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Unleashing the power of AI for education

Provided by Microsoft Education

Artificial intelligence (AI) is a major influence on the state of education today, and the implications are huge. AI has the potential to transform how our education system operates, heighten the competitiveness of institutions, and empower teachers and learners of all abilities.

Dan Ayoub is general manager of education at Microsoft.

The opportunities for AI to support education are so broad that recently Microsoft commissioned research on this topic from IDC  to understand where the company can help. The findings illustrate the strategic nature of AI in education and highlight the need for technologies and skills to make the promise of AI a reality.

The results showed almost universal acceptance among educators that AI is important for their future—99.4% said AI would be instrumental to their institution’s competitiveness within the next three years, with 15% calling it a “game-changer.” Nearly all are trying to work with it too—92% said they have started to experiment with the technology.

Yet on the other hand, most institutions still lack a formal data strategy or practical measures in place to advance AI capabilities, which remains a key inhibitor. The finding indicates that although the vast majority of leaders understand the need for an AI strategy, they may lack clarity on how to implement one. And it could be that they just don’t know where to start.

David Kellermann has become a pioneer in how to use AI in the classroom. At the University of New South Wales in Sydney, Australia, Kellermann has built a question bot capable of answering questions on its own or delivering video of past lectures. The bot can also flag student questions for teaching assistants (TAs) to follow up. What’s more, it keeps getting better at its job as it’s exposed to more and different questions over time.

Kellermann began his classroom’s transformation with a single Surface laptop. He’s also employed out-of-the-box systems like Microsoft Teams to foster collaboration among his students. Kellermann used the Microsoft Power Platform to build the question bot, and he’s also built a dashboard using Power BI that plots the class’s exam scores and builds personalized study packs based on students’ past performance.

Educators see AI as instrumental to their institution’s competitiveness, yet most institutions still lack a formal data strategy to advance AI.

Kellermann’s project illustrates a key principle for organizations in nearly every industry when it comes to working with AI and machine learning—knowing where to start, starting small, and adding to your capabilities over time. The potential applications of AI are so vast, even the most sophisticated organizations can become bogged down trying to do too much, too soon. Often, it comes down to simply having a small goal and building from there.  

As an AI initiative gradually grows and becomes more sophisticated, it’s also important to have access to experts who can navigate technology and put the right systems in place. To gain a foothold with AI, institutions need tools, technologies, and skills.

This is a big focus for our work at Microsoft—to support educational institutions and classrooms. We’ve seen the strides some institutions have already taken to bring the potential of AI technologies into the classroom. But we also know there is much more work to do. Over the next few years, AI’s impact will be felt in several ways—managing operations and processes, data-driven programs to increase effectiveness, saving energy with smart buildings, creating a modern campus with a secure and safe learning environment.  

But its most important and far-reaching impact may lie in AI’s potential to change the way teachers teach and students learn, helping maximize student success and prepare them for the future.  ​

Collective intelligence tools will be available to save teachers time with tasks like grading papers so teachers and TAs can spend more time with students. AI can help identify struggling students through behavioral cues and give them a nudge in the right direction.

AI can also help educators foster greater inclusivity—AI-based language translation, for example, can enable more students with diverse backgrounds to participate in a class or listen to a lecture. Syracuse University’s School of Information Studies is working to drive experiential learning for students while also helping solve real-world problems, such as Our Ability , a website that helps people with disabilities get jobs. 

AI has the power to become an equalizer in education and a key differentiator for institutions that embrace it.

Schools can even use AI to offer a truly personalized learning experience—overcoming one of the biggest limitations of our modern, one-to-many education model. Kellermann’s personalized learning system in Sydney shows that the technology is here today.

AI has the power to become a great equalizer in education and a key differentiator for institutions that embrace it. Schools that adopt AI in clever ways are going to show better student success and empower their learners to enter the work force of tomorrow.

Given its importance, institutions among that 92% should start thinking about the impact they can achieve with AI technologies now. Do you want to more quickly grade papers? Empower teachers to spend more time with students? Whatever it is, it’s important to have that goal in mind, and then maybe dream a little.

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AI for Good blog

How can artificial intelligence improve education.

• 31 May 2019

The world is changing dramatically, and Artificial Intelligence (AI) is becoming more integrated into society and work. As such, approaches to education are being reimagined.

So how can AI be leveraged to improve education? How can you create AI solutions in your community and what problems could it solve? What are the benefits and key challenges of AI education?

These are just some of the questions panelists discussed and debated during the  AI for Good Global Summit’s Education Breakthrough Sessions.  This full-day workshop hosted around 300 educators, community practitioners and AI experts to propose new solutions to reach and engage 21st-century learners and to work together to create a plan for bringing AI education into communities across the world.

Why AI education?

Panelists agreed that education needs to be a lifelong journey that everyone has the opportunity to pursue, and through which everyone can develop the skills needed to thrive in a world increasingly augmented by AI-powered technologies.

“The business-as-usual model is: ‘listen, memorize, test, repeat.’ It’s very efficiency, factory oriented. It is not the best model to support cognition,” said Ani Martinez, the Field Director of Remake Learning, and part of the  Pittsburgh AI  Community. “Aligning today’s learning with the challenges of tomorrow requires new approaches to teaching and learning. ‘Engaging, relevant and equitable’ is the new model.”

AI education helps students develop agency to make a change in tomorrow’s world, which will require the ability to identify problems, generate creative solutions and work collaboratively across multi-cultural teams.

Tara Chklovski, Founder and CEO of  Iridescent , discussed how AI education can help young people develop these skills and traits, which they will need to succeed later in life — particularly life-long learning, curiosity, and adaptability.

The challenge is often just getting started, because many people are scared of these technologies and, as a result, are not willing or curious to learn more, agreed participants.

Overcoming challenges

Ashley Patton of Carnegie Mellon University (CMU) discussed some of the lessons learned at CMU in the last 2 and a half years, as they have developed AI education programmes for the community:

• It’s not as impossible as it seems. It’s much less expensive than you think.
• Communities know what they need. They are the experts.
• We’re most successful when we’re working on what we are passionate about. You need to partner with communities that you want to serve.

Speakers highlighted that there are a range of freely available AI education platforms for children and parents online.

Stefania Druga of the Massachusetts Institute of Technology (MIT) discussed the online learning platform,  www.Cognimates.Me , an inclusive AI literacy platform for children around the world.

Developed at MIT Media Lab, the platform uses Scratch, a popular and free computer programming language, to spur fun activities for AI learning, such as programming speech assistants or robots. Teachers and parents with no background in AI or programming can use it.

Two projects spawned at the Summit

The AI Education Breakthrough track unveiled two projects that will be moving forward as a result of the work at the AI for Good Global Summit.

• The world’s largest AI education programme for families: The project is going to target 8000 children and parents and 150 educations with 150 000 hours of training, with the support of Google.org, McGovern Foundation, AIDA, University of Malta, Power to Code Spain, School of Computer Science at Carnegie Mellon University and others.
• The world’s largest AI mentoring programme: This mentoring programme will run experiential, hands-on AI mentoring to help industry professional be more open to learning AI. With the support of software company NVIDIA, Graz University of Technology in Austria and others, the programme aims to train mentors with 10 000 mentoring hours.

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Much of the public conversation about artificial intelligence in education has focused on the perils of plagiarism — students using AI tools, such as ChatGPT, to write convincing essays that they pass off as their own, and academic institutions, in turn, deploying AI tools to try to catch these cheaters. 

But, as new technologies including AI break into the mainstream, they also promise to revolutionise learning for the better. 

According to Hadi Partovi, chief executive of education non-profit Code.org, AI “is going to be one of the largest education platforms outside of school.”

Already, there are AI-powered platforms and tools emerging that can support learners — particularly those who are struggling, or from low-income backgrounds — and potentially improve equity across the board.

For example, there are already AI-powered ‘tutors’, that can offer a personalised experience to the tutee, adapting to their needs in real time. One app — Khanmigo made by the Khan Academy, a US-based non-profit online educational organisation — dubs itself “your always available study-buddy”, promising to challenge learners “to think critically and solve problems without giving you direct answers”. The app is free for teachers, and $44 a year for users and their families.

“Until now, expert, personalised tutoring has been cost-prohibitive for all but the most elite learners and institutions,” points out Joseph South, chief innovation officer at the International Society for Technology in Education, an education non-profit. “Generative AI has the potential to scale this experience for online learners at an affordable price.” 

One 2024 study, led by the University of Oxford, found that school students in Ghana who used an AI-powered maths tutor accessible via WhatsApp for half an hour, twice a week, over eight months had “substantially higher” maths growth scores than peers in a control group.

Emily DeJeu, assistant teaching professor of business management communication at Carnegie Mellon University notes that there is further research to show that low performers “get a significant boost” when using the guidance powered by AI. “It fills in their gaps, it makes them more productive,” she says.

The technology can also be used in novel ways for simulating working environments or training. “Transferring knowledge from the classroom to the workplace has always suffered from the artificial nature of a classroom setting, which lacks the complexities of real-world application, says South. “No more.” He adds that AI-generated simulations mean that students can enter workplaces “having pressure-tested their performance in simulated environments”.

As well as helping students directly, AI can be wielded by educators, themselves. It has the ability to help generate personalised learning plans, and prepare individualised assessments.

“AI transforms the educator’s role, saving significant time on lesson planning and assessment, thus allowing more classroom interaction and individualised support,” says Christophe Mallet, chief executive of Bodyswaps, a virtual reality soft skills training provider.

For example, AI can swiftly generate problem sets on a chosen topic. “It can also triage student work to identify individual errors,” adds South, “as well as patterns of performance that need to be corrected.” 

In business schools, it can generate innovative and original cases studies, too, says DeJeu.

All of this has the potential to save staff significant amounts of time. A report by educational publisher Twinkl found that adopting AI could prevent $77bn of unpaid teacher overtime across the US. That costing was based on McKinsey data suggesting teachers could regain as many as 13 hours a week by embracing AI tools.

New, up-and-coming tools include the lesson planner MagicSchool, which says it has 2mn educators signed up, and Pressto, which will generate writing prompts for teachers based on the subject topics or level they want. Merlyn Mind is another an AI-powered assistant that teachers can use in the classroom. 

However, there are still potential problems to address. AI is still unreliable and prone to hallucinations — presenting fiction as fact — which could mean students might ‘learn’ incorrect information. Similarly, AI can develop biases based on the data used to train it, and potentially pass them on to learners.

In addition, experts warn that any educational institution needs to have restrictions on what student data and personal information can be fed into AI systems, to mitigate privacy risks. 

But, to some, the biggest risk is to the learning process. DeJeu says that, as students become increasingly used to relying on technology from a young age, AI tools could mean they are “shortcutting” learning. 

“Is that good for them long term?” she asks. “Some of that is still an open question. The challenge is constantly narrating to students ways to use these tools for their net benefit, professionally, and tips to avoid using it in ways that shortcut their growth.”

DeJeu believes teachers will need to be “sufficiently tool agnostic” in introducing their students to AI, as well as in their own usage of technology. She advises them to seek out “timeless” use cases for AI because the tools and capabilities are evolving so rapidly. 

Mallet notes the risk of “blindly replacing human educators by AI for learning on the basis of cost-efficiency”. He also warns that a “rapidly widening AI skills gap among students” could emerge, as some schools and institutions are banning AI altogether over concerns about cheating, while others are embracing it. 

To ensure the correct guardrails are in place to protect students, Partovi of Code.org suggests “we need government policies — national and regional — to provide guidance to schools.” He has helped to create a consortium called TeachAI to this end. 

Nevertheless, he adds: “The biggest risk of all with AI is doing nothing. The world has changed and is changing. We should change how we teach and what we teach to embrace AI.”

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Harnessing AI's Powers For All

• Posted January 29, 2024
• By Ryan Nagelhout
• Cognitive Development
• Global Education
• Inequality and Education Gaps
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The explosion of interest and research in generative artificial intelligence (AI) has created plenty of excitement about what the technology can make possible. While large language models like ChatGPT have brought new interest to the field within the last year, researchers and educators have been exploring these technologies for more than a decade.

For the last 15 years Visiting Professor Seiji Isotani has focused much of his research on how people learn with interactive and intelligent educational technologies. He’s also studied the impacts gamification — the use of gameplay-like elements such as rewards or incentives to participate — has on learning experiences. Isotani, a professor of computer science and learning technology at the University of São Paulo, Brazil, has worked recently to develop AIED Unplugged (Artificial Intelligence in Education Unplugged), AI systems to help teachers bridge the digital divide.

“We are reaching students that don’t have access to this technology,” says Isotani, noting many don’t even have the internet. “And they still do not have access to the technology, but now they gain the benefit of the power of technology by a proxy, which is the teacher in this case. We provide all the power of this cool stuff that AI is providing to the teacher so the teacher can be better for the student.”

AIED Unplugged has designed applications that have impacted 266,000 students in Brazil, using AI software to aid teachers in evaluating student writing, leading to improvement in students’ skills. That work has since extended into countries like Mexico, Peru, and the Philippines, where Isotani and other researchers help develop policy to increase AI access in remote and low-income areas.

“In a society where everything is changing so fast, we need to be better at understanding the impact of technology and moving along with its advances. Because if we don’t do that, we are slowly going to be left behind,” Isotani says. “It’s important to understand and have the balance to use these technologies and test these technologies and improve our practices based on the evolution of knowledge.”

Below, Isotani shares the impact his research has had across the globe and what lay ahead as technology continues to provide opportunities to bridge the equity gap in education.

How would you describe the focus of your research?

I have two pillars in terms of research and impact. The first one is how we can create technologies using AI that support students the most, especially considering their needs, their skills, their knowledge, their culture, and any other factors that we can put on the table. The second one is that it doesn’t matter how personalized we can be to support students if they are not engaged, we need to support and motivate students to be engaged while they are learning.

These two topics somewhat converse and talk with each other. And so, what I’ve tried to do is ask how I can use these two topics to create or support policies that actually reach out to the most people possible.

Artificial intelligence — how to grapple with it, how best to use it — is a huge focus in education right now. How does your research into AI — which is often focused on the digital divide and issues of access — stand out?

I remember when I was in UNESCO in 2019 and we were discussing this divide. AI wasn’t this big thing that it is today, but the trend was there. My work tries to rethink how we can design AI technologies to reach underserved communities. Those that are usually left behind. The idea is if you’re a student that does not have access to the internet, or a computer, or a cell phone, can [you] receive any benefit from these AI technologies? Most people will say no.

I’ve seen so many reports from OECD, UNESCO, and all of these international organizations asking governments to invest more resources in infrastructure. I don’t believe investing more money in infrastructure is going to solve problems in education. What we need to do is stop and think: Can we be better at designing these technologies so we don’t actually need better infrastructure? We can use the current infrastructure.

With AIED Unplugged you’ve focused on using cell phones to access this tech because there’s a much higher user rate at lower income levels. But what do these AI technologies look like?

You can use generative AI to do some different tasks. In our case, we take a picture of the essays of students to improve their writing. What we’ve done is process that picture to understand where the text is, do segmentation to identify lines and words. And then the AI is able to provide analysis of this essay and give recommendations to teachers.

"How can you use this technology to help students be better learners? To have more capacity to learn and be more engaged with the learning process?"

We compile this large number of essays and the AI identifies what students are missing, what they are doing right, and gives recommendations to teachers on how they can approach students. It empowers the teacher with knowledge so they can act in class in a better way.

And I think that’s the beauty of AI: It’s being used to empower ourselves to be better in what we do. You can think about teachers, you can think about students. How can you use this technology to help students be better learners? To have more capacity to learn and be more engaged with the learning process?

How does the idea of gamification factor in?

In AI, I’m trying to adapt or personalize student pathways to learn, using gamification elements to provide nudges or incentives so they can change their behavior. We are doing work to understand the impact of these strategies on student behavior and then on student learning. Sometimes, we try to understand learning itself. Not just the behavior of learning or the process itself.

In the case of gamification, I like to quote the founder of Duolingo, Luis von Ahn. If students are not interested or engaged, it doesn’t matter if you have the best pedagogical approach. They will not learn as much as if they were engaged and motivated to learn. So before thinking about anything else, we have to motivate them.

Gamification gives us tools to engage students in different ways. Many people only look at the extrinsic motivation of students using gaming elements. But we can also touch the intrinsic motivation of students using gaming elements. Which is interesting is how we design the interactions and the way gamification will affect students.

So much of the conversation about AI is how it can be used to circumvent learning, but your work argues that it can to actually enhance learning. How do you strike that balance with new and emerging technology?

That’s an interesting question because one thing many people say to us is “My teachers do not want to use technology in the classroom.” And they are being left behind because they are overburdened with work. The problem is that changing culture is difficult. And when we are thinking of adapting technology, it’s not about the technology, it’s about the people.

We need a full cultural transformation, and that's so hard. You can be any big tech company and develop this super technology, but it won’t solve problems because the problems are in the day-to-day interactions. That’s why when we work in these huge, country-wide policies, we need to think about how not to disrupt an environment and actually use that particular space to find benefits and embed it in teachers’ lives so they do their jobs better. Then people start to adopt technologies and new strategies. With these small changes, you can be in a much better place over time.

Do you think some of the hype around AI has masked the fact that we still know very little about this technology?

I agree, we are in the hype phase. For good and for bad. The perceived risk is stretched and the perceived benefit is, too. We actually don’t know where we are going right now, and I don’t necessarily think that’s a bad thing. It’s only bad when people bet on those stretches. And I regret to say that many policies are being designed considering the perceived risks of AI in a very bad sense. Thinking that AI is going to destroy the world in so many ways, so we have to contain it in a black box and only a few people can use it and you can control everything.

I don’t believe that’s the right approach for any technology design. You need to provide nudges and pathways so companies and researchers can actually work in a free space where they can innovate and actually support their society. 

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The challenges and opportunities of Artificial Intelligence in education

Artificial Intelligence (AI) is producing new teaching and learning solutions that are currently being tested globally. These solutions require advanced infrastructures and an ecosystem of thriving innovators. How does that affect countries around the world, and especially developing nations? Should AI be a priority to tackle in order to reduce the digital and social divide?

These are some of the questions explored in a Working Paper entitled ‘ Artificial Intelligence in Education: Challenges and Opportunities for Sustainable Development ’ presented by UNESCO and ProFuturo at Mobile Learning Week 2019 . It features cases studies on how AI technology is helping education systems use data to improve educational equity and quality.

Concrete examples from countries such as China, Brazil and South Africa are examined on AI’s contribution to learning outcomes, access to education and teacher support. Case studies from countries including the United Arab Emirates, Bhutan and Chile are presented on how AI is helping with data analytics in education management.  

The Paper also explores the curriculum and standards dimension of AI, with examples from the European Union, Singapore and the Republic of Korea on how learners and teachers are preparing for an AI-saturated world.

Beyond the opportunities, the Paper also addresses the challenges and policy implications of introducing AI in education and preparing students for an AI-powered future. The challenges presented revolve around:

• Developing a comprehensive view of public policy on AI for sustainable development : The complexity of the technological conditions needed to advance in this field require the alignment of multiple factors and institutions. Public policies have to work in partnership at international and national levels to create an ecosystem of AI that serves sustainable development.
• Ensuring inclusion and equity for AI in education : The least developed countries are at risk of suffering new technological, economic and social divides with the development of AI. Some main obstacles such as basic technological infrastructure must be faced to establish the basic conditions for implementing new strategies that take advantage of AI to improve learning.
• Preparing teachers for an AI-powered education : Teachers must learn new digital skills to use AI in a pedagogical and meaningful way and AI developers must learn how teachers work and create solutions that are sustainable in real-life environments.
• Developing quality and inclusive data systems : If the world is headed towards the datafication of education, the quality of data should be the main chief concern. It´s essential to develop state capabilities to improve data collection and systematization. AI developments should be an opportunity to increase the importance of data in educational system management.
• Enhancing research on AI in education : While it can be reasonably expected that research on AI in education will increase in the coming years, it is nevertheless worth recalling the difficulties that the education sector has had in taking stock of educational research in a significant way both for practice and policy-making.
• Dealing with ethics and transparency in data collection, use and dissemination : AI opens many ethical concerns regarding access to education system, recommendations to individual students, personal data concentration, liability, impact on work, data privacy and ownership of data feeding algorithms. AI regulation will require public discussion on ethics, accountability, transparency and security.

The key discussions taking place at Mobile Learning Week 2019 address these challenges, offering the international educational community, governments and other stakeholders a unique opportunity to explore together the opportunities and threats of AI in all areas of education.

• Download the working paper

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Event International Conference of the Memory of the World Programme, incorporating the 4th Global Policy Forum 28 October 2024 - 29 October 2024

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7 Ways AI can Impact Students’ Critical Thinking Skills

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Can AI help solve education's big data problems?

Insights offered by data can help create solutions to business problems. But managing big data can overwhelm rather than help.

Kavitha Prasad, Intel

"There's so much data that's lying around in everything that we are doing, and now the amount of data that is getting generated is so much. But how do you get meaningful insights out of this data?" Kavitha Prasad, Intel's vice president and general manager of Datacenter, AI, and cloud execution and strategy, told ZDNet. s

"It's beyond humanly possible right now to sit down with the data and figure out not just what the data is saying, but the interrelationship between the different sets of data that is getting collected, and to figure out what business insights are hidden behind this data."

This issue doesn't just impact tech companies. The education sector also faces challenges in managing, protecting, and extracting value from big data.

How can schools deploy AI to improve student outcomes?

Artificial intelligence can make it possible to solve big data problems. 

For example, Intel collaborated with Aible to use AI to help Nova Southeastern University. Aible can sense, explore, and optimize data through AI. 

Prasad said the university wanted data-based insight on two primary issues: Improving undergraduate student retention and optimizing student welfare. Intel and Aible, in collaboration with Dell, deployed AI on the school's data sets. This project identified ways to potentially lower student attrition by 17% in 15 days. 

 Don Rudawsky, Nova Southeastern's vice president of institutional effectiveness, wrote in an Aible case study highlighting the project: 

During a one-hour meeting, we went from a raw dataset to exploring insights in the data automatically highlighted by Aible, to creating and even deploying a predictive model. The collaboration with academic and financial aid advisors helped us further optimize the models and made them more useful — but we went end-to-end from raw data to deployed model in such a short amount of time.

Achieving a business outcome in 15 days is "huge," Prasad added. 

The companies and the university achieved these results by running about 500 AI models across six projects. Each project took about 25 minutes to process. Using Xeon Scalable processors in tandem with Aible's technology, developers can run applications without managing servers, Intel said.

AI students optimistic about healthcare innovation at Intel Vision

Students say the tech industry should focus on AI's power to improve healthcare equity, access, and quality of life.

Intel and Aible first began working together through Intel's Disruptor Initiative. The program lets startups and established companies access Intel solutions and technologies. 

The partnership gives "that Intel muscle to these startup companies to make sure that their technologies can be leveraged broadly across the industries," Prasad said.

Aible has also worked with customers in K-12 who wanted to get insights on student retention.

"Aible helped us proactively identify students who were leaving for reasons where we could address to retain them," said Hywel Benbow, senior vice president of global data and analytics at GEMS Education. 

"When it comes to K-12, minimizing churn by itself is interesting, but Aible helped us distinguish where and how we could act to reduce churn and directly map the economic impact of taking those recommended actions."

SEE: What is an AI engineer?

Executive: AI will be in 'all realms of human life'

As AI expands, so do questions around using the technology ethically .

"AI is as good as the data is, and AI gives predictions," Prasad said. "It's still predictive." 

How to become an AI engineer

Learn more about the AI engineer career. Find out what these professionals study, where they work, and how you can prepare for this field.

And it's up to humans to conduct the risk-benefit analysis of inaccurate predictions. 

"We acknowledge that there are a lot of implications associated with these AI technologies, and we are focused to make sure that it is used for social goodness," she said. 

Prasad added that Intel follows "a continuous, rigorous process of review" for projects using AI. 

Prasad also predicts that AI soon will "impact every facet of human life."

"The proliferation of AI is only expanding, and it's very interesting to see where the businesses are trying to use techniques like AI to help achieve better business outcomes — be it in the context of education for student retention or figuring out what programs make sense from a student perspective.

"We are only at the tip of the iceberg," she continued. "It's only getting started, but eventually, it is going to expand a lot more into all realms of human life."

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Why We Shouldn't Fear AI in Education (and How to Use It Effectively) Facing resistance to new technologies in the educational process is nothing new, and AI is no exception. Yet, this powerful tool is set to overcome these challenges and revolutionize education, preparing students and professionals for a future of unparalleled efficiency and personalized learning.

By Alex Goryachev Edited by Chelsea Brown Jun 11, 2024

Key Takeaways

• AI's transformative potential in education requires proactive management and an open mind to leverage its capabilities while addressing its challenges.
• AI literacy should be viewed as a fundamental skill that must be learned. Teaching and implementing AI literacy at all levels can make the learning process exponentially more efficient and powerful.
• Successful integration of AI in education hinges on cooperation and collaboration.

Opinions expressed by Entrepreneur contributors are their own.

Much like a precocious but uncontrollable student, AI is stirring up shock waves of controversy wherever it goes, regardless of the industry, the field or the application. That's especially true when it's applied in education , where the stakes are much higher given the fundamental way we all relate to the topic. Whether it's our own education, that of our employees and children or the educational standards we choose for the community at large, we all have high standards for teachers, educators and the curriculum they use — and there's no doubt that AI is a major disruptor in this area.

The emergence of AI and its impact on the educational system needs to be proactively managed. Educators themselves acknowledge that some elements of the system we've all built together and the best practices that come with it are often outdated and anachronistic, and there's no better tool than AI when it comes to changing and improving those best practices. In many ways, that's exactly what it was designed to do.

To succeed, though, we all have to have an open mind when it comes to the capabilities, potential and possible pitfalls of AI. And an open mind just happens to be one of the fundamental tenets of outstanding education, right? AI must be viewed as a useful tool to improve the education system at all levels, and it has to be used thoughtfully and responsibly to get the best results, so let's look at one of the most pivotal keys to doing that — AI literacy.

Related: Artificial Intelligence Can be a Game-Changer for Education, Here are 5 Reasons Why

We all have a personal education technology journey

The most common thread that unites almost all of us in our journey through the education system is that we all have technology touchstones. To illustrate, I'll use my own personal "EdTech" journey, which was unusual, to say the least.

I grew up in the USSR, so my education was seriously different. Believe it or not, not only were calculators not allowed, but we actually had to use an abacus. No other choice was suggested or provided; the methodology was strictly monochromatic.

Technologically speaking, there are several useful U.S. parallels. In America, educators and parents were terrified when Texas Instruments first introduced powerful calculators to the public at large in the early 1970s.

Those concerns proved to be unfounded, of course. Calculators became incredibly useful tools that allowed talented students to advance to upper-level math skills at a much faster rate, and that speed probably helped usher in the era of the personal computer — which was just as terrifying initially.

Once educators and parents started to see PCs as potentially powerful learning tools, though, they quickly got over their fear, and computers became common. That same fear occurred when tablets and smartphones made their way into classrooms, and now it's a big part of the initial reaction to today's AI.

Many of these fears are unfounded, although there are many other concerns about critical thinking, ethics and many other topics that are definitely genuine.

Here are a couple of examples to illustrate a couple of those unfounded fears.

Using AI to foster critical thinking and creativity in education

It's easy to talk about using AI to spur critical thinking and creativity in the learning process, but talk is just that, so let's walk the walk with a couple of simple, hypothetical examples that show how educators can nurture independent thinking and problem-solving skills.

Start with a simple essay. The fact that students can easily use AI to write essays has caused all kinds of hand-wringing because it allows them to "cheat." Students will find a way to use AI to do this, but that's a loaded word with all kinds of negative implications, so let's flip the concept around.

What if students were presented with an AI-generated essay and then asked to pick it apart and improve it, or write a slightly different version where they choose a different viewpoint or perspective? Suddenly, the use of AI becomes an exercise in critical thinking, which is exactly what education is supposed to be about.

Now let's turn to the math side of the AI equation. For this example, we'll go with basic geometry, which requires spatial skills that include visualization, image manipulation and the ability to solve problems in three dimensions.

AI happens to be a brilliant tool to help with this process. It can assist students as they generate, understand and manipulate 3D images, and AI can also help them solve geometry and physics problems as students are taught how to ask questions and prompt the technology effectively. This is a fundamental skill that becomes even more vital as advanced concepts are introduced, and like that old TI calculator, it can help speed up the learning process exponentially.

Related: Don't Be Afraid Of AI — Your Fears Are Unfounded, and Here's Why

Critical thinking, complexity and AI literacy as a fundamental skill

To some extent, these two examples oversimplify the complexity that comes part and parcel with using AI as a critical thinking tool in the educational process at multiple levels, from middle school to professional development for senior executives. Using AI is inherently a multifaceted exercise — it makes mistakes, and it often comes with biases and limitations — just like people. The way people communicate with AI tools and critically evaluate the results they produce is fast becoming one of today's most important skills.

How should we become better at AI and more accepting of the results it produces? The process starts with viewing AI literacy as a fundamental skill that must be learned, and that learning demands curiosity, adaptability and a relentless approach to improvement.

It also demands that educators, teachers and learners all recognize the disruptive power of AI. Sudden change usually isn't easy, and that's especially true with AI. In many ways, we're all on the same level when it comes to learning about AI literacy as an essential skill.

Preparing for the age of AI also means multiple, fundamental changes in our current educational system as we know it, and the complexity that comes with those changes won't be a comfortable process for many of us.

The payoff, though, is potentially huge. Teaching and implementing AI literacy at all levels can make the learning process exponentially more efficient and powerful, regardless of what's being taught and what the learning goals are. AI literacy is the first step to providing education that's personalized to the needs of individuals, based on their strengths and weaknesses. This kind of customized learning experience will also increase the level of engagement and motivation exponentially.

AI literacy requires collaboration and cooperation to improve education

I often teach AI literacy workshops and provide AI keynote presentations to organizational leaders in many industries in multiple settings, and one of the concepts I typically stress the most is the need for cooperation and collaboration. In educational AI implementations, that means that all of us, from corporate leaders to educators and parents, have to understand that AI will free up our time and increase our productivity tremendously, not just add layers of complexity that require more resources and constant attention.

Developing a strategy and delivering impactful AI literacy education is the first step that most organizations need to take now. Nearly all of us need this skill now to adapt, learn and grow, and we must prepare for the opportunities and challenges that come with an AI-enabled future. The urgency of this need can't be overemphasized, and the overall message is clear: Act quickly to take control of your future and develop AI skills , or risk falling behind those who do.

Related: Why We Must Reimagine Education in the Age of Technology

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AI 101 for Teachers

Discover the groundbreaking world of artificial intelligence (AI) and its transformative potential in education with our foundational online learning series for teachers.

What is AI 101 for Teachers?

This is a free, foundational online learning series for any teacher and educator interested in the groundbreaking world of artificial intelligence (AI) and its transformative potential in education. Partners Code.org, ETS, ISTE and Khan Academy are offering engaging sessions with renowned experts that will demystify AI, explore responsible implementation, address bias, and showcase how AI-powered learning can revolutionize student outcomes. Join us on this journey of exploration and empowerment, and unlock the future of teaching with and about AI.

Available Now

Fireside Chat with Sal Khan and Hadi Partovi

Join Sal Khan (Khan Academy) and Hadi Partovi (Code.org) for a fireside chat on embracing artificial intelligence in the classroom. Expect a lively discussion including some of the most controversial topics surrounding AI today.

Demystifying AI for Educators

Introduction to the fundamentals of Artificial Intelligence which will equip you with the foundational knowledge to understand the profound impact of AI in our society and its increasing relevance to the field of education.

Companion guide: Large Language Model Prompts for Educators

Transforming Learning with AI

Explore the intersection of AI and education. This session explores how AI can be combined with pedagogy to enhance student learning. It is a must-attend for educators seeking to leverage AI to transform their teaching practices to enrich their students’ learning experiences.

Companion guide: AI Prompts for Transforming Student Learning

Ensuring a Responsible Approach to AI

Explore the ethical considerations and best practices for incorporating artificial intelligence in educational environments. This session provides concrete strategies, resources, and real-world examples, all aimed at understanding the potential biases of AI, and the ethical considerations that come with it.

Companion guide: Guide to Ensuring a Responsible Approach to AI

Bringing AI to the Classroom

This session focuses on teaching about AI, evaluating and utilizing AI educational tools, and leveraging AI for student assessment. With a rich blend of theory, practical examples, and resources, it aims to empower educators to navigate the AI landscape and enhance student learning experiences.

More ways to learn about AI

Large language model prompts for educators.

Tools for getting the best possible output from LLMs like ChatGPT, from beginner to advanced. Includes starter prompts for those just dipping their toes in, as well as advanced tools for those looking to maximize their potential.

How AI Works

Learn more about the new technology driving the large changes in AI, featuring two new videos on Large Language Models (like ChatGPT) and Generative AI. Hear how these new technologies work directly from the experts: Mira Murati, CTO of OpenAI, and Cristóbal Valenzuela, CEO of RunwayML.

AI-related resources

We have a variety of additional resources for you from Code.org, ETS, ISTE and Khan Academy to support teachers in this new and growing field. Check them out below.

AI Curricula

In Code.org’s How AI Works videos and lessons for grades 6-12, students learn basic principles about AI and use fun activities to see how computers learn from data, while reflecting on the ethical implications of AI.

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This AI-powered writing platform coaches learners through five critical phases of the writing process: Prepare/Plan, Write, Revise, Edit, Reflect, providing iterative feedback throughout the writing experience.

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Khan Academy’s AI-powered teaching aid allows teachers to stop spending time on prep tasks they don’t love, and focus on what they do love—teaching.

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More From Forbes

100% literacy: or what if ai could fix our broken educational system.

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AI in education, individualized for each student

No shortage of articles and reports describe the many problems we face daily as Americans: immigration, natural disasters, wars, racism, sexism, and shootings. It’s easy to get lost in the sheer number of crises, paralyzing to even contemplate what do to next. Worse, we can fall into the trap of negativity, overwhelmed by all that must be done to fix our problems.

However, there’s one challenge we don’t talk about enough: education. “It’s a huge problem in the U.S.,” the founder and CEO of Learning Ovations Jay Connor told my coauthor Neil Sahota and I during an interview for our upcoming book, Uber Yourself Before You Get Kodaked: A Modern Primer on A.I. for the Modern Business . “Less than 50 percent of our children are reading at grade level, and if you’re dealing with high-need or high-poverty populations, in some locations, like our schools in New York, it’s below 20 percent.”

Before we continue, let those numbers sink in for a moment. Less than 50 percent of our kids are reading at grade level — and in some places it’s as low as 20 percent. How can this be true in America, a country possessing the world’s largest economy ? According to Science Magazine , “Reading and writing are among the most important skills in today’s information world. Yet, according to the National Assessment of Progress, more than one one-third of children in the United States lack fundamental reading skills.”

Like many of today’s challenges, fixing our educational system feels insurmountable, largely because it’s often viewed as a political problem. To many, the recent tension between the left and the right over the Supreme Court nominee indicates our country’s difficulties finding non-partisan solutions to our many problems. But what if the answer to our education problem has nothing to do with politics? What if the answer is as simple as changing our mindset from “This can’t be fixed” to “This can be fixed?” And what if artificial intelligence can help us do it?

Before discussing how groundbreaking AI can change the way we educate our kids, let’s talk about how illiteracy damages lives. “Third grade is the bench mark. If you're not reading successfully by the end of third grade, you are consigned to be behind the rest of your life,” said Connor. “You’re six times more likely to be involved in the criminal justice system, to have poor health outcomes and obviously poor economic outcomes.”

I’ve personally witnessed illiteracy’s harmful effects. A few years ago, I tutored at the Los Angeles Public Library. My student was a former convict. Not much younger than my father, this man had no interest in harming anyone or committing crimes now that he was out. What he wanted was to be a truck driver so he could pay his rent and marry his girlfriend. However, to get the job he desired, he needed to pass the written driver’s exam. The problem was he couldn’t understand most of the questions, much less study for them. To make his goal a reality, he and I worked together for months sounding out words, writing them down, and building his comprehension abilities.

I am happy to report my student passed. He got the job. He got the girl and things turned out okay. But what if he didn’t? This man was one of L.A.’s working poor. Without a mechanism such as the public library to help him learn reading/writing skills, where would he be? By the time we began working together, he already had health problems, most likely exacerbated from spending most of his adulthood behind bars. If he couldn’t secure a job, if he couldn’t improve his situation, there’s every reason to believe he would revert to crime to survive — and likely wind up back in prison.

With a better understanding of the dangers illiteracy poses for our society and for our children, it’s time to look at how Learning Ovations is engaged in solving this problem through technology. Years ago, the group partnered with the Department of Education (E.D.) and the National Institutes of Health (N.I.H.) to support and scale research from observing children performing in classrooms. Its objective was to discover what kinds of instruction enables students to read at the appropriate grade level. “This allowed us to support teachers and make recommendations about the curriculum,” said Connor. “Where AI comes into play is that our approach is tremendously human-specific. We needed to have lots of human hours and vast amounts of data to make our determinations.”

For years, our country’s educational approach has been generalized with the emphasis on standardizing education for every child. What’s revolutionary about Learning Ovations’ approach is that it uses abundant data that no single human — or even a group of humans — could ever amass, much less study, to determine an individualistic approach to each student in the class. “The core starting point for us was the preceding research that pointed to the fact you need to individualize to a child’s needs to be successful,” said Connor.

A good way to understand Connor’s approach is to consider a scenario in which you have five third-graders in a classroom. One advanced kid may be reading Harry Potter , three other kids may have average reading skills, and the fifth child may have no familiarity at all with books. This child may not even know their letters, much less how to read. According to Connor, “The present classroom structure, by and large, would teach all these kids the same. What individualized instruction delivers to the child that doesn’t even know their letters is a whole set of support activities and curriculum choices that would be helpful to them, but not needed for the child reading Harry Potter .”

Interestingly, though many dystopian sci-fi fantasies portend a world in which humans shed their individualism to become slaves to robot masters, the real value of AI technology in Learning Ovations’ project is its ability to support numerous teachers so they can respond individually to students through indexing. “Indexing takes what is in a school's existing curriculum and links it to the recommended curriculum,” said Connor. “The power behind AI is its ability to determine the right amount of individualized activities, material, and instruction to get each child to read at their grade level. As we know, there’s a huge difference between the skill sets of individual children, therefore you need to respond to their individual needs.”

Of course, individualization doesn’t just begin and end with children. Every public school district’s curriculum varies from county to county and school to school. Also, every teacher within every school has their own lesson plans and teaching models. With the help of AI, that’s not a problem. AI-enhanced indexing uses algorithms to marry the idiosyncratic teaching materials of these disparate schools, districts, and teachers to create an individualized approach for each child based on their needs.

So, how does Connor know this method works? “The way we know it works is much like if you were going to test out an artificial heart implant. To do so, the FDA would insist you go through a number of randomized trials. Our work with the E.D. and N.I.H. has undergone the same level of rigor and research. More importantly, the data speaks for itself. In those same at-risk populations, the new literacy achievement rate is 94 percent!”

The research is very clear, according to Connor; knowing the right type of instruction and the amount of time for that instruction is key to individualizing general education classrooms. Nevertheless, knowing your method works and getting buy-in does not necessarily go hand-in-hand. Our toxic political landscape provides this proof daily. However, Connor is no politician and has no intention of securing political buy-ins. His organization is focused on collaborating with teachers; not to convince them this works, but to work with them to make it better.

“Just last week, I was in upstate New York and we had about 40 teachers together,” said Connor. “Now in the past, what officials tried to do was talk teachers out of using their own materials. That just creates resistance. Instead, what we can do now with AI is say, ‘Let’s just have IBM Watson look at it. Watson will tell us what the rigors are, what the standards are, what it’ll accomplish, and then you can make the choice whether you want to include it in your curriculum or make it available to other teachers across the country.’”

Connor happily related the energy level in the room couldn’t have been higher when he and his team rolled out their program to these 40 educators that day. The teachers were ecstatic. Why? At that moment, no one was thinking about politics or educational philosophies. Everyone was focused on the one thing that matters: a solution — learning what it takes to get all our kids reading.

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Artificial intelligence in education: Addressing ethical challenges in K-12 settings

Selin akgun.

Michigan State University, East Lansing, MI USA

Christine Greenhow

Associated data.

Not applicable.

Artificial intelligence (AI) is a field of study that combines the applications of machine learning, algorithm productions, and natural language processing. Applications of AI transform the tools of education. AI has a variety of educational applications, such as personalized learning platforms to promote students’ learning, automated assessment systems to aid teachers, and facial recognition systems to generate insights about learners’ behaviors. Despite the potential benefits of AI to support students’ learning experiences and teachers’ practices, the ethical and societal drawbacks of these systems are rarely fully considered in K-12 educational contexts. The ethical challenges of AI in education must be identified and introduced to teachers and students. To address these issues, this paper (1) briefly defines AI through the concepts of machine learning and algorithms; (2) introduces applications of AI in educational settings and benefits of AI systems to support students’ learning processes; (3) describes ethical challenges and dilemmas of using AI in education; and (4) addresses the teaching and understanding of AI by providing recommended instructional resources from two providers—i.e., the Massachusetts Institute of Technology’s (MIT) Media Lab and Code.org. The article aims to help practitioners reap the benefits and navigate ethical challenges of integrating AI in K-12 classrooms, while also introducing instructional resources that teachers can use to advance K-12 students’ understanding of AI and ethics.

Introduction

“Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks.” — Stephen Hawking.

We may not think about artificial intelligence (AI) on a daily basis, but it is all around us, and we have been using it for years. When we are doing a Google search, reading our emails, getting a doctor’s appointment, asking for driving directions, or getting movie and music recommendations, we are constantly using the applications of AI and its assistance in our lives. This need for assistance and our dependence on AI systems has become even more apparent during the COVID-19 pandemic. The growing impact and dominance of AI systems reveals itself in healthcare, education, communications, transportation, agriculture, and more. It is almost impossible to live in a modern society without encountering applications powered by AI  [ 10 , 32 ].

Artificial intelligence (AI) can be defined briefly as the branch of computer science that deals with the simulation of intelligent behavior in computers and their capacity to mimic, and ideally improve, human behavior [ 43 ]. AI dominates the fields of science, engineering, and technology, but also is present in education through machine-learning systems and algorithm productions [ 43 ]. For instance, AI has a variety of algorithmic applications in education, such as personalized learning systems to promote students’ learning, automated assessment systems to support teachers in evaluating what students know, and facial recognition systems to provide insights about learners’ behaviors [ 49 ]. Besides these platforms, algorithm systems are prominent in education through different social media outlets, such as social network sites, microblogging systems, and mobile applications. Social media are increasingly integrated into K-12 education [ 7 ] and subordinate learners’ activities to intelligent algorithm systems [ 17 ]. Here, we use the American term “K–12 education” to refer to students’ education in kindergarten (K) (ages 5–6) through 12th grade (ages 17–18) in the United States, which is similar to primary and secondary education or pre-college level schooling in other countries. These AI systems can increase the capacity of K-12 educational systems and support the social and cognitive development of students and teachers [ 55 , 8 ]. More specifically, applications of AI can support instruction in mixed-ability classrooms; while personalized learning systems provide students with detailed and timely feedback about their writing products, automated assessment systems support teachers by freeing them from excessive workloads [ 26 , 42 ].

Despite the benefits of AI applications for education, they pose societal and ethical drawbacks. As the famous scientist, Stephen Hawking, pointed out that weighing these risks is vital for the future of humanity. Therefore, it is critical to take action toward addressing them. The biggest risks of integrating these algorithms in K-12 contexts are: (a) perpetuating existing systemic bias and discrimination, (b) perpetuating unfairness for students from mostly disadvantaged and marginalized groups, and (c) amplifying racism, sexism, xenophobia, and other forms of injustice and inequity [ 40 ]. These algorithms do not occur in a vacuum; rather, they shape and are shaped by ever-evolving cultural, social, institutional and political forces and structures [ 33 , 34 ]. As academics, scientists, and citizens, we have a responsibility to educate teachers and students to recognize the ethical challenges and implications of algorithm use. To create a future generation where an inclusive and diverse citizenry can participate in the development of the future of AI, we need to develop opportunities for K-12 students and teachers to learn about AI via AI- and ethics-based curricula and professional development [ 2 , 58 ]

Toward this end, the existing literature provides little guidance and contains a limited number of studies that focus on supporting K-12 students and teachers’ understanding of social, cultural, and ethical implications of AI [ 2 ]. Most studies reflect university students’ engagement with ethical ideas about algorithmic bias, but few addresses how to promote students’ understanding of AI and ethics in K-12 settings. Therefore, this article: (a) synthesizes ethical issues surrounding AI in education as identified in the educational literature, (b) reflects on different approaches and curriculum materials available for teaching students about AI and ethics (i.e., featuring materials from the MIT Media Lab and Code.org), and (c) articulates future directions for research and recommendations for practitioners seeking to navigate AI and ethics in K-12 settings.

Next, we briefly define the notion of artificial intelligence (AI) and its applications through machine-learning and algorithm systems. As educational and educational technology scholars working in the United States, and at the risk of oversimplifying, we provide only a brief definition of AI below, and recognize that definitions of AI are complex, multidimensional, and contested in the literature [ 9 , 16 , 38 ]; an in-depth discussion of these complexities, however, is beyond the scope of this paper. Second, we describe in more detail five applications of AI in education, outlining their potential benefits for educators and students. Third, we describe the ethical challenges they raise by posing the question: “how and in what ways do algorithms manipulate us?” Fourth, we explain how to support students’ learning about AI and ethics through different curriculum materials and teaching practices in K-12 settings. Our goal here is to provide strategies for practitioners to reap the benefits while navigating the ethical challenges. We acknowledge that in centering this work within U.S. education, we highlight certain ethical issues that educators in other parts of the world may see as less prominent. For example, the European Union (EU) has highlighted ethical concerns and implications of AI, emphasized privacy protection, surveillance, and non-discrimination as primary areas of interest, and provided guidelines on how trustworthy AI should be [ 3 , 15 , 23 ]. Finally, we reflect on future directions for educational and other research that could support K-12 teachers and students in reaping the benefits while mitigating the drawbacks of AI in education.

Definition and applications of artificial intelligence

The pursuit of creating intelligent machines that replicate human behavior has accelerated with the realization of artificial intelligence. With the latest advancements in computer science, a proliferation of definitions and explanations of what counts as AI systems has emerged. For instance, AI has been defined as “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings” [ 49 ]. This particular definition highlights the mimicry of human behavior and consciousness. Furthermore, AI has been defined as “the combination of cognitive automation, machine learning, reasoning, hypothesis generation and analysis, natural language processing, and intentional algorithm mutation producing insights and analytics at or above human capability” [ 31 ]. This definition incorporates the different sub-fields of AI together and underlines their function while reaching at or above human capability.

Combining these definitions, artificial intelligence can be described as the technology that builds systems to think and act like humans with the ability of achieving goals . AI is mainly known through different applications and advanced computer programs, such as recommender systems (e.g., YouTube, Netflix), personal assistants (e.g., Apple’s Siri), facial recognition systems (e.g., Facebook’s face detection in photographs), and learning apps (e.g., Duolingo) [ 32 ]. To build on these programs, different sub-fields of AI have been used in a diverse range of applications. Evolutionary algorithms and machine learning are most relevant to AI in K-12 education.

Algorithms are the core elements of AI. The history of AI is closely connected to the development of sophisticated and evolutionary algorithms. An algorithm is a set of rules or instructions that is to be followed by computers in problem-solving operations to achieve an intended end goal. In essence, all computer programs are algorithms. They involve thousands of lines of codes which represent mathematical instructions that the computer follows to solve the intended problems (e.g., as computing numerical calculation, processing an image, and grammar-checking in an essay). AI algorithms are applied to fields that we might think of as essentially human behavior—such as speech and face recognition, visual perception, learning, and decision-making and learning. In that way, algorithms can provide instructions for almost any AI system and application we can conceive [ 27 ].

Machine learning

Machine learning is derived from statistical learning methods and uses data and algorithms to perform tasks which are typically performed by humans [ 43 ]. Machine learning is about making computers act or perform without being given any line-by-line step [ 29 ]. The working mechanism of machine learning is the learning model’s exposure to ample amounts of quality data [ 41 ]. Machine-learning algorithms first analyze the data to determine patterns and to build a model and then predict future values through these models. In other words, machine learning can be considered a three-step process. First, it analyzes and gathers the data, and then, it builds a model to excel for different tasks, and finally, it undertakes the action and produces the desired results successfully without human intervention [ 29 , 56 ]. The widely known AI applications such as recommender or facial recognition systems have all been made possible through the working principles of machine learning.

Benefits of AI applications in education

Personalized learning systems, automated assessments, facial recognition systems, chatbots (social media sites), and predictive analytics tools are being deployed increasingly in K-12 educational settings; they are powered by machine-learning systems and algorithms [ 29 ]. These applications of AI have shown promise to support teachers and students in various ways: (a) providing instruction in mixed-ability classrooms, (b) providing students with detailed and timely feedback on their writing products, (c) freeing teachers from the burden of possessing all knowledge and giving them more room to support their students while they are observing, discussing, and gathering information in their collaborative knowledge-building processes [ 26 , 50 ]. Below, we outline benefits of each of these educational applications in the K-12 setting before turning to a synthesis of their ethical challenges and drawbacks.

Personalized learning systems

Personalized learning systems, also known as adaptive learning platforms or intelligent tutoring systems, are one of the most common and valuable applications of AI to support students and teachers. They provide students access to different learning materials based on their individual learning needs and subjects [ 55 ]. For example, rather than practicing chemistry on a worksheet or reading a textbook, students may use an adaptive and interactive multimedia version of the course content [ 39 ]. Comparing students’ scores on researcher-developed or standardized tests, research shows that the instruction based on personalized learning systems resulted in higher test scores than traditional teacher-led instruction [ 36 ]. Microsoft’s recent report (2018) of over 2000 students and teachers from Singapore, the U.S., the UK, and Canada shows that AI supports students’ learning progressions. These platforms promise to identify gaps in students’ prior knowledge by accommodating learning tools and materials to support students’ growth. These systems generate models of learners using their knowledge and cognition; however, the existing platforms do not yet provide models for learners’ social, emotional, and motivational states [ 28 ]. Considering the shift to remote K-12 education during the COVID-19 pandemic, personalized learning systems offer a promising form of distance learning that could reshape K-12 instruction for the future [ 35 ].

Automated assessment systems

Automated assessment systems are becoming one of the most prominent and promising applications of machine learning in K-12 education [ 42 ]. These scoring algorithm systems are being developed to meet the need for scoring students’ writing, exams and assignments, and tasks usually performed by the teacher. Assessment algorithms can provide course support and management tools to lessen teachers’ workload, as well as extend their capacity and productivity. Ideally, these systems can provide levels of support to students, as their essays can be graded quickly [ 55 ]. Providers of the biggest open online courses such as Coursera and EdX have integrated automated scoring engines into their learning platforms to assess the writings of hundreds of students [ 42 ]. On the other hand, a tool called “Gradescope” has been used by over 500 universities to develop and streamline scoring and assessment [ 12 ]. By flagging the wrong answers and marking the correct ones, the tool supports instructors by eliminating their manual grading time and effort. Thus, automated assessment systems deal very differently with marking and giving feedback to essays compared to numeric assessments which analyze right or wrong answers on the test. Overall, these scoring systems have the potential to deal with the complexities of the teaching context and support students’ learning process by providing them with feedback and guidance to improve and revise their writing.

Facial recognition systems and predictive analytics

Facial recognition software is used to capture and monitor students’ facial expressions. These systems provide insights about students’ behaviors during learning processes and allow teachers to take action or intervene, which, in turn, helps teachers develop learner-centered practices and increase student’s engagement [ 55 ]. Predictive analytics algorithm systems are mainly used to identify and detect patterns about learners based on statistical analysis. For example, these analytics can be used to detect university students who are at risk of failing or not completing a course. Through these identifications, instructors can intervene and get students the help they need [ 55 ].

Social networking sites and chatbots

Social networking sites (SNSs) connect students and teachers through social media outlets. Researchers have emphasized the importance of using SNSs (such as Facebook) to expand learning opportunities beyond the classroom, monitor students’ well-being, and deepen student–teacher relations [ 5 ]. Different scholars have examined the role of social media in education, describing its impact on student and teacher learning and scholarly communication [ 6 ]. They point out that the integration of social media can foster students’ active learning, collaboration skills, and connections with communities beyond the classroom [ 6 ]. Chatbots also take place in social media outlets through different AI systems [ 21 ]. They are also known as dialogue systems or conversational agents [ 26 , 52 ]. Chatbots are helpful in terms of their ability to respond naturally with a conversational tone. For instance, a text-based chatbot system called “Pounce” was used at Georgia State University to help students through the registration and admission process, as well as financial aid and other administrative tasks [ 7 ].

In summary, applications of AI can positively impact students’ and teachers’ educational experiences and help them address instructional challenges and concerns. On the other hand, AI cannot be a substitute for human interaction [ 22 , 47 ]. Students have a wide range of learning styles and needs. Although AI can be a time-saving and cognitive aide for teachers, it is but one tool in the teachers’ toolkit. Therefore, it is critical for teachers and students to understand the limits, potential risks, and ethical drawbacks of AI applications in education if they are to reap the benefits of AI and minimize the costs [ 11 ].

Ethical concerns and potential risks of AI applications in education

The ethical challenges and risks posed by AI systems seemingly run counter to marketing efforts that present algorithms to the public as if they are objective and value-neutral tools. In essence, algorithms reflect the values of their builders who hold positions of power [ 26 ]. Whenever people create algorithms, they also create a set of data that represent society’s historical and systemic biases, which ultimately transform into algorithmic bias. Even though the bias is embedded into the algorithmic model with no explicit intention, we can see various gender and racial biases in different AI-based platforms [ 54 ].

Considering the different forms of bias and ethical challenges of AI applications in K-12 settings, we will focus on problems of privacy, surveillance, autonomy, bias, and discrimination (see Fig.  1 ). However, it is important to acknowledge that educators will have different ethical concerns and challenges depending on their students’ grade and age of development. Where strategies and resources are recommended, we indicate the age and/or grade level of student(s) they are targeting (Fig. ​ (Fig.2 2 ).

Potential ethical and societal risks of AI applications in education

Student work from the activity of “Youtube Redesign” (MIT Media Lab, AI and Ethics Curriculum, p.1, [ 45 ])

One of the biggest ethical issues surrounding the use of AI in K-12 education relates to the privacy concerns of students and teachers [ 47 , 49 , 54 ]. Privacy violations mainly occur as people expose an excessive amount of personal information in online platforms. Although existing legislation and standards exist to protect sensitive personal data, AI-based tech companies’ violations with respect to data access and security increase people’s privacy concerns [ 42 , 54 ]. To address these concerns, AI systems ask for users’ consent to access their personal data. Although consent requests are designed to be protective measures and to help alleviate privacy concerns, many individuals give their consent without knowing or considering the extent of the information (metadata) they are sharing, such as the language spoken, racial identity, biographical data, and location [ 49 ]. Such uninformed sharing in effect undermines human agency and privacy. In other words, people’s agency diminishes as AI systems reduce introspective and independent thought [ 55 ]. Relatedly, scholars have raised the ethical issue of forcing students and parents to use these algorithms as part of their education even if they explicitly agree to give up privacy [ 14 , 48 ]. They really have no choice if these systems are required by public schools.

Another ethical concern surrounding the use of AI in K-12 education is surveillance or tracking systems which gather detailed information about the actions and preferences of students and teachers. Through algorithms and machine-learning models, AI tracking systems not only necessitate monitoring of activities but also determine the future preferences and actions of their users [ 47 ]. Surveillance mechanisms can be embedded into AI’s predictive systems to foresee students’ learning performances, strengths, weaknesses, and learning patterns . For instance, research suggests that teachers who use social networking sites (SNSs) for pedagogical purposes encounter a number of problems, such as concerns in relation to boundaries of privacy, friendship authority, as well as responsibility and availability [ 5 ]. While monitoring and patrolling students’ actions might be considered part of a teacher’s responsibility and a pedagogical tool to intervene in dangerous online cases (such as cyber-bullying or exposure to sexual content), such actions can also be seen as surveillance systems which are problematic in terms of threatening students’ privacy. Monitoring and tracking students’ online conversations and actions also may limit their participation in the learning event and make them feel unsafe to take ownership for their ideas. How can students feel secure and safe, if they know that AI systems are used for surveilling and policing their thoughts and actions? [ 49 ].

Problems also emerge when surveillance systems trigger issues related to autonomy, more specifically, the person’s ability to act on her or his own interest and values. Predictive systems which are powered by algorithms jeopardize students and teachers’ autonomy and their ability to govern their own life [ 46 , 47 ]. Use of algorithms to make predictions about individuals’ actions based on their information raise questions about fairness and self-freedom [ 19 ]. Therefore, the risks of predictive analysis also include the perpetuation of existing bias and prejudices of social discrimination and stratification [ 42 ].

Finally, bias and discrimination are critical concerns in debates of AI ethics in K-12 education [ 6 ]. In AI platforms, the existing power structures and biases are embedded into machine-learning models [ 6 ]. Gender bias is one of the most apparent forms of this problem, as the bias is revealed when students in language learning courses use AI to translate between a gender-specific language and one that is less-so. For example, while Google Translate translated the Turkish equivalent of “S he/he is a nurse ” into the feminine form, it also translated the Turkish equivalent of “ She/he is a doctor ” into the masculine form [ 33 ]. This shows how AI models in language translation carry the societal biases and gender-specific stereotypes in the data [ 40 ]. Similarly, a number of problematic cases of racial bias are also associated with AI’s facial recognition systems. Research shows that facial recognition software has improperly misidentified a number of African American and Latino American people as convicted felons [ 42 ].

Additionally, biased decision-making algorithms reveal themselves throughout AI applications in K-12 education: personalized learning, automated assessment, SNSs, and predictive systems in education. Although the main promise of machine-learning models is increased accuracy and objectivity, current incidents have revealed the contrary. For instance, England’s A-level and GCSE secondary level examinations were cancelled due to the pandemic in the summer of 2020 [ 1 , 57 ]. An alternative assessment method was implemented to determine the qualification grades of students. The grade standardization algorithm was produced by the regulator Ofqual. With the assessment of Ofqual’s algorithm based on schools' previous examination results, thousands of students were shocked to receive unexpectedly low grades. Although a full discussion of the incident is beyond the scope of this article [ 51 ] it revealed how the score distribution favored students who attended private or independent schools, while students from underrepresented groups were hit hardest. Unfortunately, automated assessment algorithms have the potential to reconstruct unfair and inconsistent results by disrupting student’s final scores and future careers [ 53 ].

Teaching and understanding AI and ethics in educational settings

These ethical concerns suggest an urgent need to introduce students and teachers to the ethical challenges surrounding AI applications in K-12 education and how to navigate them. To meet this need, different research groups and nonprofit organizations offer a number of open-access resources based on AI and ethics. They provide instructional materials for students and teachers, such as lesson plans and hands-on activities, and professional learning materials for educators, such as open virtual learning sessions. Below, we describe and evaluate three resources: “AI and Ethics” curriculum and “AI and Data Privacy” workshop from the Massachusetts Institute of Technology (MIT) Media Lab as well as Code.org’s “AI and Oceans” activity. For readers who seek to investigate additional approaches and resources for K-12 level AI and ethics interaction, see: (a) The Chinese University of Hong Kong (CUHK)’s AI for the Future Project (AI4Future) [ 18 ]; (b) IBM’s Educator’s AI Classroom Kit [ 30 ], Google’s Teachable Machine [ 25 ], UK-based nonprofit organization Apps for Good [ 4 ], and Machine Learning for Kids [ 37 ].

"AI and Ethics Curriulum" for middle school students by MIT Media Lab

The MIT Media Lab team offers an open-access curriculum on AI and ethics for middle school students and teachers. Through a series of lesson plans and hand-on activities, teachers are guided to support students’ learning of the technical terminology of AI systems as well as the ethical and societal implications of AI [ 2 ]. The curriculum includes various lessons tied to learning objectives. One of the main learning goals is to introduce students to basic components of AI through algorithms, datasets, and supervised machine-learning systems all while underlining the problem of algorithmic bias [ 45 ]. For instance, in the activity “ AI Bingo” , students are given bingo cards with various AI systems, such as online search engine, customer service bot, and weather app. Students work with their partners collaboratively on these AI systems. In their AI Bingo chart, students try to identify what prediction the selected AI system makes and what dataset it uses. In that way, they become more familiar with the notions of dataset and prediction in the context of AI systems [ 45 ].

In the second investigation, “Algorithms as Opinions” , students think about algorithms as recipes, which are created by set of instructions that modify an input to produce an output [ 45 ]. Initially, students are asked to write an algorithm to make the “ best” jelly sandwich and peanut butter. They explore what it means to be “ best” and see how their opinions of best in their recipes are reflected in their algorithms. In this way, students are able to figure out that algorithms can have various motives and goals. Following this activity, students work on the “Ethical Matrix” , building on the idea of the algorithms as opinions [ 45 ]. During this investigation, students first refer back to their developed algorithms through their “best” jelly sandwich and peanut butter. They discuss what counts as the “best” sandwich for themselves (most healthy, practical, delicious, etc.). Then, through their ethical matrix (chart), students identify different stakeholders (such as their parents, teacher, or doctor) who care about their peanut butter and jelly sandwich algorithm. In this way, the values and opinions of those stakeholders also are embedded in the algorithm. Students fill out an ethical matrix and look for where those values conflict or overlap with each other. This matrix is a great tool for students to recognize different stakeholders in a system or society and how they are able to build and utilize the values of the algorithms in an ethical matrix.

The final investigation which teaches about the biased nature of algorithms is “Learning and Algorithmic Bias” [ 45 ]. During the investigation, students think further about the concept of classification. Using Google’s Teachable Machine tool [ 2 ], students explore the supervised machine-learning systems. Students train a cat–dog classifier using two different datasets. While the first dataset reflects the cats as the over-represented group, the second dataset indicates the equal and diverse representation between dogs and cats [ 2 ]. Using these datasets, students compare the accuracy between the classifiers and then discuss which dataset and outcome are fairer. This activity leads students into a discussion about the occurrence of bias in facial recognition algorithms and systems [ 2 ].

In the rest of the curriculum, similar to the AI Bingo investigation, students work with their partners to determine the various forms of AI systems in the YouTube platform (such as its recommender algorithm and advertisement matching algorithm). Through the investigation of “ YouTube Redesign”, students redesign YouTube’s recommender system. They first identify stakeholders and their values in the system, and then use an ethical matrix to reflect on the goals of their YouTube’s recommendation algorithm [ 45 ]. Finally, through the activity of “YouTube Socratic Seminar” , students read an abridged version of Wall Street Journal article by participating in a Socratic seminar. The article was edited to shorten the text and to provide more accessible language for middle school students. They discuss which stakeholders were most influential or significant in proposing changes in the YouTube Kids app and whether or not technologies like auto play should ever exist. During their discussion, students engage with the questions of: “Which stakeholder is making the most change or has the most power?”, “Have you ever seen an inappropriate piece of content on YouTube? What did you do?” [ 45 ].

Overall, the MIT Media Lab’s AI and Ethics curriculum is a high quality, open-access resource with which teachers can introduce middle school students to the risks and ethical implications of AI systems. The investigations described above involve students in collaborative, critical thinking activities that force them to wrestle with issues of bias and discrimination in AI, as well as surveillance and autonomy through the predictive systems and algorithmic bias.

“AI and Data Privacy” workshop series for K-9 students by MIT Media Lab

Another quality resource from the MIT Media Lab’s Personal Robots Group is a workshop series designed to teach students (between the ages 7 and 14) about data privacy and introduce them to designing and prototyping data privacy features. The group has made the content, materials, worksheets, and activities of the workshop series into an open-access online document, freely available to teachers [ 44 ].

The first workshop in the series is “ Mystery YouTube Viewer: A lesson on Data Privacy” . During the workshop, students engage with the question of what privacy and data mean [ 44 ]. They observe YouTube’s home page from the perspective of a mystery user. Using the clues from the videos, students make predictions about what the characters in the videos might look like or where they might live. In a way, students imitate YouTube algorithms’ prediction mode about the characters. Engaging with these questions and observations, students think further about why privacy and boundaries are important and how each algorithm will interpret us differently based on who creates the algorithm itself.

The second workshop in the series is “ Designing ads with transparency: A creative workshop” . Through this workshop, students are able to think further about the meaning, aim, and impact of advertising and the role of advertisements in our lives [ 44 ]. Students collaboratively create an advertisement using an everyday object. The objective is to make the advertisement as “transparent” as possible. To do that, students learn about notions of malware and adware, as well as the components of YouTube advertisements (such as sponsored labels, logos, news sections, etc.). By the end of the workshop, students design their ads as a poster, and they share with their peers.

The final workshop in MIT’s AI and data privacy series is “ Designing Privacy in Social Media Platforms”. This workshop is designed to teach students about YouTube, design, civics, and data privacy [ 44 ]. During the workshop, students create their own designs to solve one of the biggest challenges of the digital era: problems associated with online consent. The workshop allows students to learn more about the privacy laws and how they impact youth in terms of media consumption. Students consider YouTube within the lenses of the Children’s Online Privacy Protections Rule (COPPA). In this way, students reflect on one of the components of the legislation: how might students get parental permission (or verifiable consent)?

Such workshop resources seem promising in helping educate students and teachers about the ethical challenges of AI in education. Specifically, social media such as YouTube are widely used as a teaching and learning tool within K-12 classrooms and beyond them, in students’ everyday lives. These workshop resources may facilitate teachers’ and students’ knowledge of data privacy issues and support them in thinking further about how to protect privacy online. Moreover, educators seeking to implement such resources should consider engaging students in the larger question: who should own one’s data? Teaching students the underlying reasons for laws and facilitating debate on the extent to which they are just or not could help get at this question.

Investigation of “AI for Oceans” by Code.org

A third recommended resource for K-12 educators trying to navigate the ethical challenges of AI with their students comes from Code.org, a nonprofit organization focused on expanding students’ participation in computer science. Sponsored by Microsoft, Facebook, Amazon, Google, and other tech companies, Code.org aims to provide opportunities for K-12 students to learn about AI and machine-learning systems [ 20 ]. To support students (grades 3–12) in learning about AI, algorithms, machine learning, and bias, the organization offers an activity called “ AI for Oceans ”, where students are able to train their machine-learning models.

The activity is provided as an open-access tutorial for teachers to help their students explore how to train, model and classify data , as well as to understand how human bias plays a role in machine-learning systems. During the activity, students first classify the objects as either “fish” or “not fish” in an attempt to remove trash from the ocean. Then, they expand their training data set by including other sea creatures that belong underwater. Throughout the activity, students are also able to watch and interact with a number of visuals and video tutorials. With the support of their teachers, they discuss machine learning, steps and influences of training data, as well as the formation and risks of biased data [ 20 ].

Future directions for research and teaching on AI and ethics

In this paper, we provided an overview of the possibilities and potential ethical and societal risks of AI integration in education. To help address these risks, we highlighted several instructional strategies and resources for practitioners seeking to integrate AI applications in K-12 education and/or instruct students about the ethical issues they pose. These instructional materials have the potential to help students and teachers reap the powerful benefits of AI while navigating ethical challenges especially related to privacy concerns and bias. Existing research on AI in education provides insight on supporting students’ understanding and use of AI [ 2 , 13 ]; however, research on how to develop K-12 teachers’ instructional practices regarding AI and ethics is still in its infancy.

Moreover, current resources, as demonstrated above, mainly address privacy and bias-related ethical and societal concerns of AI. Conducting more exploratory and critical research on teachers’ and students’ surveillance and autonomy concerns will be important to designing future resources. In addition, curriculum developers and workshop designers might consider centering culturally relevant and responsive pedagogies (by focusing on students’ funds of knowledge, family background, and cultural experiences) while creating instructional materials that address surveillance, privacy, autonomy, and bias. In such student-centered learning environments, students voice their own cultural and contextual experiences while trying to critique and disrupt existing power structures and cultivate their social awareness [ 24 , 36 ].

Finally, as scholars in teacher education and educational technology, we believe that educating future generations of diverse citizens to participate in the ethical use and development of AI will require more professional development for K-12 teachers (both pre-service and in-service). For instance, through sustained professional learning sessions, teachers could engage with suggested curriculum resources and teaching strategies as well as build a community of practice where they can share and critically reflect on their experiences with other teachers. Further research on such reflective teaching practices and students’ sense-making processes in relation to AI and ethics lessons will be essential to developing curriculum materials and pedagogies relevant to a broad base of educators and students.

This work was supported by the Graduate School at Michigan State University, College of Education Summer Research Fellowship.

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The authors declare that they have no conflict of interest.

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Contributor Information

Selin Akgun, Email: ude.usm@lesnugka .

Christine Greenhow, Email: ude.usm@wohneerg .

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AI in Education: The 14 Uses and 8 Cons of Artificial Intelligence in Education

Introduction

Since their launches, OpenAI GPT and Google Bard have created a spark in the tech industry. This is all because of the capabilities of these AI language models. We have seen how artificial intelligence (AI) is helping many businesses do better. AI in learning and education is one of them. There are many ways in which AI could polish classroom practice. Education systems that are powered by artificial intelligence can give each student a personalized learning experience by using complex algorithms and machine learning. By adapting to how they learn and giving them immediate feedback and guidance. AI can also make it easier to streamline administrative tasks, give students more learning opportunities, and help with research and development in the academic world. Like many fields, what are the disadvantages of AI in education?

Even though the future of AI in learning and education looks good, some disadvantages of AI in education need to be addressed. To make sure AI systems are used ethically and responsibly, several things need to be looked at. There are worries about data privacy and security, the possibility that AI will reinforce biases and keep inequalities going, the risk that people will rely too much on AI at the expense of human interaction and creativity, and the need for ongoing oversight and regulation.

Part 1: Enhancing Teaching

A. personalized learning through ai algorithms.

Personalized learning experiences for students are one of the most exciting uses of AI in the classroom. Artificial intelligence (AI) powered systems can analyze student data, such as learning styles, strengths, and weaknesses, to personalize instructional materials and activities. This could make it easier for students to learn and help close the gap between those who do well and those who don’t.

EdTech Magazine found that 82% of teachers think that AI can help students learn by making it more personalized, and 74% think that AI can help teachers figure out what each student needs and give them help that fits their needs.

B. Intelligent tutoring systems (ITS) for students

The term “ intelligent tutoring system” (ITS) refers to software run by artificial intelligence that can help students in a personalized, one-on-one way. Systems like these can help students understand hard ideas by responding immediately to their questions and inquiries and pointing them toward relevant supporting materials. ITS can modify itself to suit the needs of each student, giving them the best chance of succeeding.

In a study by the U.S. Department of Education, the performance of students who got help from an ITS was compared to that of their classmates who only got instruction in the classroom.

C. AI-powered grading and assessments

Artificial intelligence can also be used to grade student work, which would lighten the load on teachers and allow for faster feedback cycles. AI-powered grading can be more objective and consistent than human grading and can also eliminate bias.

EdWeek Research Center found that 51% of K-12 educators see artificial intelligence as helpful for enhancing assessment and grading.

D. Supporting teachers in lesson planning and delivery

In the same way, AI can help teachers in the classroom by assisting them in planning and teaching lessons. Artificial intelligence (AI) powered systems can analyze student data to recommend instructional materials and activities and provide feedback to teachers on how effective those materials and activities were. Teachers will be able to devote more time to teaching when administrative tasks like taking attendance and grading are handled automatically by AI.

E. Challenges and Disadvantages of AI in Education:

While AI has the potential to enhance teaching in several ways, several challenges and disadvantages need to be addressed. These include:

• AI can reinforce biases and perpetuate inequalities : The degree to which an AI system is objective depends on the quality of the training data it is given. Because AI systems can be just as biased as the data used to train them, this could lead to even more biased outcomes. As a result, existing educational and other disparities may widen.
• Overreliance on AI could lead to reduced human interaction and connection : AI systems will only partially replace the connections and interactions between people, which are essential for teaching and learning to work well. Artificial intelligence (AI) has the potential to replace human interaction and innovation in the classroom, two fundamental elements of an excellent education that its overuse could jeopardise.
• High costs associated with implementing and maintaining AI systems in education : Artificial intelligence (AI) systems can be expensive to set up and keep running. This is because they require a lot of money on technology and infrastructure. Especially in financially strapped school districts, this can be a major hurdle to implementation.
• Limited understanding and training of educators to effectively utilize AI : Many teachers may need more knowledge and experience to use AI effectively. Because of this, AI-powered systems may not be used to their full potential, reducing their positive impact on education.

To get the most out of AI in learning and education, dealing with these problems and drawbacks will be important.

Part 2: Improving Student Learning

A. adapting to different learning styles through ai.

Adjusting to students’ individual preferences for how they learn is one of AI’s most promising applications in academia. AI can analyse data collected from students to reveal each student’s unique learning style and areas of strength and improvement. This allows AI-driven systems to deliver individualised lesson plans, which can improve student learning outcomes.

B. Providing timely feedback and remediation

Artificial intelligence can help students by giving them immediate feedback on their performance. Students benefit from this kind of feedback—which can be both encouraging and critical—because it keeps them thinking actively and critically. Another way that AI-driven systems are helping students succeed is by providing individualised remediation to help them work on their areas of weakness.

The Journal found that 90% of students agreed or strongly agreed that getting personalized feedback from AI systems would improve their learning.

C. Analyzing student data to identify knowledge gaps

AI can analyse student data to reveal areas of ignorance, allowing teachers to assist those struggling students better. Artificial intelligence (AI)-powered systems can aid educators in identifying students’ areas of weakness by analysing data from assessments, homework, and other sources.

D. Encouraging student engagement and motivation

AI can inspire students to learn by creating more interesting and interactive lessons. For example, AI-powered systems that use games and simulations can keep students interested and motivated. The instantaneous responses provided by AI can also increase students’ motivation to learn.

AI Munch’s recent survey found that 74% of teachers agree that AI has the potential to get students more interested in learning.

AI has the potential to help students learn in many ways, but it also has some problems that need to be fixed. These include:

• AI may fail to capture certain nuances and aspects of student learning that only human teachers can detect : Although AI-driven systems can analyse student data, they may need to learn more subtleties and nuances of learning that human educators can pick up. For instance, AI might not understand a student’s tone of voice or body language, which can reveal important information about their mood and motivation.
• Concerns about data privacy and security when handling student data : Many student data, including personal and sensitive information, must be available to AI-powered systems. Because of this, and in light of recent data breaches and cyberattacks, people are understandably worried about their personal information and its safety.
• The potential for overreliance on AI could lead to students lacking creativity and critical thinking skills : Students must think creatively and critically to gain the 21st-century skills necessary for success. While AI can be helpful, students must keep their critical and creative thinking skills sharp.

To make the most of the potential advantages of AI in learning and education and help students learn better, it will be important to deal with these problems.

Part 3: Revolutionizing the Education System

A. streamlining administrative tasks with ai.

Automating routine administrative tasks is a major advantage of AI in the classroom. For example, artificial intelligence (AI) systems can help automate tasks like keeping track of student records, making schedules, and communicating so that teachers don’t have to do as much work. If teachers don’t have to spend as much time on administrative tasks, they can spend more time teaching and helping students in the classroom.

McKinsey & Company found that AI-enabled systems could automate up to 60% of teachers’ workload.

B. Enhancing accessibility to education through AI

AI in learning and education can make it easier for all students to learn, even those who have trouble with traditional learning materials. Remote learning opportunities and individualized course materials are just two ways in which AI-driven systems are helping students with special needs. In addition, AI can provide translation and support for languages, facilitating communication across borders.

C. AI-powered research and development

Artificial intelligence (AI) can potentially improve educational research and development by shedding light on student learning and revealing ways teachers’ practices can be enhanced. Researchers can use the ability of AI-powered systems to sort through huge datasets to find new ways of teaching.

MarketsandMarkets predicts that between 2020 and 2025, the global market for AI in education will grow at a compound annual growth rate (CAGR) of 38.1%, reaching a value of $3.7 billion in the last year of that period.

D. Addressing equity issues through AI

Artificial intelligence can help solve educational equity problems by pointing out and fixing differences in how well students do in school and how they get to school. Artificial intelligence (AI)-powered systems can analyse demographic and student achievement data to identify subsets of students at risk of falling behind and provide educators with insights into how to intervene best to prevent this.

Artificial intelligence can change the education system in many ways, but some problems must be fixed. These include:

• The potential for AI to replace jobs in the education industry : As AI-powered systems become more common in the classroom, there is concern that some jobs, like those in administration, could be taken over by machines. For those with lower levels of education and skills, this could have serious consequences for employment in the education sector.
• The possibility of AI exacerbating existing socioeconomic disparities if not implemented equitably: AI can make social and economic differences worse if it isn’t used fairly. One way that inequality in education could be kept going is if only a few people could use AI-powered systems.
• The need for ongoing oversight and regulation to ensure that AI systems are used ethically and responsibly : AI-based systems must be constantly watched and regulated to make sure they are used in a responsible and ethical way. This means that AI systems should be open, fair, and honest and only be used to help and improve human judgment.

The game-changing potential of AI in classrooms must be fully realized. At the same time, it is important to reduce the risks and bad effects as much as possible.

There are risks to artificial intelligence because it changes society in mysterious ways . But this does not mean we should stop implementing it in our lives. When artificial intelligence (AI) is used in education, it could change how people teach and learn. AI can make education more accessible, help students learn better, and make research and development easier in the education sector by making learning more personalized and administrative tasks easier. Still, AI in education comes with many challenges and drawbacks, such as the chance that people will rely too much on AI and that socioeconomic differences will worsen.

You need a balanced integration strategy to get the most out of AI in education while minimizing risks. This means figuring out how to deal with possible problems and drawbacks, like ensuring AI is used ethically and responsibly and doesn’t replace human interaction and creativity.

AI in education will probably keep changing the way we teach and learn in the future. Emerging trends like using AI chatbots to help students and making virtual learning environments powered by AI could greatly affect the education industry.

In conclusion, AI has the potential to make a big difference in education, but it needs to be added in carefully and with thought. By addressing potential obstacles and disadvantages, we can ensure that the positive effects of AI in education are maximized while the negative effects are minimized.

FAQs AI in Education

There are some possible disadvantages to implementing AI in education. These include the risk of reinforcing biases and keeping inequalities in place, the chance that relying too much on AI will reduce human interaction and connection, the high costs of setting up and maintaining AI systems in education, and the fact that most teachers don’t know how to use AI well and haven’t been trained to do so.

One of the biggest problems with AI in education is that it could make people less creative and less able to interact with each other. There is also the risk of reinforcing biases and perpetuating inequalities and the opportunity of high implementation and maintenance costs associated with AI systems in education.

Some possible bad effects of AI are that it could reinforce biases and keep inequalities going, replace human jobs, cause privacy and security problems when handling data, and make people less social and creative if they rely too much on it.

AI’s future in education is likely to involve the development of more advanced technologies that can improve personalized learning, make administrative tasks easier, and help research and development in the education sector. New trends include using AI chatbots to help students and creating virtual learning environments that AI powers.

Intelligent tutoring systems (ITS) are one way AI is used in education. These systems give each student a personalized learning experience based on their needs and learning styles. Another example is the use of tools for grading and testing that are powered by AI and can give students more accurate and timely feedback.

AI for students means using AI technologies to improve teaching and learning in the education sector. This can be done with the help of AI-powered tools like smart tutoring systems, personalized learning algorithms, and tools for grading and testing.

Some of the disadvantages of technology in education include the potential for distractions and decreased attention spans, the risk of overreliance on technology resulting in diminished critical thinking and problem-solving skills, the possibility of widening socioeconomic disparities, the risk of reduced social interaction and communication skills, and the possibility of data privacy and security concerns.

AI can help fix problems in education by making learning more personalized, giving students more accurate and timely feedback, making administrative tasks easier, and making research and development in the education sector easier. However, it is important to take a balanced approach to integrate AI into education and address potential challenges and drawbacks, such as ensuring ethical and responsible use of AI technologies.

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PREPARING STUDENTS FOR AN AI-DRIVEN FUTURE

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As the school year begins, their thinking has evolved.

By Natasha Singer

I sat in on a ChatGPT workshop this month for teachers at Walla Walla High School, about 270 miles southeast of Seattle. As a reporter who covers education technology, I have closely followed how generative artificial intelligence has upended education .

Now that the first full school year of the A.I. chatbot era is beginning, I wanted to ask administrators and educators how their thinking had evolved since last spring. Walla Walla, a district that serves some 5,500 students, seemed like a timely location to begin the conversation. After blocking student access to ChatGPT in February, Walla Walla administrators told me they unblocked it last month and are now embracing A.I. tools.

So I jumped at the chance to learn more about how teachers there are planning to use chatbots with their students this academic year. You can read more in my story today about how school districts across the country are repealing their ChatGPT bans.

My colleague Kevin Roose has some great suggestions in his column today on how schools can survive, “and maybe even thrive,” with A.I. tools this fall. Step one, Kevin says: “Assume all students are going to use the technology.”

We recently asked educators, professors, and high school and college students to tell us about their experiences using A.I. chatbots for teaching and learning. We got a massive response — more than 350 submissions. Here are some highlights:

Teaching with A.I.

I love A.I. chatbots! I use them to make variations on quiz questions. I have them check my instructions for clarity. I have them brainstorm activity and assignment ideas. I’ve tried using them to evaluate student essays, but it isn’t great at that.

— Katy Pearce, associate professor, University of Washington

Before they even use ChatGPT, I help students discern what is worth knowing, figuring out how to look it up, and what information or research is worth “outsourcing” to A.I. I also teach students how to think critically about the data collected from the chatbot — what might be missing, what can be improved and how they can expand the “conversation” to get richer feedback.

— Nicole Haddad, Southern Methodist University

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Watch Reimagine Education and learn what's new with responsible AI in education >

• AI in education
• Published Jun 17, 2024

Get started with free AI tools from Microsoft Education

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Developments in AI are happening quickly, with many exciting new products and features launching. Wondering where to get started with AI tools in education? We’ve got you covered! We’ve compiled a list of free AI tools and offers to kickstart your AI journey.

AI offers opportunities to reshape how we approach creativity, problem-solving, learning, and communication in a time where these skills are more important than ever. It can help educators grow and focus on what matters most and help prepare students for a future where AI will play an increasingly important role in many careers. It’s also important to teach students how to use AI tools in a safe and responsible way, making sure they understand the benefits and potential impacts.

In addition to leveraging new products below, we recommend first building a strong foundation for understanding AI in education with our free training courses and resources. Take the courses in the AI for educators learning path to understand essential AI concepts and applications for your classroom. Use the Classroom Toolkit: Unlock generative AI safely and responsibly to help students gain skills like fact-checking, cultivating good digital hygiene habits, safeguarding their data privacy, and adopting strategies to manage their mental wellbeing effectively.

Take a step toward a more informed and inclusive future with AI. Let curiosity be your guide and explore these free AI tools from Microsoft.

1. Microsoft Copilot

Learn all about Microsoft Copilot, your AI assistant for education. Copilot offers AI-powered web chat , using the latest AI models, with sources and commercial data protection. It’s available to education staff, faculty, and higher education students aged 18+ at no additional cost. Get an overview of how to use Copilot, as well as ideas and inspiration for how you can use Copilot to save time, differentiate instruction, and enhance student learning.

• Read Meet your AI assistant for education: Microsoft Copilot to get started.
• Save or print this  quick guide to Microsoft Copilot  to refer back to as needed.

2. GitHub Copilot

GitHub Education brings tech and open-source collaboration to students and educators worldwide. To empower the next generation, GitHub Copilot is free for all verified educators and students aged 13 and above. GitHub Copilot suggests code and entire functions in real-time from your editor, allowing you and your students to focus on solving bigger problems and building better software.

With GitHub Copilot Chat, students and teachers can not only get code suggestions, but also ask questions, get explanations, offer prompts for code, and more, all in the integrated development environment (IDE) while staying in the flow.

• Visit GitHub Education to start your journey.
• Read more about GitHub Copilot Chat for students and teachers .

3. Learning Accelerators

Build supportive classrooms with Microsoft 365

Learning Accelerators can help students catch up, keep up, and get ahead. With these powerful tools included in Microsoft 365 Education, students can start practicing right away, and you can start gathering data to help them build their skills.

• Use Reading Coach to provide personalized, engaging, consistent, and independent reading fluency practice. It allows learners to create unique AI-generated stories by choosing characters and settings from a curated collection of options. Reading Coach is available for free as a Windows application and a web app to use in the classroom or at home with a Microsoft account.
• Leverage Search Progress and Search Coach, two information literacy tools, to empower students to seek, evaluate, and use online sources responsibly—a critical step in helping them navigate AI-generated content and the wider information ecosystem with confidence.
• Visit Learning Accelerators: Tools for Students to get started with these powerful tools in your classroom.

4. Microsoft Teams for Education

New AI-powered features in Microsoft Teams for Education, a free communication app for schools, are available to all educators for no additional cost. They leverage AI to draft content like rubrics, assignment instructions, personalized reading passages, and learning objectives, all while keeping the educator in control.  

• In Assignments, get multiple suggestions for how to enrich your content, including creating rubrics and assignment instructions. After the content is created, you can make edits and generate different options. 
• Use Reading Progress, a powerful reading fluency tool, to track students’ reading skills, get actionable insights quickly, and focus students on specific areas for improvement.  New enhancements in the tool —AI passage generation and AI comprehension questions generation—can help you save time and personalize learning.
• Visit Microsoft Teams for Schools and Students to download Teams and get started.

5. Minecraft Education AI literacy solutions

Build AI literacy and digital citizenship with Minecraft Education’s learning content and teaching resources. In fun, immersive worlds, students explore how AI can help us solve real-world problems, demystify the risks and opportunities of AI, and build confidence through game-based learning. Use Minecraft’s lesson planning resources to spark classroom engagement and inspire AI learning.

• Teach standards-aligned STEM lessons that build AI literacy like Hour of Code: Generation AI, AI for Earth, and Hour of Code: AI for Good.
• Use the Minecraft Prompt Lab for educators to learn how to use Copilot with sample prompts to create engaging game-based learning lessons and assessment materials.
• Visit aka.ms/MinecraftAI to get started!

6. Khanmigo for Teachers

Khanmigo for Teachers, a pilot AI-powered teaching assistant, is free for all US educators due to our new partnership Khan Academy . Khanmigo streamlines class prep and gives teachers more time and energy to spend with students. As your planning ally and instructional collaborator, Khanmigo leverages Khan Academy content to simplify AI for teachers. No prompting is required. Khanmigo will help create engaging lesson hooks, provide insights on student performance, recommended assignments, and support for refreshing your knowledge.

• Read the article Enhancing the future of education with Khan Academy to learn more and get started today.
• Save or print this  quick guide to Khanmigo for Teachers  to refer back to as needed.

AI tools are quickly transforming the way schools foster creativity, approach challenges, and enhance learning. We’re here to help you find the tools and resources to feel inspired and level up your teaching skills. Get started with these free AI tools from Microsoft and seamlessly integrate AI into your teaching and learning.

Related Posts

Explore insights from the AI in Education Report  

Meet your AI assistant for education: Microsoft Copilot  

Five quick prompting tips to get more from your AI assistant  

Ai in education brings opportunity to life.

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AI in Education: Big Opportunities, Big Problems

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Conversations about the implications of artificial intelligence are everywhere. They are in education, health care, law enforcement, the military, and the music and art worlds. AI’s potential to transform society is both promising and scary. Now, the emergence of ChatGPT this school year and related concerns about students using it to plagiarize assignments has put AI front and center in schools. Some districts have taken a hardline approach and banned the technology in classroom. Others are encouraging their students to learn how to use it in smart, ethical ways. How can schools put AI to use effectively but avoid the problems it poses, such as rampant cheating and breakdowns in student data privacy? Find out what the experts and your colleagues have to say.

First time K-12 Essentials Forum attendee? Learn more about our free virtual forums .

Welcome and Introduction

Interactive Session: What Do You Know About AI in Education?

Industry Perspective: Advances in AI — Opportunities and Challenges in Education

Presentation: How to Make AI a Teaching and Learning Partner

Industry Perspective: Redefining Rigor: Embracing Chat GPT in the Classroom

Panel Discussion: Are You AI Literate?  See What It Takes

Interactive Session: How AI Should Be Used in K-12 Education

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AI Can Help You Ask Better Questions — and Solve Bigger Problems

• Hal Gregersen
• Nicola Morini Bianzino

Leaders should focus less on automation and more on innovation.

Most companies still view AI rather narrowly, as a tool that alleviates the costs and inefficiencies of repetitive human labor and increasing organizations’ capacity to produce, process, and analyze piles and piles of data. But when paired with “soft” inquiry-related skills it can help people ask better questions and be more innovative.

There are two distinct, yet related, paths to do this. 1) Use the technology to change the cadence and patterns of their questions: AI increases question velocity, question variety, and question novelty. 2) Use AI to transform the conditions and settings where people work so that questions that spark change — what we call “catalytic” questions — can emerge. This pushes leaders out of their comfort zones and into the position of being intellectually wrong, emotionally uncomfortable, and behaviorally quiet and more reflective, all of which, it turns out, promotes innovative thinking and action.

Just a few years ago, businesses wrestled with artificial intelligence mainly in the abstract — a “future of work” problem they’d have to contend with down the line. Now? More than half the companies around the world are actively adopting AI. Although investments are particularly high in industries such as health care, data management and processing, cloud computing, and fintech, all types of organizations and functions have incorporated AI technology into their operations. And generative AI tools such as ChatGPT are forcing leaders to ask where and how AI can help their businesses.

• Hal Gregersen is a Senior Lecturer in Leadership and Innovation at the MIT Sloan School of Management , a globally recognized expert in navigating rapid change, and a Thinkers50 ranked management thinker. He is the author of Questions Are the Answer: A Breakthrough Approach to Your Most Vexing Problems at Work and in Life and the coauthor of The Innovator’s DNA: Mastering the Five Skills of Disruptive Innovators .
• NB Nicola Morini Bianzino is EY Global Chief Technology Officer, focused on bringing technology products to EY clients, positioning technology at the heart of the organization, advising global clients on technology investment and their innovation agendas, and providing industrialized technology products to meet their most pressing business needs. An early AI pioneer, he wrote a thesis on the application of neural networks to business in 1997. He holds a master’s degree in Artificial Intelligence and Economics from the University of Florence.

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AI accelerates problem-solving in complex scenarios

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While Santa Claus may have a magical sleigh and nine plucky reindeer to help him deliver presents, for companies like FedEx, the optimization problem of efficiently routing holiday packages is so complicated that they often employ specialized software to find a solution.

This software, called a mixed-integer linear programming (MILP) solver, splits a massive optimization problem into smaller pieces and uses generic algorithms to try and find the best solution. However, the solver could take hours — or even days — to arrive at a solution.

The process is so onerous that a company often must stop the software partway through, accepting a solution that is not ideal but the best that could be generated in a set amount of time.

Researchers from MIT and ETH Zurich used machine learning to speed things up.

They identified a key intermediate step in MILP solvers that has so many potential solutions it takes an enormous amount of time to unravel, which slows the entire process. The researchers employed a filtering technique to simplify this step, then used machine learning to find the optimal solution for a specific type of problem.

Their data-driven approach enables a company to use its own data to tailor a general-purpose MILP solver to the problem at hand.

This new technique sped up MILP solvers between 30 and 70 percent, without any drop in accuracy. One could use this method to obtain an optimal solution more quickly or, for especially complex problems, a better solution in a tractable amount of time.

This approach could be used wherever MILP solvers are employed, such as by ride-hailing services, electric grid operators, vaccination distributors, or any entity faced with a thorny resource-allocation problem.

“Sometimes, in a field like optimization, it is very common for folks to think of solutions as either purely machine learning or purely classical. I am a firm believer that we want to get the best of both worlds, and this is a really strong instantiation of that hybrid approach,” says senior author Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering (CEE), and a member of a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS).

Wu wrote the paper with co-lead authors Sirui Li, an IDSS graduate student, and Wenbin Ouyang, a CEE graduate student; as well as Max Paulus, a graduate student at ETH Zurich. The research will be presented at the Conference on Neural Information Processing Systems.

Tough to solve

MILP problems have an exponential number of potential solutions. For instance, say a traveling salesperson wants to find the shortest path to visit several cities and then return to their city of origin. If there are many cities which could be visited in any order, the number of potential solutions might be greater than the number of atoms in the universe.  

“These problems are called NP-hard, which means it is very unlikely there is an efficient algorithm to solve them. When the problem is big enough, we can only hope to achieve some suboptimal performance,” Wu explains.

An MILP solver employs an array of techniques and practical tricks that can achieve reasonable solutions in a tractable amount of time.

A typical solver uses a divide-and-conquer approach, first splitting the space of potential solutions into smaller pieces with a technique called branching. Then, the solver employs a technique called cutting to tighten up these smaller pieces so they can be searched faster.

Cutting uses a set of rules that tighten the search space without removing any feasible solutions. These rules are generated by a few dozen algorithms, known as separators, that have been created for different kinds of MILP problems. 

Wu and her team found that the process of identifying the ideal combination of separator algorithms to use is, in itself, a problem with an exponential number of solutions.

“Separator management is a core part of every solver, but this is an underappreciated aspect of the problem space. One of the contributions of this work is identifying the problem of separator management as a machine learning task to begin with,” she says.

Shrinking the solution space

She and her collaborators devised a filtering mechanism that reduces this separator search space from more than 130,000 potential combinations to around 20 options. This filtering mechanism draws on the principle of diminishing marginal returns, which says that the most benefit would come from a small set of algorithms, and adding additional algorithms won’t bring much extra improvement.

Then they use a machine-learning model to pick the best combination of algorithms from among the 20 remaining options.

This model is trained with a dataset specific to the user’s optimization problem, so it learns to choose algorithms that best suit the user’s particular task. Since a company like FedEx has solved routing problems many times before, using real data gleaned from past experience should lead to better solutions than starting from scratch each time.

The model’s iterative learning process, known as contextual bandits, a form of reinforcement learning, involves picking a potential solution, getting feedback on how good it was, and then trying again to find a better solution.

This data-driven approach accelerated MILP solvers between 30 and 70 percent without any drop in accuracy. Moreover, the speedup was similar when they applied it to a simpler, open-source solver and a more powerful, commercial solver.

In the future, Wu and her collaborators want to apply this approach to even more complex MILP problems, where gathering labeled data to train the model could be especially challenging. Perhaps they can train the model on a smaller dataset and then tweak it to tackle a much larger optimization problem, she says. The researchers are also interested in interpreting the learned model to better understand the effectiveness of different separator algorithms.

This research is supported, in part, by Mathworks, the National Science Foundation (NSF), the MIT Amazon Science Hub, and MIT’s Research Support Committee.

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How AI tools are being used in classrooms

Christopher Intagliata

Ailsa Chang

AI tools in classrooms

When Chat GPT came out a year and a half ago, school districts rushed to block the tool amid fears students would use it to cheat. Now, many districts are embracing AI more broadly.

Copyright © 2024 NPR. All rights reserved. Visit our website terms of use and permissions pages at www.npr.org for further information.

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What problems can artificial intelligence help us solve.

The information age has reached a turning point. See how AI is being used to solve the world’s problems and register for our upcoming webinar to learn more.

• What can AI do?
• AI and ML Working together

Transforming Industries

Solving global problems.

The information age has reached a turning point, with data constantly being produced by devices, and sensors as the Internet of Things continues to grow. Yet, unless we interpret it, that data remains just what it is – data. In order to turn this data into actionable insights, we need the help of artificial intelligence and its problem-solving abilities.

What Can AI Do?

Whereas humans have the potential to make a lot of errors, AI has the benefit of being supported by machines, which have much superior computation abilities than humans, such as the ability to sift through enormous amounts of data and use that data to make better decisions. Simply put, AI has the ability to do the heavy thinking for us. What exactly does that heavy thinking comprise of?

• Find trends, patterns, and associations
• Discover inefficiencies
• Execute plans
• Learn and become better
• Predict future outcomes based on historical trends
• Inform fact-based decisions

In addition to AI, machine learning (ML) is a technological subset of AI that allows computers to adjust when exposed to new data – basically, learning without being explicitly programmed. ML is equivocal to data mining in that databases are examined by humans to produce new insight and information. Yet, ML is able to provide an unbiased analysis of the data.

AI and ML Working Together

Each industry features a combination of interconnecting inputs and variables. Analyzing complex data to understand meaningful value can often overwhelm human analysts, keeping us from finding adequate solutions in a timely manner.

Many tech companies are in the midst of developing AI solutions, allowing the companies to:

• Improve and automate complicated analytical tasks
• Study data in real-time, adjusting its behavior with minor needs for supervision
• Increase accuracy and efficiency

AI will eventually be found in every industry on the planet. Here are a few industries AI will transform:

Cybersecurity

AI can be applied to cybersecurity in a preventative and predictable way. For instance, AI prediction technology can be used to study millions of files and attacks to understand what exactly makes them up. By comprehending mathematical DNA, companies can prevent future attacks.

Reducing Energy Costs

In the energy sector, companies can utilize AI to sort through vast datasets to predict and adapt to specific scenarios. There are several ways they can reduce operational costs and proactively mitigate issues, such as increasing automation, decreasing downtime, optimizing asset management, identifying efficiencies, and improving operational performance.

Essentially, anything that’s data-driven – like analyzing MRI scans and detecting early forms of disease or cancer – can benefit hugely from machine learning. One of the biggest benefits of AI is its ability to sift through massive amounts of data in record time, helping researchers pinpoint areas of focus for their own research. For instance, a recent discovery on Amyotrophic Lateral Sclerosis (ALS), was discovered thanks to a partnership between Barrow Neurological Institute and the artificial intelligence company IBM Watson Health.

The artificial intelligence computer (known as IBM Watson) reviewed thousands of research pieces to identify new genes that are linked with ALS. Another promising use of AI when it comes to healthcare is its ability to predict the outcome of drug treatments. For example, cancer patients are often given the same drug, then monitored to see how effective that drug is. Using AI, scientists could predict which patients benefit from using a particular drug with data, saving time, money, and providing a highly customized approach.

On the patient side of things, an AI-driven healthcare system could lessen some of the burdens on a system that is struggling to keep up with ever-growing and evolving demand. Being able to access this technology will allow patients to make better health decisions, diagnose disease and other health risks earlier, avoid expensive procedures, and ultimately, live longer.

Consumer Goods and Services

From a Google search to self-driving cars, AI essentially encompasses everything. For instance, well-known streaming platform Netflix gives users what they want by collecting a vast amount of consumer data, letting them know what shows you watch, when you watch them, when you pause, rewind, and more, seeing everything in real time.

An example of the decisions Netflix makes based on the big data from their worldwide 30 million subscribers includes their programming. After running the numbers through their AI technology, they determined people like the British version of House of Cards, David Fincher movies, and films featuring Kevin Spacey. They made a decision based on this information, buying House of Cards as a result.

AI technology is being used to look at financial model to garner greater levels of predicting future pricing patterns, achieving greater levels of trend analysis, identifying new markets, and assessing supply chain risks.

Government and Environment

Synthesizing and disseminating inputs rapidly can help alert governments to make better decisions on crucial social issues, the environment and economy, all in real time. They can place sensors on everything from mountains to streetlights, and by applying AI to that data, governments can accomplish things like building more livable cities, preventing crime and terrorist attacks, reducing poverty, and understanding climate change.

Another area that can use big data to gain insights into conflicts before they occur is the military. AI programs can utilize satellite photo interpretation capabilities in order to identify potential targets and threats. Further, by analyzing speech patterns in communications, AI can look for certain phrases and words that may point to terrorist activity, then respond efficiently to lessen the situation before it escalates.

Procurement

Procurement departments have been using spend analytics software to utilize big data to the fullest. AI software could help the procurement industry overcome huge challenges, such as risk analysis of suppliers, monitoring exchange rates, comparing prices of suppliers, managing supply chain risks, and finding the best value without compromising quality. Some of the benefits of AI is wrapped up in the fact that companies could garner huge savings if buying decisions are accelerated.

Clearly, no matter the industry, the potential for AI is great. Rule-based analysis and machine learning can find ways to create efficiency, reduce costs, and optimize working environments. As more and more parts of our lives and work generate huge amounts of data, it stands to reason that eventually, AI will contextualize the data and extract meaningful insights, allowing companies to make more informed decisions and improving their bottom line.

Machine learning and AI also have the potential to extend outside industries, providing help that can strengthen industries and economies overall. See the following examples from SciPol :

1.       Making driving safer

Though self-driving cars are still a few years away from being fully safe to drive, this area of AI could dramatically decrease the rates of deaths and injuries on the roads. According to a report drafted by Stanford University , self-driving cars have the ability to reduce traffic-related deaths and injuries. The change will also bring about shifts in our lifestyle, with passengers using the time freed up from driving to giving us more time to get work done on our commutes or entertain ourselves. We also may have more of a choice where we work from thanks to self-driving cars, with the study reporting that the increased comfort and decreased cognitive load may affect where people choose to live.

2.       Transforming how we learn

In 2016, the Georgia Tech News Center reported that an artificial intelligence course created an AI teaching assistant. At the end of the course, students enrolled in Georgia Tech’s online master’s of science in computer science program found out that their teaching assistant was in fact a virtual assistant. And her work was necessary – with roughly 300 students enrolled and posting 10,000 messages in the online forums, it was too much for the professor and his eight human TAs to handle.

After a few initial hiccups, the robot started answering the students’ questions with 97% certainty. The robot was designed by the university after their research revealed that one of the main factors behind students dropping out was a lack of support. The use of this type of robot is revolutionary for universities. People learn differently, at different starting points and at different speeds. Thanks to AI, students could learn in a more personalized way. And since most education systems can’t afford to tutor every child, AI can be deployed in a useful way.

3.       Help us become more energy efficient

AI could help us be more efficient with our energy consumption; and in some parts of the world, this is already happening. Tech giant Google has an enormous data center that requires a massive amount of energy to run the servers and keep them cool. To combat this effect, Google has deployed its AI platform Deep Mind to predict when its data centers will get too hot. As a result, cooling systems are only activated when required, saving Google around forty percent at its server farms.

4.       Helping wildlife

Wildlife transformation can be transformed thanks to the analysis of massive amounts of data. An example of this comes with tracking animal movements, which allows researchers to see where they go and as a result, which habits need to be protected. For instance, this Montana-based study pinpoints the best places to create wildlife corridors – continuous areas of protected land that link zones of biological significance that animals can use to move safely through the wilderness – for wolverines and grizzly bears.

Stefanini’s AI Insights

At Stefanini, we deploy AI-based solutions every day and look to AI and ML to lead us into the future. In fact, we are in the midst of nearshoring talent in LATAM to leverage already-existing talent that is often overlooked.

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Four experts show how AI solves real-world problems

Through deep learning techniques, McKinsey experts identified a feature – ink blots – that can be used to detect fraudulent signatures.

September 10, 2018 What better place to learn about the newest advances in artificial intelligence (AI) than at a place once called the ‘Tomorrow Lab’?

McKinsey recently hosted an educational AI forum for press and colleagues in our Waltham, Massachusetts, office, which coincidentally started out in 1998 as a ‘Tomorrow Lab’.

Following an initial stint as an accelerator for internet start-ups in the late 90s, the Waltham office became one of McKinsey’s major hubs for research and analytics . Here, some 350 data scientists, mathematicians, and developers (many of whom are doctors in their fields) are working across more than 40 different areas of expertise at any one time.

At the forum, the talk was of data lakes, random forests, and “boiling the ocean.” Nature metaphors aside, it was a chance to experience our newest techniques for analyzing and modeling data to help our clients solve some of their most intractable problems.

“The thinking in AI has changed from ‘What’s possible?’ to ‘How do I do this?’” explains Rafiq Ajani , the partner who leads the North American analytics group. “Quickly selecting the right algorithms and models from the many open-source options—and precisely customizing them for the use case—is where we are now. It’s not about ‘What’s the right answer?’ but ‘Am I asking the right questions?’—with more investment on the data side.”

The general session was followed by a whirlwind tour of demos from our experts.

Up first was Luke Gerdes , who joined McKinsey two years ago, after working for years as a civilian researcher and professor at West Point, developing analytics to help counter terrorist networks and insurgents.

Today, he focuses on natural language processing, a branch of artificial intelligence that uses analytics to derive insights from unstructured text—that is, text that is not organized into a structured table but appears in written documents.

These could be legal contracts, consumer complaints, social media—and, in one example, conversation logs between pilots and towers.

“Almost 90 percent of all data is unstructured,” he explains. “We are just at the tip of the iceberg in developing potential applications.” What’s different now is that there are application programming interfaces (APIs), or new ways to access a vast variety of data across applications, and tools have advanced to allow more nuanced, granular, and accurate analysis of text.

Luke recounts helping a company that had come through multiple acquisitions and inherited numerous sets of product data. They were using the same part number for vastly different parts and buying the same materials for different prices from different suppliers. “Their goal was simple: pay the lowest price point for the same part. We helped them rationalize their product portfolio by applying machine learning to analyze textual product descriptions—it wouldn’t have worked with SKUs or bar codes,” he said.

In another example, a Southern US state was able to slice and dice reams of student data in a myriad of ways to tease out the socioeconomic factors that influenced test scores. They then could advise their school districts on the qualitative factors they had control over, such as teacher-student ratio, that could improve performance.

Following next was Jack Zhang , who has been with the analytics group from its humble beginnings of Excel sheets and traditional statistical models. Today, he leads our work on AI-enabled feature discovery (AFD), which he describes as “boiling the ocean” to find the insights.

AFD is about testing every possible variation on a set of information to understand outcomes, such as why customers cancel a service or patients make certain choices.

“It’s a statistical-modeling concept that’s been around for years—but automation has changed everything,” he says. We can test every possible variation of immense data sets—hundreds of millions of variations—in a fraction of the time, and it highlights pockets of features we can dive into for new insights.”

He walked through an AFD model designed for a wireless company that was struggling with a stubborn 20 percent annual customer-churn rate. The team helped them construct an airtight 360-degree view of millions of customers and then ran the algorithm—more than 300 features emerged that signaled when a customer was about to cancel their contract, such as moving into a specific zip code. The company could address most of them with three overarching preemptive measures, representing $100 million in possible incremental revenue. The model is being adapted across more than ten industries including media, banking, and pharmaceuticals.

Adrija Roy , a geospatial expert, demoed our OMNI solution. It combines geospatial data (transit hubs, foot traffic, demographics) with customer psychographics (shopping history) and machine-learning techniques. Businesses are using OMNI to understand the economic value of each of their locations in the context of all of their channels. It’s guiding decisions on optimizing location networks, including opening and closing outlets and designing individual store experiences.

The final demo was about deep learning, presented by Vishnu Kamalnath , an electrical engineer and computer scientist, who did early work training humanoid robots.

In deep learning (DL), algorithms ingest huge sets of unstructured data, including text, audio, and video, and process them through multiple layers of neural networks, often producing insights humans or less complex models could not grasp. DL algorithms can detect underlying emotion in audio text, are used in facial recognition, and can track small, fast-moving objects in satellite imagery, such as fake license plates in traffic.

In a recent example, working with a bank to reduce fraud, the team developed an algorithm that surfaced and analyzed a key feature of fraudulent signatures—tiny blots created when a nervous, often inexperienced perpetrator faked a signature. They used the algorithm to test all of the existing signatures on record, garnering a 97 percent rate of accuracy in detecting fraudulent ones.

“People used to think of deep learning as a really expensive proposition, requiring complex hardware,” says Vishnu, “But the cost is dropping; tools are becoming commoditized. Don’t shy away from it as an esoteric solution.” Deep-learning applications are estimated to be $3.5 trillion to $5.8 trillion in value annually across 19 industries.

While technologies are advancing at a healthy pace, one challenge remains unchanged: getting right kind of data for the right use case at the right time. “The data component takes 80 percent of the time to gather, clean, and run in any project,” says Jack Zhang. “We still live by the maxim ‘garbage in, garbage out’.”

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