people are expected to vote globally in 2024, this chapter also features a special section on AI and elections and more broadly explores the potential impact of AI on political processes.

• 1. Robust and standardized evaluations for LLM responsibility are seriously lacking.
• 2. Political deepfakes are easy to generate and difficult to detect.
• 3. Researchers discover more complex vulnerabilities in LLMs.
• 4. Risks from AI are a concern for businesses across the globe.
• 5. LLMs can output copyrighted material.
• 6. AI developers score low on transparency, with consequences for research.
• 7. Extreme AI risks are difficult to analyze.
• 8. The number of AI incidents continues to rise.
• 9. ChatGPT is politically biased.

Chapter 4: Economy

The integration of AI into the economy raises many compelling questions. Some predict that AI will drive productivity improvements, but the extent of its impact remains uncertain. A major concern is the potential for massive labor displacement—to what degree will jobs be automated versus augmented by AI? Companies are already utilizing AI in various ways across industries, but some regions of the world are witnessing greater investment inflows into this transformative technology. Moreover, investor interest appears to be gravitating toward specific AI subfields like natural language processing and data management. This chapter examines AI-related economic trends using data from Lightcast, LinkedIn, Quid, McKinsey, Stack Overflow, and the International Federation of Robotics (IFR). It begins by analyzing AI-related occupations, covering labor demand, hiring trends, skill penetration, and talent availability. The chapter then explores corporate investment in AI, introducing a new section focused specifically on generative AI. It further examines corporate adoption of AI, assessing current usage and how developers adopt these technologies. Finally, it assesses AI’s current and projected economic impact and robot installations across various sectors.

• 1. Generative AI investment skyrockets.
• 2. Already a leader, the United States pulls even further ahead in AI private investment.
• 3. Fewer AI jobs, in the United States and across the globe.
• 4. AI decreases costs and increases revenues.
• 5. Total AI private investment declines again, while the number of newly funded AI companies increases.
• 6. AI organizational adoption ticks up.
• 7. China dominates industrial robotics.
• 8. Greater diversity in robot installations.
• 9. The data is in: AI makes workers more productive and leads to higher quality work.
• 10. Fortune 500 companies start talking a lot about AI, especially generative AI.

Chapter 5: Science and Medicine

This year’s AI Index introduces a new chapter on AI in science and medicine in recognition of AI’s growing role in scientific and medical discovery. It explores 2023’s standout AI-facilitated scientific achievements, including advanced weather forecasting systems like GraphCast and improved material discovery algorithms like GNoME. The chapter also examines medical AI system performance, important 2023 AI-driven medical innovations like SynthSR and ImmunoSEIRA, and trends in the approval of FDA AI-related medical devices.

• 1. Scientific progress accelerates even further, thanks to AI.
• 2. AI helps medicine take significant strides forward.
• 3. Highly knowledgeable medical AI has arrived.
• 4. The FDA approves more and more AI-related medical devices.

Chapter 6: Education

This chapter examines trends in AI and computer science (CS) education, focusing on who is learning, where they are learning, and how these trends have evolved over time. Amid growing concerns about AI’s impact on education, it also investigates the use of new AI tools like ChatGPT by teachers and students. The analysis begins with an overview of the state of postsecondary CS and AI education in the United States and Canada, based on the Computing Research Association’s annual Taulbee Survey. It then reviews data from Informatics Europe regarding CS education in Europe. This year introduces a new section with data from Studyportals on the global count of AI-related English-language study programs.  The chapter wraps up with insights into K–12 CS education in the United States from Code.org and findings from the Walton Foundation survey on ChatGPT’s use in schools.

• 1. The number of American and Canadian CS bachelor’s graduates continues to rise, new CS master’s graduates stay relatively flat, and PhD graduates modestly grow.
• 2. The migration of AI PhDs to industry continues at an accelerating pace.
• 3. Less transition of academic talent from industry to academia.
• 4. CS education in the United States and Canada becomes less international.
• 5. More American high school students take CS courses, but access problems remain.
• 6. AI-related degree programs are on the rise internationally.
• 7. The United Kingdom and Germany lead in European informatics, CS, CE, and IT graduate production.

Chapter 7: Policy and Governance

AI’s increasing capabilities have captured policymakers’ attention. Over the past year, several nations and political bodies, such as the United States and the European Union, have enacted significant AI-related policies. The proliferation of these policies reflect policymakers’ growing awareness of the need to regulate AI and improve their respective countries’ ability to capitalize on its transformative potential. This chapter begins examining global AI governance starting with a timeline of significant AI policymaking events in 2023. It then analyzes global and U.S. AI legislative efforts, studies AI legislative mentions, and explores how lawmakers across the globe perceive and discuss AI. Next, the chapter profiles national AI strategies and regulatory efforts in the United States and the European Union. Finally, it concludes with a study of public investment in AI within the United States.

• 1. The number of AI regulations in the United States sharply increases.
• 2. The United States and the European Union advance landmark AI policy action.
• 3. AI captures U.S. policymaker attention.
• 4. Policymakers across the globe cannot stop talking about AI.
• 5. More regulatory agencies turn their attention toward AI.

Chapter 8: Diversity

The demographics of AI developers often differ from those of users. For instance, a considerable number of prominent AI companies and the datasets utilized for model training originate from Western nations, thereby reflecting Western perspectives. The lack of diversity can perpetuate or even exacerbate societal inequalities and biases. This chapter delves into diversity trends in AI. The chapter begins by drawing on data from the Computing Research Association (CRA) to provide insights into the state of diversity in American and Canadian computer science (CS) departments. A notable addition to this year’s analysis is data sourced from Informatics Europe, which sheds light on diversity trends within European CS education. Next, the chapter examines participation rates at the Women in Machine Learning (WiML) workshop held annually at NeurIPS. Finally, the chapter analyzes data from Code.org, offering insights into the current state of diversity in secondary CS education across the United States.  The AI Index is dedicated to enhancing the coverage of data shared in this chapter. Demographic data regarding AI trends, particularly in areas such as sexual orientation, remains scarce. The AI Index urges other stakeholders in the AI domain to intensify their endeavors to track diversity trends associated with AI and hopes to comprehensively cover such trends in future reports.

• 1. U.S. and Canadian bachelor’s, master’s, and PhD CS students continue to grow more ethnically diverse.
• 2. Substantial gender gaps persist in European informatics, CS, CE, and IT graduates at all educational levels.
• 3. U.S. K–12 CS education is growing more diverse, reflecting changes in both gender and ethnic representation.

Chapter 9: Public Opinion

As AI becomes increasingly ubiquitous, it is important to understand how public perceptions regarding the technology evolve. Understanding this public opinion is vital in better anticipating AI’s societal impacts and how the integration of the technology may differ across countries and demographic groups. This chapter examines public opinion on AI through global, national, demographic, and ethnic perspectives. It draws upon several data sources: longitudinal survey data from Ipsos profiling global AI attitudes over time, survey data from the University of Toronto exploring public perception of ChatGPT, and data from Pew examining American attitudes regarding AI. The chapter concludes by analyzing mentions of significant AI models on Twitter, using data from Quid.

• 1. People across the globe are more cognizant of AI’s potential impact—and more nervous.
• 2. AI sentiment in Western nations continues to be low, but is slowly improving.
• 3. The public is pessimistic about AI’s economic impact.
• 4. Demographic differences emerge regarding AI optimism.
• 5. ChatGPT is widely known and widely used.

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Exploring the Impact of Artificial Intelligence in Teaching and Learning of Science: A Systematic Review of Empirical Research

• Open access
• Published: 27 June 2024

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• Firas Almasri 1 , 2 , 3 , 4  

The use of Artificial Intelligence (AI) in education is transforming various dimensions of the education system, such as instructional practices, assessment strategies, and administrative processes. It also plays an active role in the progression of science education. This systematic review attempts to render an inherent understanding of the evidence-based interaction between AI and science education. Specifically, this study offers a consolidated analysis of AI’s impact on students’ learning outcomes, contexts of its adoption, students’ and teachers’ perceptions about its use, and the challenges of its use within science education. The present study followed the PRISMA guidelines to review empirical papers published from 2014 to 2023. In total, 74 records met the eligibility for this systematic study. Previous research provides evidence of AI integration into a variety of fields in physical and natural sciences in many countries across the globe. The results revealed that AI-powered tools are integrated into science education to achieve various pedagogical benefits, including enhancing the learning environment, creating quizzes, assessing students’ work, and predicting their academic performance. The findings from this paper have implications for teachers, educational administrators, and policymakers.

Avoid common mistakes on your manuscript.

Introduction

Artificial Intelligence (AI) is a broad field encompassing various technologies that have been developed over the past 50 years to enable machines to perform tasks traditionally requiring human intelligence, such as perceiving, reasoning, learning, and interacting (Ergen, 2019). However, recent advancements in generative AI (GenAI), particularly models like ChatGPT, have brought unprecedented attention to AI’s transformative potential across multiple industries (Hong et al., 2022 ; Lucci et al., 2022 ). Unlike predictive (pre-generative) AI which focuses predictions and decision making through a variety of machine learning and modelling techniques, Generative AI specializes in creating new content, such as text, images, and codes by using models of deep learning (Dai, 2023 ; Tang & Nichols, 2024 ). This distinction is essential to understand the breadth of AI applications in education.

Artificial intelligence in education (AIEd) is an evolving interdisciplinary arena incorporating AI technologies to renovate and enhance teaching and learning environments. Particularly, the application of AI in science teaching and learning is becoming more popular, even as interest in AI’s effects on general education is growing (Chiu et al., 2023 ; Gonzalez et al., 2017 ). More specifically, machine learning, a specific artificial intelligence technology, has been applied to automatically evaluate scientific models used in the education sector. Zhai et al. ( 2022 ) employed machine learning techniques to assess the quality of these models after gathering student responses to activities. Their research demonstrates how artificial intelligence can be used to automate assessment procedures and provide students with timely and detailed feedback on their work in the area of science education (Zhai, C Haudek, Zhai et al., 2020a , b , 2022 ). Similarly, Popenici and Kerr ( 2017 ) conducted a study to investigate the impact of AI on the teaching-learning process in higher education settings. Their study focused on how intelligent technologies are affecting student learning and traditional teaching approaches in education. Their research presents valuable insights into the incorporation of AI within science education contexts.

Zawacki-Richter et al. ( 2019 ), in their systematic assessment of AI applications in higher education, focused on the vital role that teachers can play in this domain. Their results suggest how important it is to explore and understand the needs and perceptions of teachers when integrating these technologies into teaching-learning settings. Likewise, Xu and Ouyang (2022) employed a systematic literature review method to identify and summarize research studies and classify the roles of AI in the educational system. Their findings advocate the use of AI is within the education environment to support its role in three ways: (1) AI as a new subject, (2) AI as an immediate mediator, and AI as a complementary aid to impact the teacher-learner, learner-self, and learner-learner relationships.

Though artificial intelligence has flourished in numerous domains within the education system, a comprehensive analysis of its role, advantages, and challenges in science education must be further explored through empirical investigations. This knowledge gap might prompt teachers, policymakers, and educational administrators to base decisions on patchy as well as limited information, lacking potential opportunities to enhance science teaching and learning with the help of AI. To fill this gap, the present paper provides a systematic review that comprehensively examines and consolidates AI’s impact on science education, evidenced by the empirical publications published from 2014 to 2023. While GenAI represents a significant leap in AI capabilities, this review considers the full spectrum of AI technologies, including both pre-GenAI and GenAI developments. In this way, we attempt to provide a holistic perspective on the current landscape, aiding stakeholders in leveraging AI’s potential while also considering its challenges and ethical implications for educational domains. The overarching objective of this review is to provide insights that could guide future research endeavors and advocate for evidence-based practices to enrich science education through the effective utilization of artificial intelligence.

Research Background

Overview of science education.

The goal of science education is not only to teach scientific knowledge but also to develop a scientifically literate populace capable of engaging in scientific reasoning and decision-making (Almasri, 2021 ; Grinnell, 2021 ). This aligns with the “Science for All” movement, emphasizing the importance of science education for all students, not just those pursuing careers in science (Almasri et al., 2022 ; Mansour, 2009 ). Students’ scientific literacy and critical thinking abilities are developed through the teaching and learning of scientific theories, procedures, and experiments in science education (Alharbi et al., 2022 ; Liu & Pásztor, 2022 ; Mogea, 2022 ; Zulyusri et al., 2023 ).

The nature of science education extends beyond content-based instruction to include student-centered activities and the development of scientific literacy for citizenship (Almasri et al., 2021 ; Irez, 2006a , b ; Kolstø, 2001 ). National development hinges significantly on robust contributions from the scientific community, driving economic growth and propelling the overall advancement of a nation (Hewapathirana & Almasri, 2022 ; Kola, 2013 ). The “Call to Action” for science education highlights an compelling necessity to improve educational approaches and make them consistent with the demands of the 21st century (Holme, 2021 ; Ibáñez & Delgado-Kloos, 2018 ). It is essential for developing students’ foundational knowledge, intriguing their curiosity, and getting them ready for STEM careers as per the contemporary world’s needs. Through AI incorporation, science education can be made more interesting, approachable, and pertinent for students of all ages and backgrounds by emphasizing experiential learning.

Prospects of Incorporating AI in Science Learning

With AI technology’s continuous evolution and popularity, the possibilities for its application in science education are promising but not without challenges. AI has the capability to transform the way science is taught and learned. One of the most compelling applications of AI in science education is its ability to simulate scientific experiments and provide virtual laboratory experiences to science learners. This ensures that students can practice and develop their scientific skills in a safe and controlled environment, potentially saving expenses and offering new opportunities for exploring scientific concepts that may not be feasible in traditional laboratory settings (Wahyono et al., 2019 ). However, these virtual experiences may lack the tactile and hands-on aspects of interaction with the physical world (Tang & Cooper, 2024 ), which are crucial for certain types of learning.

By leveraging AI, educators can also move away from traditional, one-size-fits-all approaches to education and instead provide personalized and interactive learning experiences for students. AI-powered algorithms can go beyond simply providing recommendations and assessments to conducting deep analyses of students’ learning patterns, allowing for highly personalized learning experiences (Zhai et al., 2021 ; Zhai et al., 2020a , b ). However, the effectiveness of these personalized learning systems depends heavily on the quality and representativeness of the data they are trained on, which can sometimes introduce biases and perpetuate existing inequities.

In addition, students can also benefit from immediate feedback and adaptive learning pathways, ensuring that they are able to address any misconceptions or gaps in their understanding of scientific phenomena (Mavroudi et al., 2018 ). AI can also help science educators track and monitor students’ progress more effectively, allowing for targeted interventions and support where necessary. Moreover, the use of AI can enable the development of interactive and immersive learning environments, making science education more engaging and accessible to students with diverse learning styles and needs. As AI continues to advance, the potential for its integration into science learning is likely to grow, presenting exciting opportunities to transform and elevate the science education experiences for students at all levels.

Anticipated Benefits of AI Implementation in Science Teaching

Artificial intelligence (AI) has numerous benefits in science education, profoundly affecting teaching and learning for science subjects. AI programs can study how students learn and change the material to fit each student’s needs, skills, and the way they learn. This way of creating educational material helps students learn better and faster. It lets them go at their own speed and in a way that matches how they like to learn (Zawacki-Richter et al., 2019 ). Also, AI-powered data analysis can help science teachers understand how well their students are doing in specific scientific subjects and where they might need extra help.

Another significant advantage is the improvement of exploratory learning through virtual labs and reenactments. AI-powered instruments have the potential to recreate complex logical tests, which may be illogical or hazardous to conduct in a conventional classroom setting. These virtual situations offer hands-on learning encounters and permit understudies to try distinctive scenarios, improving their understanding of scientific concepts (Ibáñez et al., 2018). This approach was not as supportive of extending understudy engagement but too valuable to democratize access to high-quality science instruction. AI devices can interface understudies and teachers over diverse geographies, empowering the trade of logical thoughts and cultivating a worldwide point of view on logical issues. This interconnecting also permits integrating differing datasets into the educational modules, uncovering understudies to real-world logical challenges and datasets (Holmes et al., 2023 ).

Ethical Considerations of AI Integration in Education

Even though artificial intelligence (AI) in education has bright futures, important ethical issues need to be resolved when integrating AI in the classroom. Many researchers have stressed considering the ethical implications and the need for character education in the era of AI (Burton et al., 2017 ; Cathrin & Wikandaru, 2023 ). The lack of critical reflection on the pedagogical and ethical implications and the risks of implementing AI applications in higher education underscores the need for a comprehensive ethical framework (Bozkurt et al., 2021 ).

Additionally, the integration of AI into educational settings presents new ethical obligations for teachers, necessitating a revaluation of ethical frameworks and responsibilities (Adams et al., 2022 ). Moreover, the introduction of ethics courses in academic training and capacity building of AI development actors can facilitate the integration of ethical values and the development of responsible AI (Kiemde & Kora, 2022 ). The ethical implications of AI in education extend beyond technical considerations to encompass broader societal impacts, such as privacy protection and social justice (Hermansyah et al., 2023 ).

Educators and students must understand, evaluate, and familiarize themselves with the uses of generative AI tools and consider their potential impacts on academic integrity. This involves recognizing when and how AI is used, assessing the reliability and validity of AI-generated outputs, and understanding the ethical and social implications of AI applications (Akgun & Greenhow, 2021 ). Moreover, the application of AI in education brings questions about educational equity and access to the fore. Systemic biases in AI algorithms and data can perpetuate inequities, making it crucial to address these biases effectively (Adams et al., 2022 ).

Purpose of the Study

The primary goal of the present study is to highlight the potential benefits as well as any disparities that might result from the widespread use of AI in science subjects. These discrepancies could be related to gaps in infrastructure, preparedness in the region, or accessibility. Eventually, the present investigation aims to furnish all relevant stakeholders, i.e., educators, learners, policymakers, and curriculum designers—with a deeper understanding of the current interaction between AI and science education.

The following research questions are addressed in this systematic review:

Impact on Learning Outcomes: How do AI tools impact student learning outcomes and engagement in science education?

Contexts of AI Adoption: What are the potential disparities in the uptake of AI tools within science education, considering differences among countries, educational levels, and subject areas?

Student and Teacher Perceptions: What are the perceptions and attitudes of students and educators towards the use of AI tools in science education?

Pedagogical Challenges: What are the identified challenges associated with using AI in science education?

Methodology

The authors worked diligently to explore how artificial intelligence contributes to science education thoroughly. We followed a structured process suggested by the widely used review methodology called Preferred Reporting Items for Systematic Reviews and Meta-Analyses (Page et al., 2021 ). The review approach of the current study comprised various stages – defining the study’s purpose along with specific research questions, formulating a protocol, an extensive literature search, a systematic screening process, extracting pertinent data, and synthesizing the findings. The sections below specifically mention how each of these steps was carried out for this study.

Search Strategy

We accessed a range of prominent digital repositories and databases to search the relevant literature. Particularly, IEEE Xplore, Springer, Tylor and Francis, ERIC (U.S. Dept. of Education), Science Direct, and Wiley were targeted to search the relevant literature. We also used Google Scholar and Google to make sure that we didn’t miss any important information. We used advanced search features to limit our search results to papers published between 2014 and 2023, ensuring that our search was focused and up to date (Piasecki et al., 2018 ).

We utilized a smart search strategy along with a range of search terms and operators to accomplish this. Our search strategy used a combination of key terms such as “artificial intelligence”, “AI”, “generative AI”, “ChatGPT,” “machine learning”, “robotics”, “intelligent system,” and “expert system” paired with descriptors like “science education,” “science learning,” or simply “science”. These combinations, along with their possible variations, were systematically applied to search within the papers’ titles, keywords, and abstracts. This search strategy was created with the aim to identify and consider a broad range of empirical work relating to the use of artificial intelligence in the teaching and learning of science.

Eligibility Criteria

Describing clear eligibility (inclusion and exclusion) criteria allows for setting boundaries for a systematic literature review. These criteria were aimed at creating a structured framework that facilitates the inclusion of studies meeting essential prerequisites while excluding those that don’t align with our research objectives. The inclusion criteria are as follows:

The paper must have employed empirical methods, such as quantitative, qualitative, or/and mixed methods, warranting a rigorous data collection and analysis approach.

The paper should have conducted research in an educational setting, encircling primary, middle, secondary, or higher education, emphasizing the applicability of the findings in educational environments.

A pivotal criterion necessitates the use of artificial intelligence in the study. This AI practice should have been applied to the teaching-learning process, and empirical data collected and integrated into the study.

Studies should be related to a science-related content area, spanning courses like chemistry, physics, biology, engineering, health sciences, or other related disciplines, ensuring applicability to the research topic.

The timeframe specified for publication years, from 2014 to 2023, targets to capture relevant studies within the past decade, ensuring the examination of recent developments in AI-based learning.

Our exclusion criteria were as follows:

Excluding studies that are not empirical in nature, such as theoretical papers, reviews, editorials, or opinion pieces, to maintain the focus on empirical research.

Studies written in languages other than English.

Studies that did not explicitly mention the AI use within a learning context.

Excluding studies that are solely available in abstract form and lack full-length publications.

The Screening Process

In the months of November and December 2023, we went on a thorough hunt for the required information. We started by searching through loads of databases and found 5,121 articles. After getting rid of duplicates, checking publication dates and titles, and looking at abstracts to see if they met the eligibility criteria for the present study, we ended up with 128 articles. From there, we excluded 41 studies because they didn’t really dive into science education. That left us with 87 articles that we pored over super carefully. We made sure they fit our criteria and answered our research questions before diving into them. From the pool of these 61 articles, ten (13) studies were identified as lacking clear empirical evidence regarding the use of artificial intelligence and were subsequently excluded. This process resulted in a final dataset of 74 articles that were included in the systematic review. See Table  1 for the list of studies included in our review. Figure  1 demonstrates a quick preview of the search strategy and the screening process.

PRISMA review process

Coding and Analysis

We used a mix of qualitative and quantitative content analysis techniques to synthesize the findings of the empirical papers. To ensure inter-rater reliability in relation to the quality of article coding procedures, a small random sample consisting of 20 selected articles was independently coded by multiple raters. The calculated reliability level exceeded 92%, signifying a high degree of agreement across coding categories. We conducted a comprehensive examination of the studies from various perspectives. Firstly, we analyzed the characteristics of the data set, including the country where the studies were conducted, the journal name, the content area, and the educational level.

In this comprehensive review of the literature, we carefully evaluated seventy-four (74) empirical studies that deal with the incorporation of AI into science education. Numerous research approaches, such as mixed, qualitative, and quantitative approaches, were used in these studies. Examining the publication dates of the included papers revealed that they were dispersed over the review study’s 10-year focal period (2014 to 2023). The year 2023, with twenty-seven (27) papers, led the way, demonstrating researchers’ strong interest in the most recent research on the application of artificial intelligence in science education. This was followed by ten (10) studies in 2022, eight (08) studies in 2021, and nine (09) studies in year 2020. For more information on the year-wise publication, see Fig.  2 .

Year-wise publications

The review process of the present study involved the consolidation of findings pertaining to four distinct research questions, each of which is presented separately in the following sections.

RQ1: Impact on Outcomes Comparison

The first research question of the current study specifically addressed the primary intention of this systematic research i.e., analyzing the reported impact of AI-enhanced learning on students’ learning outcomes in science education. The empirical papers reviewed showed that artificial intelligence has been used within science education for a variety of purposes, such as engaging students in the learning process with a strong sense of motivation and interest (Balakrishnan, 2018 ), generating tests of science subjects (Aldabe & Maritxalar, 2014 ; Nasution & Education, 2023 ), scoring and providing personalized feedback on students’ assignments (Azcona et al., 2019 ; Maestrales et al., 2021 ; Mirchi et al., 2020 ), and predicting student performance (Blikstein et al., 2014 ; Buenaño-Fernández et al., 2019 ; Jiao et al., 2022a , b ).

AI-based tools were found to have a positive influence on student’ learning outcomes in science-related courses. The experimental group that was exposed to AI integration in their learning environments exhibited significantly higher scores in their academic tests compared to the control group who experienced traditional learning environments (Alneyadi & Wardat, 2023 ; Koć-Januchta et al., 2020 ). Ledesma and García ( 2017 ) and Lamb et al. ( 2021 ) highlighted AI’s capacity to identify complex concepts and enhance problem-solving skills significantly in subjects (Lamb et al., 2021 ; Ledesma & García, 2017 ). Ferrarelli and Iocchi ( 2021 ), Cochran et al. ( 2023 ), and Figueiredo and Paixão ( 2015 ) showcased how AI is helpful in fostering improved subject understanding and heightened motivation among students, particularly in physics and chemistry (Ferrarelli & Iocchi, 2021 ; Figueiredo et al., 2016 ).

Lee et al. ( 2022 ) argue that AI-based tools such as chatbots can help students become cognitively more active in the learning process(Lee et al., 2022 ). Likewise, Azcona ( 2019 ) suggests that personalized learning facilitated by AI can help reduce the gap between lower- and higher-performing students. Moreover, AI-powered education can empower students to predict their learning outcomes and strategically regulate their learning behavior (Buenaño-Fernández et al., 2019 ).

The effectiveness of different AI models varied across studies. Nguyen et al. ( 2023 ) highlighted the performance disparities among AI models like Google Bard, ChatGPT, and Bing Chat in addressing biology problems for Vietnamese students (Nguyen et al., 2023 ). While chatbots positively influenced online learning experiences, their impact on academic achievement remained variable (Almasri, 2022a ; Deveci Topal et al., 2021 ). In essence, these findings underscore the potential of AI to augment science education by enhancing student understanding, motivation, and engagement. However, they also underscore the importance of addressing challenges related to AI’s adaptability to subject matter and context and the need for continued exploration into AI’s comparative impact on academic achievement vis-à-vis traditional teaching methods in science education. Daher et al. ( 2023 ) pointed to AI’s limitations in comprehending specific subject matter, which could impact its effectiveness in aiding student learning. Cooper ( 2023 ) emphasized the need for educators to critically evaluate and adapt AI-generated resources to suit diverse teaching contexts.

RQ2: Contexts of AI Adoption

In our second research question, we aimed to explore the potential disparities in the uptake of AI tools within science education, considering differences among countries, educational levels, and subject areas. The results disclosed that artificial intelligence has been incorporated in a variety of subject areas within science education, including physical and natural sciences. The studies reviewed were highly dominated by investigations that did not specify any particular domain of science ( n  = 15, 20.30%), but they preferred to use “Science” as the subject area in their papers. Next in line, was the subject of Physics with the second-highest number of papers ( n  = 10, 13.50%). The list was continued by Biology and Programming with nine ( n  = 9, 12.16%) and eight ( n  = 8, 10.81%) papers, respectively. The subjects of Mathematics and Engineering occupied about 16% (with 06 papers each) of the total papers. Out of 74 studies, only five (05) studies were conducted to investigate the use of AI for AI education. The subjects of Computers/technology were focused on in four papers. Lastly, only one paper was centered around the use of artificial intelligence in Statistics and Earth Science. Figure  3 provides a summary of the content areas that were the focus of the papers included in our review.

Studies distributed around subject areas within science education

While examining the various educational levels that benefited from the integration of artificial intelligence in some manner, we found that nearly half of the studies ( n  = 35, 47%) belonged to undergraduate level, followed by high schools ( n  = 15, 20%) and middle schools ( n  = 7, 10%) respectively. Out of the total 74 papers, about 8% of the studies ( n  = 6) were conducted in secondary school contexts. Likewise, 8% of the studies involved multiple levels of educational settings. In contrast, three of the studies (about 4%) were conducted in elementary school. Only 2% of the papers belonged to the college level, and only one study was conducted at the postgraduate and college levels. Figure  4 provides a quick distribution of the students in various educational contexts.

Studies distributed across various educational levels

Similarly, country-wise categorization of the papers exposed that about 38% of the studies ( n  = 25) were conducted in the context of the United States. Germany ranked second in the list with six studies (8%). This was followed by four studies (5.4%) carried out in Turkey and Australia. UAE and Malaysia followed in the race, each with three papers. Eight countries, including Sweden, China, Mexico, Saudi Arabia, Spain, the Netherlands, Israel, and Taiwan, contributed about 21.6% of the total papers, each with two studies. The rest of the papers ( n  = 10, 13.51%) were written in the context of 10 different countries across the globe (see Fig.  5 for details).

Country of research context

RQ3: Student and Teacher Perceptions

With our third research question, we attempted to explore science teachers’ and students’ perceptions regarding the integration of AI. The studies revealed multifaceted perspectives on the integration of AI in science education among both students and teachers. The effectiveness of AI tools in augmenting learning experiences garnered students’ attention. Students showcased increased engagement and improved subject understanding through AI-based interventions, indicating positive perceptions of AI’s efficacy in enhancing learning outcomes (Ferrarelli & Iocchi, 2021 ; Ledesma & García, 2017 ). For example, Bitzenbauer (2023) found that ChatGPT’s use in Physics classrooms favorably influenced students’ perceptions in Germany. Avelino et al. ( 2017 ) echoed this sentiment for undergraduate students in the United States.

Students reported their increased interest in science courses when AI was integrated into the learning environments. Students particularly admired the AI’s power to provide prediction and personalized feedback (Azcona et al., 2019 ). According to Elkhodr et al. ( 2023 ), science students perceive AI-based tools as useful and enjoyable learning resources, while most students showed a willingness to use them in the future.

Our analysis suggests that science teachers hold a high level of acceptance and positive attitudes toward AI’s utilization in the classroom. Teachers welcome its use with positive correlations to self-efficacy, ease of use, and behavioral intentions (Al Darayseh, 2023 ). They perceive this technology as the need of the hour to boost student engagement (Almasri, 2022b ; Nersa, 2020 ). Empirical papers included in the current study exposed fluctuating degrees of comfort and adaptability among educators and students in incorporating AI into their teaching and learning processes. Al Darayseh ( 2023 ) noted that science teachers exhibited favorable attitudes toward AI’s integration, possibly due to the perceived reduced effort in its utilization and their confidence in their essential skills to incorporate AI effectively.

There are several factors that influence teachers’ intentions and behavior regarding the use of AI, including self-esteem, expected benefits, ease of utilization, and their overall attitude toward AI applications. Teachers’ favorable disposition towards AI use is also due to their perception of reduced effort in its utilization.(Nja et al., 2023 ). Overall, teachers consider AI tools like ChatGPT to be helpful in designing science units, rubrics, and quizzes (Cooper, 2023 ). Yet, challenges associated with AI integration could influence students’ and teachers’ perceptions of AI’s reliability and accuracy in supporting educational goals, posing potential barriers to widespread acceptance and utilization.

RQ4: Pedagogical Challenges

Our analysis uncovered several challenges associated with the integration of AI in terms of complexities and limitations of its use within this particular domain of the education system. One prevalent challenge revolved around AI’s capability to comprehend and effectively address specific subject matter. Daher et al. ( 2023 ) highlighted instances where AI, like ChatGPT, encountered difficulties in understanding complex concepts in chemistry. They argue that the information provided by AI tools such as ChatGPT is limited because it depends on the data it was taught with. It might not have access to the latest or most complete knowledge in a particular domain.

Adaptability and contextual relevance emerged as significant concerns regarding the use of AI within science teaching. Cooper ( 2023 ) stressed that teachers critically evaluate AI-based resources and adapt them to their teaching contexts. He suggested that a one-size-fits-all approach might not suffice in accommodating the intricacies of varied educational environments. Another challenge pertained to the effectiveness and performance variability of different AI models. Nguyen et al. ( 2023 ) showcased the varying performance levels of different AI models, indicating disparities in their ability to address specific subject-related challenges. This variability in performance, as seen in different studies, implies the need for thorough evaluation and selection of appropriate AI tools tailored to the needs of specific subject areas. Furthermore, ethical considerations and limitations in AI’s current capabilities were notable concerns. Kieser et al. ( 2023 ) raised ethical issues regarding students using AI to fabricate data for class assignments. Addressing these challenges requires a nuanced approach that acknowledges the potential and constraints of AI while striving to optimize its role in enhancing science education effectively.

Discussions

The primary objective of this review was to investigate the interaction between artificial intelligence and science education. Our study uncovered a diverse landscape of AI usage within science education. Our results suggested that integrating AI tools in science education consistently improves students’ academic performance. This was evident in higher test scores and a better understanding of complex concepts compared to those in traditional learning environments (Alneyadi & Wardat, 2023 ; Koć-Januchta et al., 2020 ; Siddaway et al., 2019 ).

Literature suggests that integrating artificial intelligence into the teaching-learning process facilitates understanding complex scientific topics (Lamb et al., 2021 ; Ledesma & García, 2017 ). It also helps develop problem-solving skills considerably, leading to a better understanding of subjects, particularly in fields like physics and chemistry. Furthermore, it was revealed that science teachers use AI-driven tools to engage students effectively and foster their motivation and interest in science-related subjects (Balakrishnan, 2018 ). Personalized learning through AI tools helps bridge performance gaps between lower and higher-performing students (Azcona et al., 2019 ), contributing to a more equitable learning environment. AI-generated personalized feedback also contributed to students’ increased engagement in the learning process (Azcona et al., 2019 ; Maestrales et al., 2021 ; Mirchi et al., 2020 ).

The current systematic review suggests that the distribution of studies within various subject areas in science education showcases a dominant focus on science in general, followed by physics, biology, programming, and other specific science subjects. Some specific domains, like earth science and statistics, received comparatively the least attention in the reviewed literature.

The distribution of research papers across countries demonstrates certain disparities. The United States had a significantly higher number of studies compared to other nations. Germany ranked second on the list. Turkey and Australia followed, while UAE, Malaysia, and Canada contributed with a moderate number of studies. Several countries had minimal representation, with a diverse spread across multiple nations. Concentration of studies in certain countries like the United States and Germany might suggest varying levels of research infrastructure or prioritization of AI in education compared to other nations with fewer studies. This could potentially lead to disparities in the implementation and impact of AI tools in science education among different regions globally.

Our analysis found that students exhibit increased engagement and interest in science courses when AI tools are integrated into learning environments. This heightened interest is attributed to AI’s ability to provide predictions and personalized feedback (Jiao et al., 2022b ), making learning more engaging and enjoyable (Hewapathirana & Almasri, 2022 ). Students perceive AI-based tools as useful and beneficial for their learning experiences. They acknowledge AI’s effectiveness in improving subject understanding and express a willingness to continue using such tools in the future (Elkhodr et al., 2023 ).

Similar to students, science teachers also demonstrate positive attitudes and acceptance of AI tools in the classroom, correlating with perceived benefits in student engagement and their own teaching efficacy. Teachers view AI integration as a means to enhance student engagement, with some perceiving it as a way to reduce effort while teaching, leading to increased confidence in utilizing AI effectively (Al Darayseh, 2023 ). Specifically, teachers perceive ChatGPT a valuable resource for designing science units, rubrics, quizzes, and teaching aids, offering convenience and potential enhancement to their teaching methodologies.

While AI showed promise in improving learning outcomes, there are challenges related to its adaptability to subject matter and context. Some studies pointed out limitations in comprehending specific subjects, potentially impacting the effectiveness of AI in aiding student learning. Previous research suggests that AI tools like ChatGPT face difficulties in comprehending and addressing complex concepts in specific subject areas, as seen in instances within chemistry (Daher et al., 2023 ). The dependency on the data it was trained with limits its access to the latest or most comprehensive knowledge in particular domains. A uniform approach might not adequately cater to the complexities and nuances of varied educational environments, emphasizing the need for adaptable solutions (Cooper, 2023 ). Addressing these challenges requires a balanced approach that acknowledges AI’s potential and constraints in science education. Thus, teachers are advised to critically evaluate AI-generated resources and tailor them to diverse teaching contexts.

Our research provides important implications for teacher preparation and in-service professional development regarding AI in our society and implementing AI tools and processes in K-12 education (Antonenko & Abramowitz, 2023 ). As a whole, integrating artificial intelligence positively enhances the process and outcome of science education. However, there are certain limitations and challenges associated with its use. Providing training and support to educators to effectively utilize AI tools can enhance their confidence and capabilities in integrating these technologies into teaching practices. Moreover, establishing clear ethical guidelines and frameworks for the responsible use of AI in education can mitigate the risk of misuse and ensure ethical practices among students and educators.

Limitations

Some of the inherent limitations of this research review are discussed in this section. First, just like with other reviews, the search terms and strategies determine which research papers are included. Although a thorough and methodologically rigorous search was the goal, using different search terms might have turned up more articles that could have been included in the review. Furthermore, a few particular research databases were searched in order to find pertinent empirical literature for inclusion in this research review. An alternative methodological strategy would have involved restricting the search for research to a predetermined list of scholarly, peer-reviewed journals. A smaller sample of literature for inclusion may have occurred due to this strategy. However, greater control over validity, reliability, and credibility during the search and inclusion processes was sought to the best level. Lastly, we may have missed some grey literature, such as dissertations and conference proceedings, that was not indexed in the databases/repositories that we used.

This systematic review examined the impact, perceptions, and challenges associated with the integration of Artificial Intelligence (AI) in the teaching and learning of science. Our analysis uncovered a landscape rich in prospective benefits and challenges. The usage of AI in science education steadily established positive impacts on student learning outcomes. It encourages participation in the educational process, enhances comprehension of the subject, and boosts motivation in the students. Both students and teachers showed positive views of AI’s effectiveness and ease of use. Both acknowledged its potential to boost learning experiences. Nevertheless, issues arose from AI’s limited ability to understand particular subject matter, its inability to adjust to various educational contexts, and the variation in performance between various AI models. Ethical considerations regarding responsible use also appeared to be an important concern. Addressing these challenges demands a careful approach that considers thorough evaluation and adaptation to diverse contexts. Educators and policymakers should navigate these complexities to join the potential of AI in science education while ensuring ethical practices and maximizing its impact on students’ learning journey worldwide.

Adams, C., Pente, P., Lemermeyer, G., Turville, J., & Rockwell, G. (2022). Artificial intelligence and teachers’ new ethical obligations. The International Review of Information Ethics , 31 (1). https://doi.org/10.29173/irie483 .

Akgun, S., & Greenhow, C. (2021). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics , 2 , 431–440. https://doi.org/10.1007/s43681-021-00096-7 .

Article   Google Scholar  

Aldabe, I., & Maritxalar, M. (2014). Semantic similarity measures for the generation of science tests in basque. IEEE Transactions on Learning Technologies , 7 (4), 375–387. https://doi.org/10.1109/TLT.2014.2355831 .

Al Darayseh, A. (2023). Acceptance of artificial intelligence in teaching science: Science teachers’ perspective. Artificial Intelligence , 4 , 100132. https://doi.org/10.1016/j.caeai.2023.100132 .

Alharbi, S. M., Elfeky, A. I., & Ahmed, E. S. (2022). The effect of e-collaborative learning environment on development of critical thinking and higher order thinking skills. Journal of Positive School Psychology , 6 (6), 6848–6854.

Google Scholar  

Almasri, F. (2021). Collaborative learning in science education: effects of student’s gender attitudes and achievement in science education University of Warwick, Retrieved from http://webcat.warwick.ac.uk/record=b3853208 .

Almasri, F. (2022a). The impact of e-learning, gender-groupings and learning pedagogies in biology undergraduate female and male students’ attitudes and achievement. Education and Information Technologies , 27 , 8329–8380. https://doi.org/10.1007/s10639-022-10967-z .

Almasri, F. (2022b). Simulations to teach science subjects: Connections among students’ engagement, self-confidence, satisfaction, and learning styles. Education and Information Technologies , 27 (5), 7161–7181. https://doi.org/10.1007/s10639-022-10940-w .

Almasri, F., Hewapathirana, G. I., Alhashem, F., Daniel, C. E., & Lee, N. (2022). The effect of gender composition and pedagogical approach on major and non-major undergraduates biology students’ achievement. Interactive Learning Environments , 1–33. https://doi.org/10.1080/10494820.2022.2066138 .

Almasri, F., Hewapathirana, G. I., Ghaddar, F., Lee, N., & Ibrahim, B. (2021). Measuring attitudes towards biology major and non-major: Effect of students’ gender, group composition, and learning environment. Plos One , 16 (5), e0251453. https://doi.org/10.1371/journal.pone.0251453 .

Alneyadi, S., & Wardat, Y. J. C. E. T. (2023). ChatGPT: Revolutionizing student achievement in the electronic magnetism unit for eleventh-grade students in Emirates schools. Contemporary Educational Technology , 15 (4), ep448. https://doi.org/10.30935/cedtech/13417 .

Antonenko, P., & Abramowitz, B. (2023). In-service teachers’(mis) conceptions of artificial intelligence in K-12 science education. Journal of Research on Technology in Education , 55 (1), 64–78. https://doi.org/10.1080/15391523.2022.2119450 .

Avelino, F., Wittmayer, J. M., Kemp, R., & Haxeltine, A. (2017). Game-changers and transformative social innovation. Ecology and Society, 22 (4). https://www.jstor.org/stable/26798984

Azcona, D., Hsiao, I. H., & Smeaton, A. F. (2019). Detecting students-at-risk in computer programming classes with learning analytics from students’ digital footprints. User Modeling and User-Adapted Interaction , 29 , 759–788. https://doi.org/10.1007/s11257-019-09234-7 .

Balakrishnan, B. (2018). Motivating engineering students learning via monitoring in personalized learning environment with tagging system. Computer Applications in Engineering Education , 26 (3), 700–710. https://doi.org/10.1002/cae.21924 .

Blikstein, P., Worsley, M., Piech, C., Sahami, M., Cooper, S., & Koller, D. (2014). Programming pluralism: Using learning analytics to detect patterns in the learning of computer programming. Journal of the Learning Sciences , 23 (4), 561–599. https://doi.org/10.1080/10508406.2014.954750 .

Bozkurt, A., Karadeniz, A., Baneres, D., Rodríguez, M. E., & Rodríguez, M. E. (2021). Artificial intelligence and reflections from educational landscape: A review of AI studies in half a century. Sustainability , 13 (2), 800. https://doi.org/10.3390/su13020800 .

Buenaño-Fernández, D., Gil, D., & Luján-Mora, S. (2019). Application of machine learning in predicting performance for computer engineering students: A case study. Sustainability , 11 (10), 2833. https://doi.org/10.3390/su11102833 .

Burton, E., Goldsmith, J., Koenig, S., Kuipers, B., Mattei, N., & Walsh, T. (2017). Ethical considerations in artificial intelligence courses. AI Magazine , 38 (2), 22–34. https://doi.org/10.1609/aimag.v38i2.2731 .

Cathrin, S., & Wikandaru, R. (2023). The future of character education in the era of artificial intelligence. Humanika Kajian Ilmiah Mata Kuliah Umum , 23 (1). https://doi.org/10.21831/hum.v23i1.59741 .

Chiu, T. K., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence , 4 , 100118. https://doi.org/10.1016/j.caeai.2022.100118 .

Cochran, K., Cohn, C., Rouet, J. F., & Hastings, P. (2023, June). Improving automated evaluation of student text responses using gpt-3.5 for text data augmentation. In International conference on artificial intelligence in education (pp. 217–228). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-36272-9_18

Cooper, G. (2023). Examining science education in chatgpt: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology , 32 (3), 444–452. https://doi.org/10.1007/s10956-023-10039-y .

Daher, W., Diab, H., & Rayan, A. (2023). Artificial intelligence generative tools and conceptual knowledge in problem solving in Chemistry. Information , 14 (7), 409. https://doi.org/10.3390/info14070409 .

Dai, Y. (2023). Negotiation of epistemological understandings and teaching practices between primary teachers and scientists about artificial intelligence in professional development. Research in Science Education , 53 (3), 577–591. https://doi.org/10.1007/s11165-022-10072-8 .

Deveci Topal, A., Eren, D., C., & Kolburan Geçer, A. (2021). Chatbot application in a 5th grade science course. Education and Information Technologies , 26 (5), 6241–6265. https://doi.org/10.1007/s10639-021-10627-8 .

Elkhodr, M., Gide, E., Wu, R., & Darwish, O. (2023). ICT students’ perceptions towards ChatGPT: An experimental reflective lab analysis. STEM Education , 3 (2), 70–88. https://doi.org/10.3934/steme.2023006 .

Ferrarelli, P., & Iocchi, L. (2021). Learning newtonian physics through programming robot experiments. Technology Knowledge and Learning , 26 (4), 789–824. https://doi.org/10.1007/s10758-021-09508-3

Figueiredo, M., Esteves, L., Neves, J., & Vicente, H. (2016). A data mining approach to study the impact of the methodology followed in chemistry lab classes on the weight attributed by the students to the lab work on learning and motivation. Chemistry Education Research and Practice , 17 (1), 156–171. https://doi.org/10.1039/C5RP00144G .

Figueiredo, M., & Paixão, F. (2015). O Papel da Observação na Compreensão da Natureza do Conhecimento Científico: Uma Proposta Didática Baseada nas Reações de Combustão. In Proceedings do Encontro Nacional de Educação em Ciências – ENEC 2015 - Ciência com Cultura (pp. 205–209). Lisboa.

Gonzalez, A. J., Hollister, J. R., DeMara, R. F., Leigh, J., Lanman, B., Lee, S. Y., & Wilder, B. (2017). AI in informal science education: Bringing turing back to life to perform the turing test. International Journal of Artificial Intelligence in Education , 27 , 353–384. https://doi.org/10.1007/s40593-017-0144-1 .

Grinnell, F. (2021). Scientific Inquiry, pluralism and complementarity. Scientific Inquiry Pluralism and Complementarity . https://doi.org/10.31235/osf.io/gejwv .

Hermansyah, M., Najib, A., Farida, A., Sacipto, R., & Rintyarna, B. S. (2023). Artificial intelligence and ethics: Building an artificial intelligence system that ensures privacy and social justice. International Journal of Science and Society , 5 (1), 154–168. https://doi.org/10.54783/ijsoc.v5i1.644 .

Hewapathirana, G., & Almasri, F. (2022). Active learning compared with lecture-based pedagogies in gender and socio-cultural context-specific major and non-major biology classes. Handbook of research on active Learning and Student Engagement in Higher Education (pp. 293–319). IGI Global.

Holmes, W., Bialik, M., & Fadel, C. (2023). Artificial intelligence in education . Globethics.

Holme, T. A. (2021). Considering the call to action for science education (Vol. 98, pp. 2739–2740). ACS.

Hong, F., Dou, W., & Chen, S. (2022). Research on the impact of artificial intelligence on government public service quality . Paper presented at the 2022 2nd International Conference on Public Management and Intelligent Society (PMIS 2022).

Ibáñez, M. B., & Delgado-Kloos, C. (2018). Augmented reality for STEM learning: A systematic review. Computers & Education , 123 , 109–123. https://doi.org/10.1016/j.compedu.2018.05.002 .

Irez, S. (2006a). Are we prepared? An assessment of preservice science teacher educators’ beliefs about nature of science. Science Education , 90 (6), 1113–1143. https://doi.org/10.1002/sce.20156 .

Irez, S. J. S. E. (2006b). Are we prepared? An assessment of preservice science teacher educators’ beliefs about nature of science. 90 (6), 1113–1143.

Jiao, P., Ouyang, F., Zhang, Q., & Alavi, A. H. (2022a). Artificial intelligence-enabled prediction model of student academic performance in online engineering education. Artificial Intelligence Review , 55 (8), 6321–6344. https://doi.org/10.1007/s10462-022-10155-y .

Jiao, P., Ouyang, F., Zhang, Q., & Alavi, A. H. (2022b). Artificial intelligence-enabled prediction model of student academic performance in online engineering education. Artificial Intelligence Review , 55 (8), 6321–6344.

Kiemde, S. M. A., & Kora, A. D. (2022). Towards an ethics of AI in Africa: Rule of education. AI and Ethics , 2 (1), 35–40. https://doi.org/10.1007/s43681-021-00106-8 .

Kieser, F., Wulff, P., Kuhn, J., & Küchemann, S. (2023). Educational data augmentation in physics education research using ChatGPT. Physical Review Physics Education Research, 19 (2), 020150.

Koć-Januchta, M. M., Schönborn, K. J., Tibell, L. A., Chaudhri, V. K., & Heller, H. C. (2020). Engaging with biology by asking questions: Investigating students’ interaction and learning with an artificial intelligence-enriched textbook. Journal of Educational Computing Research , 58 (6), 1190–1224.

Kok-Sing, T., & Nichols, K. (Eds.). (2024). Collection: Artificial intelligence in science education. Research in Science Education . https://link.springer.com/collections/dbihehajcd .

Kola, A. J. (2013). Importance of science education to national development and problems militating against its development. American Journal of Educational Research , 1 (7), 225–229. https://doi.org/10.12691/education-1-7-2 .

Kolstø, S. D. (2001). Scientific literacy for citizenship: Tools for dealing with the science dimension of controversial socioscientific issues. Science Education , 85 (3), 291–310. https://doi.org/10.1002/sce.1011 .

Lamb, R., Hand, B., & Kavner, A. (2021). Computational modeling of the effects of the science writing heuristic on student critical thinking in science using machine learning. Journal of Science Education and Technology , 30 , 283–297. https://doi.org/10.1007/s10956-020-09871-3 .

Ledesma, E. F. R., & García, J. J. G. (2017). Selection of mathematical problems in accordance with student’s learning style. International Journal of Advanced Computer Science Applications , 8 (3). https://doi.org/10.14569/IJACSA.2017.080316 .

Lee, H. S., Pallant, A., Pryputniewicz, S., Lord, T., Mulholland, M., & Liu, O. L. (2019). Automated text scoring and real-time adjustable feedback: Supporting revision of scientific arguments involving uncertainty. Science Education , 103 (3), 590–622.

Lee, Y. F., Hwang, G. J., & Chen, P. Y. (2022). Impacts of an AI-based cha bot on college students’ after-class review, academic performance, self-efficacy, learning attitude, and motivation. Educational Technology Research and Development , 70 (5), 1843–1865. https://doi.org/10.1007/s11423-022-10142-8 .

Liu, Y., & Pásztor, A. (2022). Effects of problem-based learning instructional intervention on critical thinking in higher education: A meta-analysis. Thinking Skills and Creativity , 45 , 101069. https://doi.org/10.1016/j.tsc.2022.101069 .

Lucci, S., Kopec, D., & Musa, S. M. (2022). Artificial intelligence in the 21st century: Mercury learning and information .

Maestrales, S., Zhai, X., Touitou, I., Baker, Q., Schneider, B., & Krajcik, J. (2021). Using machine learning to score multi-dimensional assessments of chemistry and physics. Journal of Science Education and Technology , 30 , 239–254. https://doi.org/10.1007/s10956-020-09895-9 .

Mansour, N. (2009). Science-technology-society (STS) a new paradigm in science education. Bulletin of Science Technology & Society , 29 (4), 287–297. https://doi.org/10.1177/0270467609336307 .

Mavroudi, A., Giannakos, M., & Krogstie, J. (2018). Supporting adaptive learning pathways through the use of learning analytics: Developments, challenges and future opportunities. Interactive Learning Environments, 26 (2), 206–220. https://doi.org/10.1080/10494820.2017.1292531

Mirchi, N., Bissonnette, V., Yilmaz, R., Ledwos, N., Winkler-Schwartz, A., & Del Maestro, R. F. J. P. (2020). The virtual operative assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine. Plos One , 15 (2), e0229596. https://doi.org/10.1371/journal.pone.0229596 .

Mogea, T. (2022). Students’ critical thinking ability in English teaching and learning. Jurnal Pendidikan Dan Sastra Inggris , 2 (3), 157–171.

Nasution & Education. (2023). Using artificial intelligence to create biology multiple choice questions for higher education. Agricultural and Environmental Education , 2 (1).

Nersa, A. (2020). The coverage of children Rights in Amhara television children’s program

Nguyen, P., Trương, H., Nguyen, P., Bruneau, P., Cao, L., & Wang, J. (2023). Evaluation of Google Bard on Vietnamese High School Biology Examination.

Nja, C. O., Idiege, K. J., Uwe, U. E., Meremikwu, A. N., Ekon, E. E., Erim, C. M., & Umalili, B. (2023). Adoption of artificial intelligence in science teaching: From the vantage point of the African science teachers. Smart Learning Environments , 10 (1), 42. https://doi.org/10.1186/s40561-023-00261-x .

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., & Brennan, S. E. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. International Journal of Surgery , 88 , 105906. https://doi.org/10.1136/bmj.n71 .

Piasecki, J., Waligora, M., & Dranseika, V. (2018). Google search as an additional source in systematic reviews. Science and Engineering Ethics , 24 , 809–810.

Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning , 12 (1), 1–13. https://doi.org/10.1186/s41039-017-0062-8 .

Siddaway, A. P., Wood, A. M., & Hedges, L. V. (2019). How to do a systematic review: A best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses. Annual Review of Psychology , 70 , 747–770. https://doi.org/10.1146/annurev-psych-010418-102803 .

Tang, K. S., & Cooper, G. (2024). The role of materiality in an era of generative artificial intelligence. Science & Education , 1–16. https://doi.org/10.1007/s11191-024-00508-0 .

Wahyono, I. D., Fadlika, I., Asfani, K., Putranto, H., Hammad, J., & Sunarti. (2019). New adaptive intelligence method for personalized adaptive laboratories. In 2019 International conference on electrical, electronics and information engineering (ICEEIE) (pp. 196–200). https://doi.org/10.1109/ICEEIE47180.2019.8981477

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? Journal of Educational Technology in Higher Education , 16 (1), 1–27. https://doi.org/10.1186/s41239-019-0171-0 .

Zhai, X., Haudek, C., Shi, K., Nehm, L. H., R., & Urban-Lurain, M. (2020a). From substitution to redefinition: A framework of machine learning‐based science assessment. Journal of Research in Science Teaching , 57 (9), 1430–1459. https://doi.org/10.1002/tea.21658 .

Zhai, X., He, P., & Krajcik, J. (2022). Applying machine learning to automatically assess scientific models. Journal of Research in Science Teaching , 59 (10), 1765–1794. https://doi.org/10.1002/tea.21773 .

Zhai, X., Shi, L., & Nehm, R. H. (2021). A Meta-analysis of machine learning-based Science assessments: Factors impacting machine-human score agreements. Journal of Science Education and Technology , 30 (3), 361–379. https://doi.org/10.1007/s10956-020-09875-z .

Zhai, X., Yin, Y., Pellegrino, J. W., Haudek, K. C., & Shi, L. (2020b). Applying machine learning in science assessment: A systematic review. Studies in Science Education , 56 (1), 111–151. https://doi.org/10.1080/03057267.2020.1735757 .

Zulyusri, Z., Elfira, I., Lufri, L., & Santosa, T. A. (2023). Literature study: Utilization of the PjBL model in science education to improve creativity and critical thinking skills. Jurnal Penelitian Pendidikan IPA , 9 (1), 133–143. https://doi.org/10.29303/jppipa.v9i1.2555 .

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Interviewing GPTs: Can generative AI intelligence become a new source of qualitative research data?

17 Pages Posted: 28 Jun 2024

Yanqing Wang

King’s College London - King's Business School

Date Written: June 11, 2024

Purpose: This study explores the potential of generative artificial intelligence (GenAI) to generate qualitative research data. It investigates how OpenAI’s GPTs (generative pre-trained transformers) can simulate human-like responses in interviews, serving as a pilot tool for developing and refining qualitative research methodologies.

Methodology: The methodology involves using GPT models to answer semi-structured interview questions related to the impact of GenAI on risk management. Inspired by the research conducted by Leippold (2023) , this paper presents interviews with three large language models (LLMs): GPT-3.5, GPT-4.0, and GPT-4o.

Findings: The results demonstrate that GPT models can effectively simulate interview responses, providing valuable preliminary insights that can be used to refine interview questions and methodologies. GPTs demonstrate a strong ability to extract, summarise, and reason with information. However, the study also underscores the importance of ethical governance and transparency to maintain the effectiveness and trustworthiness of GenAI outputs.

Value: This research contributes to the literature by showcasing how GenAI tools such as GPT models can enhance productivity and efficiency in qualitative research. By simulating a wide range of perspectives, GPTs help to identify key themes and questions for deeper exploration in human interviews. The study offers guidelines for effectively querying information from GPTs for qualitative interview purposes, and it emphasises the need for ethical considerations when using GenAI-generated data. This innovative approach provides a foundation for further exploration of GenAI’s role in qualitative research.

JEL Classification: G00, G10, G32, C88

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Martini, Barbara, Denise Bellisario, and Paola Coletti. 2024. "Human-Centered and Sustainable Artificial Intelligence in Industry 5.0: Challenges and Perspectives" Sustainability 16, no. 13: 5448. https://doi.org/10.3390/su16135448

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• 26 June 2024

How I’m using AI tools to help universities maximize research impacts

• Dashun Wang 0

Dashun Wang is a professor at the Kellogg School of Management and McCormick School of Engineering, and the founding director of the Center for Science of Science and Innovation at Northwestern University in Evanston, Illinois.

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From the Internet to CRISPR–Cas9 gene editing, many seeds of progress were planted initially in the ivory tower of academia. Could research be doing even more for society? I argue that it could — if universities used artificial intelligence (AI) tools to maximize the impact of their scientists’ outputs.

Each year, millions of grant proposals, preprints and research papers are produced, along with patents, clinical trials and drug approvals. Massive data sets storing details of these outputs can be scoured by AI algorithms to better understand how science and technology progress and to identify gaps and bottlenecks that hinder breakthroughs. Over the past few years, my colleague and close collaborator Ben Jones, my team and I have been working with large US universities to maximize their research impacts. We’ve already learnt a lot.

Revealed: the ten research papers that policy documents cite most

For example, during our pilot project at Northwestern University in Evanston, Illinois, we worked with one of its researchers in biology. She has published hundreds of papers and acquired tens of millions of dollars in research funding. By tracing her papers and grants and how her research has been used, we discovered an intriguing fact.

The researcher had never engaged with the university’s technology transfer office (TTO), yet her research had been used extensively by private companies worldwide — many of their patents cited her work. My collaborator Alicia Löffler, then head of the TTO, talked to the researcher. It turned out that she was unaware of those market impacts. Within one week of that conversation, the researcher filed her first invention disclosure with the university.

This episode raised several questions. How many scientists are in similar positions? Can researchers with untapped innovation potential be identified? And can the obstacles that hinder technological progress be addressed? To find out, Ben, Alicia and I, and our team, have expanded studies to other universities. Our preliminary work suggests that people in such positions are common.

Has your research influenced policy? Use this free tool to check

For one, the researcher is a woman. When we compared how often male and female faculty members patented their work, we found a disparity. Male faculty members typically patented their research two to ten times more often than did their female counterparts, although this rate varied by university and discipline. But when we measured the extent to which the two groups’ scientific publications were cited by patents, we found no statistically significant difference. In other words, female scientists’ work is just as close to the technological frontier.

Numerous factors can contribute to this gender gap , such as unequal access to education and mentorship, funding disparities, prevailing norms and stereotypes and structural barriers in patenting and commercialization processes. A better understanding of these challenges would help to broaden the pool of innovators.

Similarly, we see a large difference between tenure-track and tenured faculty members: tenured researchers patent their work at a higher rate. But one doesn’t magically become more innovative the moment tenure is granted. The causes of this gap are probably distinct from those of the gender one, and might include promotion incentives and what counts towards tenure. But both discrepancies point to untapped opportunities for innovation.

Want to speed up scientific progress? First understand how science policy works

Thus, data and AI tools can help institutions to identify people and ideas that are overlooked, both in a research institution and globally. But universities must take care. They have many roles and responsibilities — from educating future leaders to advancing fundamental knowledge — that must not be eclipsed by efforts to promote practical applications. Some people might argue that scientists don’t need to commercialize their ideas themselves, because industry can pick up the ball. Or there might be unintended consequences. Emphasizing what is useful could come at the expense of curiosity-driven research or result in flocking to what seem to be the hottest and most fruitful ideas today rather than to those that will help the world most in future.

But the role of science is changing. Many of today’s issues, from pandemics to climate change, are closely linked with scientific progress. The dichotomy of basic versus applied research is becoming inadequate. For example, advances along the science–society interface, such as discoveries that aid marketable applications ( M. Ahmadpoor and B. F. Jones Science 357 , 583–587; 2017 ) or social-science insights that guide policymaking ( Y. Yin et al. Nature Hum. Behav. 6 , 1344–1350; 2022 ), are highly impactful, as evidenced by high citation rates. By engaging more with use-inspired research, scientists can produce insights that both advance basic understanding and address societal needs.

Encouraging developments are under way. In 2022, the US National Science Foundation created the Directorate for Technology, Innovation and Partnerships to support use-inspired research and translate discoveries into real-world applications. Its Assessing and Predicting Technology Outcomes programme will fund innovative projects — including our work, which we plan to expand to more than 20 universities — to understand how investments in science and technology can best accelerate progress. Other nations, university leaders and policymakers must seize this opportunity, too. I think of science as ‘the little engine that could’. If research and development could be made even 5% more efficient, the returns could be immense.

Nature 630 , 794 (2024)

doi: https://doi.org/10.1038/d41586-024-02081-6

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Artificial Intelligence (AI) in Legal Data Mining

• Aniket Deroy , Naksatra Kumar Bailung , +2 authors Abhijnan Chakraborty
• Published in arXiv.org 23 May 2024
• Law, Computer Science

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Technique improves the reasoning capabilities of large language models

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Large language models like those that power ChatGPT have shown impressive performance on tasks like drafting legal briefs, analyzing the sentiment of customer reviews, or translating documents into different languages.

These machine-learning models typically use only natural language to process information and answer queries, which can make it difficult for them to perform tasks that require numerical or symbolic reasoning.

For instance, a large language model might be able to memorize and recite a list of recent U.S. presidents and their birthdays, but that same model could fail if asked the question “Which U.S. presidents elected after 1950 were born on a Wednesday?” (The answer is Jimmy Carter.)

Researchers from MIT and elsewhere have proposed a new technique that enables large language models to solve natural language, math and data analysis, and symbolic reasoning tasks by generating programs.

Their approach, called natural language embedded programs (NLEPs), involves prompting a language model to create and execute a Python program to solve a user’s query, and then output the solution as natural language.

They found that NLEPs enabled large language models to achieve higher accuracy on a wide range of reasoning tasks. The approach is also generalizable, which means one NLEP prompt can be reused for multiple tasks.

NLEPs also improve transparency, since a user could check the program to see exactly how the model reasoned about the query and fix the program if the model gave a wrong answer.

“We want AI to perform complex reasoning in a way that is transparent and trustworthy. There is still a long way to go, but we have shown that combining the capabilities of programming and natural language in large language models is a very good potential first step toward a future where people can fully understand and trust what is going on inside their AI model,” says Hongyin Luo PhD ’22, an MIT postdoc and co-lead author of a paper on NLEPs .

Luo is joined on the paper by co-lead authors Tianhua Zhang, a graduate student at the Chinese University of Hong Kong; and Jiaxin Ge, an undergraduate at Peking University; Yoon Kim, an assistant professor in MIT’s Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); senior author James Glass, senior research scientist and head of the Spoken Language Systems Group in CSAIL; and others. The research will be presented at the Annual Conference of the North American Chapter of the Association for Computational Linguistics.

Problem-solving with programs

Many popular large language models work by predicting the next word, or token, given some natural language input. While models like GPT-4 can be used to write programs, they embed those programs within natural language, which can lead to errors in the program reasoning or results.

With NLEPs, the MIT researchers took the opposite approach. They prompt the model to generate a step-by-step program entirely in Python code, and then embed the necessary natural language inside the program.

An NLEP is a problem-solving template with four steps. First, the model calls the necessary packages, or functions, it will need to solve the task. Step two involves importing natural language representations of the knowledge the task requires (like a list of U.S. presidents’ birthdays). For step three, the model implements a function that calculates the answer. And for the final step, the model outputs the result as a line of natural language with an automatic data visualization, if needed.

“It is like a digital calculator that always gives you the correct computation result as long as the program is correct,” Luo says.

The user can easily investigate the program and fix any errors in the code directly rather than needing to rerun the entire model to troubleshoot.

The approach also offers greater efficiency than some other methods. If a user has many similar questions, they can generate one core program and then replace certain variables without needing to run the model repeatedly.

To prompt the model to generate an NLEP, the researchers give it an overall instruction to write a Python program, provide two NLEP examples (one with math and one with natural language), and one test question.

“Usually, when people do this kind of few-shot prompting, they still have to design prompts for every task. We found that we can have one prompt for many tasks because it is not a prompt that teaches LLMs to solve one problem, but a prompt that teaches LLMs to solve many problems by writing a program,” says Luo.

“Having language models reason with code unlocks many opportunities for tool use, output validation, more structured understanding into model's capabilities and way of thinking, and more,” says Leonid Karlinsky, principal scientist at the MIT-IBM Watson AI Lab.

“No magic here”

NLEPs achieved greater than 90 percent accuracy when prompting GPT-4 to solve a range of symbolic reasoning tasks, like tracking shuffled objects or playing a game of 24, as well as instruction-following and text classification tasks. The researchers found that NLEPs even exhibited 30 percent greater accuracy than task-specific prompting methods. The method also showed improvements over open-source LLMs. 

Along with boosting the accuracy of large language models, NLEPs could also improve data privacy. Since NLEP programs are run locally, sensitive user data do not need to be sent to a company like OpenAI or Google to be processed by a model.

In addition, NLEPs can enable small language models to perform better without the need to retrain a model for a certain task, which can be a costly process.

“There is no magic here. We do not have a more expensive or fancy language model. All we do is use program generation instead of natural language generation, and we can make it perform significantly better,” Luo says.

However, an NLEP relies on the program generation capability of the model, so the technique does not work as well for smaller models which have been trained on limited datasets. In the future, the researchers plan to study methods that could make smaller language models generate more effective NLEPs. In addition, they want to investigate the impact of prompt variations on NLEPs to enhance the robustness of the model’s reasoning processes.

This research was supported, in part, by the Center for Perceptual and Interactive Intelligence of Hong Kong. 

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Title: explainable ai: current status and future directions.

Abstract: Explainable Artificial Intelligence (XAI) is an emerging area of research in the field of Artificial Intelligence (AI). XAI can explain how AI obtained a particular solution (e.g., classification or object detection) and can also answer other "wh" questions. This explainability is not possible in traditional AI. Explainability is essential for critical applications, such as defense, health care, law and order, and autonomous driving vehicles, etc, where the know-how is required for trust and transparency. A number of XAI techniques so far have been purposed for such applications. This paper provides an overview of these techniques from a multimedia (i.e., text, image, audio, and video) point of view. The advantages and shortcomings of these techniques have been discussed, and pointers to some future directions have also been provided.

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Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .