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Educational Technology Research & Development - 2024 - Leading the AI transformation in schools: it starts with a digital mindset

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Educational Technology Research & Development - 2024 - Leading the AI transformation in schools: it starts with a digital mindset

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Computers & Education - 2024 - Artificial intelligence for teaching and learning in schools: The need for pedagogical intelligence.

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Computers & Education - 2024 - Artificial intelligence for teaching and learning in schools: The need for pedagogical intelligence.

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European Journal of Education - 2024 - A comprehensive exploration of artificial intelligence competence frameworks for educators: A critical review

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European Journal of Education - 2024 - A comprehensive exploration of artificial intelligence competence frameworks for educators: A critical review

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Euro J of Education - 2024 - Mikeladze - A comprehensive exploration of artificial intelligence competence frameworks for

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Annals of DAAAM & Proceedings - 2024 - IMPLEMENTATION OF AN AI-POWERED LANGUAGE ASSISTANT FOR ENHANCING ENGINEERING STUDENTS' COMMUNICATIVE PROFICIENCY THROUGH MOBILE TECHNOLOGIES. 

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Annals of DAAAM & Proceedings - 2024 - IMPLEMENTATION OF AN AI-POWERED LANGUAGE ASSISTANT FOR ENHANCING ENGINEERING STUDENTS' COMMUNICATIVE PROFICIENCY THROUGH MOBILE TECHNOLOGIES. 

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Studies in Higher Education - 2024 - From surface to deep learning approaches with Generative AI in higher education: an analytical framework of student agency

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Studies in Higher Education - 2024 - From surface to deep learning approaches with Generative AI in higher education: an analytical framework of student agency

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Injuruty: Interdiciplinary Journal and Humanity - 2023 - Role of AI in Education

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Injuruty: Interdiciplinary Journal and Humanity - 2023 - Role of AI in Education

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Education Week - 2025 -More Teachers Say They’re Using AI in Their Lessons. Here’s How

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Education Week - 2025 -More Teachers Say They’re Using AI in Their Lessons. Here’s How

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More Teachers Say They're Using AI in Their Lessons. Here's How

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Education Week - 2024 - Can AI Be Used Effectively in Class?

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Education Week - 2024 - Can AI Be Used Effectively in Class?

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Can AI Be Used Effectively in Class_ (Opinion)

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University of California - 2023 - Teachers’ attitudes towards AI: what is the difference with non-AI technologies?

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University of California - 2023 - Teachers’ attitudes towards AI: what is the difference with non-AI technologies?

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Artificial intelligence has gone through many cycles of hype, but even to skeptics, the release of ChatGPT seems to mark a turning point. OpenAI's chatbot, powered by its latest large language model, can write poems, tell jokes, and churn out essays that look like a human created them. Prompt ChatGPT with a few words, and out comes love poems in the form of Yelp reviews, or song lyrics in the style of Nick Cave.

The last time generative AI loomed this large, the breakthroughs were in computer vision. Selfies transformed into Renaissance-style portraits and prematurely aged faces filled social media feeds. Five years later, it's the leap forward in natural language processing, and the ability of large language models to riff on just about any theme, that has seized the popular imagination. And it's not just language: Generative models can also learn the grammar of software code, molecules, natural images, and a variety of other data types.

The applications for this technology are growing every day, and we're just starting to explore the possibilities. At IBM Research, we're working to help our customers use generative models to write high-quality software code faster, discover new molecules, and train trustworthy conversational chatbots grounded on enterprise data. We're even using generative AI to create synthetic data to build more robust and trustworthy AI models and to stand-in for real data protected by privacy and copyright laws.

As the field continues to evolve, we thought we'd take a step back and explain what we mean by generative AI, how we got here, and how these models work.

The rise of deep generative models

Generative AI refers to deep-learning models that can take raw data - say, all of Wikipedia or the collected works of Rembrandt - and "learn" to generate statistically probable outputs when prompted. At a high level, generative models encode a simplified representation of their training data and draw from it to create a new work that's similar, but not identical, to the original data.

Generative models have been used for years in statistics to analyze numerical data. The rise of deep learning, however, made it possible to extend them to images, speech, and other complex data types. Among the first class of models to achieve this cross-over feat were variational autoencoders, or VAEs, introduced in 2013. VAEs were the first deep-learning models to be widely used for generating realistic images and speech.

"VAEs opened the floodgates to deep generative modeling by making models easier to scale," said Akash Srivastava, an expert on generative AI at the MIT-IBM Watson AI Lab. "Much of what we think of today as generative AI started here."

Autoencoders work by encoding unlabeled data into a compressed representation, and then decoding the data back into its original form. "Plain" autoencoders were used for a variety of purposes, including reconstructing corrupted or blurry images. Variational autoencoders added the critical ability to not just reconstruct data, but to output variations on the original data.

This ability to generate novel data ignited a rapid-fire succession of new technologies, from generative adversarial networks (GANs) to diffusion models, capable of producing ever more realistic - but fake - images. In this way, VAEs set the stage for today's generative AI.

They are built out of blocks of encoders and decoders, an architecture that also underpins today's large language models. Encoders compress a dataset into a dense representation, arranging similar data points closer together in an abstract space. Decoders sample from this space to create something new while preserving the dataset's most important features.

Transformers, introduced by Google in 2017 in a landmark paper "Attention Is All You Need," combined the encoder-decoder architecture with a text-processing mechanism called attention to change how language models were trained. An encoder converts raw unannotated text into representations known as embeddings; the decoder takes these embeddings together with previous outputs of the model, and successively predicts each word in a sentence.

Through fill-in-the-blank guessing games, the encoder learns how words and sentences relate to each other, building up a powerful representation of language without anyone having to label parts of speech and other grammatical features. Transformers, in fact, can be pre-trained at the outset without a particular task in mind. Once these powerful representations are learned, the models can later be specialized - with much less data - to perform a given task.

Several innovations made this possible. Transformers processed words in a sentence all at once, allowing text to be processed in parallel, speeding up training. Earlier techniques like recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks processed words one by one. Transformers also learned the positions of words and their relationships, context that allowed them to infer meaning and disambiguate words like "it" in long sentences.

By eliminating the need to define a task upfront, transformers made it practical to pre-train language models on vast amounts of raw text, allowing them to grow dramatically in size. Previously, people gathered and labeled data to train one model on a specific task. With transformers, you could train one model on a massive amount of data and then adapt it to multiple tasks by fine-tuning it on a small amount of labeled task-specific data.

Transformers have come to be known as foundation models for their versatility. "If you wanted to improve a classifier, you used to have to feed it more labeled data," said Srivastava. "Now, with foundation models, you can feed the model large amounts of unlabeled data to learn a representation that generalizes well to many tasks."

Language transformers today are used for non-generative tasks like classification and entity extraction as well as generative tasks like translation, summarization, and question answering. More recently, transformers have stunned the world with their capacity to generate convincing dialogue, essays, and other content.

Language transformers fall into three main categories: encoder-only models, decoder-only models, and encoder-decoder models.

Encoder-only models like BERT power search engines and customer-service chatbots, including IBM's Watson Assistant. Encoder-only models are widely used for non-generative tasks like classifying customer feedback and extracting information from long documents. In a project with NASA, IBM is building an encoder-only model to mine millions of earth-science journals for new knowledge.

Decoder-only models like the GPT family of models are trained to predict the next word without an encoded representation. GPT-3, at 175 billion parameters, was the largest language model of its kind when OpenAI released it in 2020. Other massive models - Google's PaLM (540 billion parameters) and open-access BLOOM (176 billion parameters), among others, have since joined the scene.

Encoder-decoder models, like Google's Text-to-Text Transfer Transformer, or T5, combine features of both BERT and GPT-style models. They can do many of the generative tasks that decoder-only models can, but their compact size makes them faster and cheaper to tune and serve.

Generative AI and large language models have been progressing at a dizzying pace, with new models, architectures, and innovations appearing almost daily.

Supervised learning strikes back

The ability to harness unlabeled data was the key innovation that unlocked the power of generative AI. But human supervision has recently made a comeback and is now helping to drive large language models forward. AI developers are increasingly using supervised learning to shape our interactions with generative models and their powerful embedded representations.

Instruction-tuning, introduced with Google's FLAN series of models, has enabled generative models to move beyond simple tasks to assist in a more interactive, generalized way. Feeding the model instructions paired with responses on a wide range of topics can prime it to generate not just statistically probable text, but humanlike answers to questions like, "What is the capital of France?" or requests like, "Sort the following list of numbers."

By carefully engineering a set of prompts - the initial inputs fed to a foundation model - the model can be customized to perform a wide range of tasks. In some cases, no labeled data is required at all. You simply ask the model to perform a task, including those it hasn't explicitly been trained to do. This completely data-free approach is called zero-shot learning, because it requires no examples. To improve the odds the model will produce what you're looking for, you can also provide one or more examples in what's known as one- or few-shot learning.

Zero- and few-shot learning dramatically lower the time it takes to build an AI solution, since minimal data gathering is required to get a result. But as powerful as zero- and few-shot learning are, they come with a few limitations. First, many generative models are sensitive to how their instructions are formatted, which has inspired a new AI discipline known as prompt-engineering. A good instruction prompt will deliver the desired results in one or two tries, but this often comes down to placing colons and carriage returns in the right place. While effective, prompt engineering can also be fiddly. A prompt that works beautifully on one model may not transfer to other models.

Another limitation of zero- and few-shot prompting for enterprises is the difficulty of incorporating proprietary data, often a key asset. If the generative model is large, fine-tuning it on enterprise data can become prohibitively expensive. Techniques like prompt-tuning and adaptors have emerged as alternatives. They allow you to adapt the model without having to adjust its billions to trillions of parameters. They work by distilling the user's data and target task into a small number of parameters that are inserted into a frozen large model. There, they modulate the model's behavior without directly changing it.

"Parameter-efficient tuning methods allow users to have their cake and eat it too," said David Cox, IBM director of the MIT-IBM Watson AI Lab. "You can leverage the power of a large pre-trained model with your own proprietary data. Together, prompt engineering and parameter-efficient tuning provide a powerful suite of tools for getting a model to do what you want, without spending time and money on traditional deep-learning solutions."

Most recently, human supervision is shaping generative models by aligning their behavior with ours. Alignment refers to the idea that we can shape a generative model's responses so that they better align with what we want to see. Reinforcement learning from human feedback (RLHF) is an alignment method popularized by OpenAI that gives models like ChatGPT their uncannily human-like conversational abilities. In RLHF, a generative model outputs a set of candidate responses that humans rate for correctness. Through reinforcement learning, the model is adjusted to output more responses like those highly rated by humans. This style of training results in an AI system that can output what humans deem as high-quality conversational text.

Where is generative AI headed?

Until recently, a dominant trend in generative AI has been scale, with larger models trained on ever-growing datasets achieving better and better results. You can now estimate how powerful a new, larger model will be based on how previous models, whether larger in size or trained on more data, have scaled. Scaling laws allow AI researchers to make reasoned guesses about how large models will perform before investing in the massive computing resources it takes to train them.

On the flip side, there's a continued interest in the emergent capabilities that arise when a model reaches a certain size. It's not just the model's architecture that causes these skills to emerge but its scale. Examples include glimmers of logical reasoning and the ability to follow instructions. Some labs continue to train ever larger models chasing these emergent capabilities.

Recent evidence, however, is bucking the trend toward larger models. Several research groups have shown that smaller models trained on more domain-specific data can often outperform larger, general-purpose models. Researchers at Stanford, for example, trained a relatively small model, PubMedGPT 2.75B, on biomedical abstracts and found that it could answer medical questions significantly better than a generalist model the same size. Their work suggests that smaller, domain-specialized models may be the right choice when domain-specific performance is important.

"When you want specific advice, it may be better to ask a domain expert for help rather than trying to find the single smartest person you know," said Cox, at MIT-IBM. "Specialization also comes with other advantages; a smaller model is vastly cheaper and less carbon-intensive to run."

The question of whether generative models will be bigger or smaller than they are today is further muddied by the emerging trend of model distillation. A group from Stanford recently tried to "distill" the capabilities of OpenAI's large language model, GPT-3.5, into its Alpaca chatbot, built on a much smaller model. The researchers asked GPT-3.5 to generate thousands of paired instructions and responses, and through instruction-tuning, used this AI-generated data to infuse Alpaca with ChatGPT-like conversational skills. Since then, a herd of similar models with names like Vicuna and Dolly have landed on the internet.

"The Alpaca approach calls into question whether large models are essential for emergent capabilities," said Cox. "Some models, like Dolly 2, are even skipping the distillation step and instead crowdsourcing instruction-response data directly from humans. Taken together, recent events suggest we may be entering an era where more compact models are sufficient for a wide variety of practical use cases."

Generative AI holds enormous potential to create new capabilities and value for enterprise. However, it also can introduce new risks, be they legal, financial or reputational. Many generative models, including those powering ChatGPT, can spout information that sounds authoritative but isn't true (sometimes called "hallucinations") or is objectionable and biased. Generative models can also inadvertently ingest information that's personal or copyrighted in their training data and output it later, creating unique challenges for privacy and intellectual property laws.

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Everyone is talking about LLMs and generative AI these days. Here's why.

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Harvard EDCAST - 2023 - Educating in a World of Artificial Intelligence (interview with Chris Dede)

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Harvard EDCAST - 2023 - Educating in a World of Artificial Intelligence (interview with Chris Dede)

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Senior Researcher Chris Dede isn't overly worried about growing concerns over generative artificial intelligence, like ChatGPT, in education. As a longtime researcher on emerging technologies, he's seen many decades where new technologies promised to upend the field. Instead, Dede says artificial intelligence requires educators to get smarter about how they teach in order to truly take advantage of what AI has to offer."The trick about AI is that to get it, we need to change what we're educating people for because if you educate people for what AI does well, you're just preparing them to lose to AI. But if you educate them for what AI can't do, then you've got IA [Intelligence Augmentation]," he says. Dede, the associate director of research for the National AI Institute for Adult Learning and Online Education, says AI raises the bar and it has the power to significantly impact learning in powerful ways.

In this episode of the Harvard EdCast, Dede talks about how the field of education needs to evolve and get smarter, in order to work with - not against - artificial intelligence.

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TRANSCRIPT

Jill Anderson: I'm Jill Anderson. This is the Harvard EdCast.

Chris Dede thinks we need to get smarter about using artificial intelligence and education. He has spent decades exploring emerging learning technologies as a Harvard researcher. The recent explosion of generative AI, like ChatGPT, has been met with mixed reactions in education. Some public school districts have banned it. Some colleges and universities have tweaked their teaching and learning already.

Generative AI raises que

Chris Dede: I've actually been working with AI for more than half a century. Way back when when I was a graduate student, I read the first article on AI in education, which was published in 1970. And the author confidently predicted that we wouldn't need teachers within five or six years because AI was going to do everything. And of course, we still see predictions like that today.

But having lived through nine hype cycles for AI, I'm both impressed by how much it's advanced, but I'm also wary about elaborate claims for it. And there is a lot of excitement now about generative AI is the term that people are using, which includes programs like ChatGPT. It includes things like Dolly that are capable of creating images. It includes really AI on its own doing performances that we previously would have thought were something that people would have to do.

But it's interesting to compare ChatGPT to a search engine. And people don't remember this, but there was a time when-- before search engines when people really struggled to find resources, and there was enormous excitement when search engines came out. And search engines are, in fact, AI. They are based on AI at the back end, coming up with lists of things that hopefully match what you typed in. In fact, the problem with the search engine becomes not trying to find anything, but trying to filter everything to decide what's really useful.

So you can think of ChatGPT as the next step beyond a search engine where instead of getting a list of things and then you decide which might be useful and you examine them, you get an answer that says, this is what I think you want. And that is really more the AI taking charge than it is the AI saying, I can help you. Here's some things that you might look at and decide about. That makes me wary because AI is not at a stage where it really understands what it's saying.

And so it will make up things when it doesn't know them, kind of a not very good student seeing if they can fake out the teacher. And it will provide answers that are not customized to somebody's culture or to somebody's reading level or to somebody's other characteristics. So it's really quite limited.

I know that Harvard has sent some wording out that I've now put into my syllabi about students being welcome to use whatever tools they want. But when they present something as their work, it has to be something that they wrote themselves. It can't be something that somebody else wrote, which is classic plagiarism. It can't be something that Chat AI wrote that they're presenting as their work and so on. I think that what Chat AI does is it raises the bar for human performance.

I know a lot about what people are going through now in terms of job interviews because my older daughter is an HR manager, and my younger daughter just graduated. And she's having a lot of job interviews. And in contrast to earlier times, now, job interviews typically involve a performance.

If you're going to be hired for a marketing position, they'll say bring in a marketing plan when we do our face-to-face interview on this, and we'll evaluate it. Or in her case, in mechanical engineering, they say when you come in, there's this system that you're going to have a chance to debug, and we'll see how well you do it. Those employers are going to type the same thing into Chat AI. And if someone comes in with something that isn't any better than Chat AI, they're not going to get hired because why hire somebody that can't outcompete a free resource?

Jill Anderson: Oh interesting.

Chris Dede: So it raises the bar for human performance in an interesting way.

Jill Anderson: Your research looks at something called intelligence augmentation. I want to know what that means and how that's different from artificial intelligence.

Chris Dede: Intelligence augmentation is really about the opposite of this sort of negative example I was describing where now you've got to outthink Chat AI if you want to get a job. It says, when is the whole more than the sum of the parts? When do a person and AI working together do things that neither one could do as well on their own?

And often, people think, well, yeah, I can see a computer programmer, there might be intelligence augmentation because I know that machines can start to do programming. What they don't realize is that it applies to a wide range of jobs, including mine, as a college professor. So I am the associate director for research in a national AI institute funded by the National Science Foundation on adult learning and online education. And one of the things the Institute is building is AI assistants for college faculty.

So there's question answering assistants to help with student questions, and there's tutoring assistants and library assistants and laboratory assistants. There's even a social assistant that can help students in a large class meet other students who might be good learning partners. So now, as a professor, I'm potentially surrounded by all these assistants who are doing parts of my job, and I can be deskilled by that, which is a bad future. You sort of end up working for the assistant where they say, well, here's a question I can't answer.

So you have to do it. Or you can upskill because the assistant is taking over routine parts of the job. And in turn, you can focus much more deeply on personalization to individual students, on bringing in cultural dimensions and equity dimensions that AI does not understand and cannot possibly help with. The trick about AI is that to get it, we need to change what we're educating people for because if you educate people for what AI does well, you're just preparing them to lose to AI. But if you educate them for what AI can't do, then you've got IA.

Jill Anderson: So that's the goal here. We have to change the way that we're educating young people, even older people at this point. I mean, everybody needs to change the way that they're learning about these things and interacting with them.

Chris Dede: They do. And we're hampered by our system of assessment because the assessments that we use, including Harvard with the GRE and the SAT and so on, those are what AI does well. AI can score really well on psychometric tests. So we're using the wrong measure, if you will. We need to use performance assessments to measure what people can do to get into places like Harvard or higher education in general because that's emphasizing the skills that are going to be really useful for them.

Jill Anderson: You mentioned at the start artificial intelligence isn't really something brand new. This has been around for decades, but we're so slow to adapt and prepare and alter the way that we do things that once it reaches kind of the masses, we're already behind.

Chris Dede: Well, we are. And the other part of it is that we keep putting old wine in new bottles. I mean, this is - if I had to write a headline for the entire history of educational technology, it would be old wine in new bottles. But we don't understand what the new bottle really means.

So let me give you an example of something that I think generative AI could make a big difference, be very powerful, but I'm not seeing it discussed in all the hype about generative AI. And that is evidence-based modeling for local decisions. So let's take climate change.

One of the problems with climate change is that let's say that you're in Des Moines, Iowa, and you read about all this flooding in California. And you say to yourself, well, I'm not next to an ocean. I don't live in California. And I don't see why I should be that worried about this stuff.

Now, no one has done a study, I assume, of flooding in Des Moines, Iowa, in 2050 based on mid-level projections about climate change. But with generative AI, we can estimate that now.

Generative AI can reach out across topographic databases, meteorological databases, and other related databases to come up with here's the parts of Des Moines that are going to go underwater in 2050 and here's how often this is going to happen if these models are correct. That really changes the dialogue about climate change because now you're talking about wait a minute.
You mean that park I take my kids to is going to have a foot of water in it? So I think that kind of evidence-based modeling is not something that people are doing with generative AI right now, but it's perfectly feasible. And that's the new wine that we can put in the new bottle.

Jill Anderson: That's really a great way to use that. I mean, and you could even use that in your classroom. Something that you said a long, long time ago was that - and this is paraphrasing - the idea that we often implement new technology, and we make this mistake of focusing on students first rather than teachers.

Chris Dede: In December, I gave a keynote at a conference called Empowering Learners for the Age of AU that has been held the last few years. And one of the things I talked about was the shift from teaching to learning. Both are important, but teaching is ultimately sort of pouring knowledge into the minds of learners. And learning is much more open ended, and it's essential for the future because every time you need to learn something new, you can't afford to go back and have another master's degree. You need to be able to do self-directed learning.

And where AI can be helpful with this is that AI can be like an intellectual partner, even when you don't have a teacher that can help you learn in different ways. One of the things that I've been working on with a professor at the Harvard Business School is AI systems that can help you learn negotiation.

Now, the AI can't be the person you're negotiating with. AI is not good at playing human beings - not yet and not for quite a long time, I think. But what AI can do is to create a situation where a human being can play three people at once. So here you are. You're learning how to negotiate a raise.

You go into a virtual conference room. There's three virtual people who are three bosses. There's one simulation specialist behind all three, and you negotiate with them. And then at the end, the system gives you some advice on what you did well and not so well.

And if you have a human mentor, that person gives you advice as well. Ronda Bandy, who was a professor in HGSE until she moved to Hunter College, she and I have published five articles on the work we did for the HGSE's Reach Every Reader Project on using this kind of digital puppeteering to help teachers practice equitable discussion leading. So again, here's something that people aren't talking about where AI on the front end can create rich evocative situations, and AI and machine learning on the back end can find really interesting patterns for improvement.

Jill Anderson: You know, Chris, how hard is it to get there for educators?

Chris Dede: I think, in part, that's what these national AI institutes are about. Our institute, which is really adult learning with a workplace focus, is looking at that part of the spectrum. There's another institute whose focus is middle school and high school and developing AI partners for students where the student and the partner are learning together in a different kind of IA. There's a third Institute that's looking at narrative and storytelling as a powerful form of education and how can AI help with narrative and storytelling.

You can imagine sitting down. Mom and dad aren't around. You've got a storybook like Goldilocks and the Three Bears, and you've got something like Alexa that can listen to what you're reading and respond.

And so you begin, and you say, Goldilocks went out of her house one day and went into the woods and got lost. And Alexa says, why do you think Goldilocks went into the woods? Was she a naughty girl? No. Or was she an adventurous girl, or was she deeply concerned about climate change and wanting to study ecosystems?

I mean, I'm being playful about this, but I think the point is that AI doesn't understand any of the questions that it's asking but it can ask the questions, and then the child can start to think deeper than just regurgitating the story. So there's all sorts of possibilities here that we just have to think of as new wine instead of asking how can AI automate our order thinking about teaching and learning.

Jill Anderson: I've been hearing a lot of concern about writing in particular-- writing papers where young people are actually expressing their own ideas, concerns about plagiarism and cheating, which I would say the latter have long existed as challenges in education, aren't really a new one. Does AI really change this? And how might a higher ed or any educator really look at this differently?

Chris Dede: So I think where AI changes this is it helps us understand the kind of writing that we should be teaching versus the kind of writing that we are teaching. So I remember preparing my children for the SAT, and it used to have something called the essay section. And you had to write this very formal essay that was a certain number of paragraphs, and the topic sentences each had to do this and so on.

Nobody in the world writes those kinds of essays in the real world. They're just like an academic exercise. And of course, AI now can do that beautifully.

But any reporter will tell you that they could never use Chat AI to write their stories because stories is what they write. They write narratives. If you just put in a description, you'll be fired from your reportorial job because no one is interested in descriptions. They want a story.

So giving students a description and teaching them to turn it into a story or teaching them to turn it into something else that has a human and creative dimension for it, how would you write this for a seventh-grader that doesn't have much experience with the world? How would you write this for somebody in Russia building on the foundation of what AI gives you and taking it in ways that only people can? That's where writing should be going.

And of course, good writing teachers will tell you, well, that's nothing new. I've been teaching my students how to write descriptive essays. The people who are most qualified to talk about the limits of AI are the ones who teach what the AI is supposedly doing.

Jill Anderson: So do you have any helpful tips for educators regardless of what level they're working at on where to kind of begin embracing this technology?

Chris Dede: What AI can do well is what's called reckoning, which is calculative prediction. And I've given some examples of that with flooding in Des Moines and other kinds of things. And what people do is practical wisdom, if you will, and it involves culture and ethics and what it's like to be embodied and to have the biological things that are part of human nature and so on.

So when I look at what I'm teaching, I have to ask myself, how much of what I'm teaching is reckoning? So I'm preparing people to lose to AI. And how much of what I'm teaching is practical wisdom?

So for example, we spend a lot of time in vocational technical education and standard academic education teaching people to factor. How do you factor these complex polynomials?

There is no workplace anywhere in the world, even in the most primitive possible conditions, where anybody makes a living by factoring. It's an app. It's an app on a phone. Should you know a little bit about factoring so it's not magic? Sure.

Should you become fluent in factoring? Absolutely not. It's on the wrong side of the equation.
So I think just teachers and curriculum developers and assessors and stakeholders in the outcomes of education need to ask themselves, what is being taught now, and which parts of it are shifting over? And how do we include enough about those parts that AI isn't magic? But how do we change the balance of our focus to be more on the practical wisdom side?

Jill Anderson: So final thoughts here - don't be scared but figure out how to use this to your advantage?

Chris Dede: Yeah, don't be scared. AI is not smart. It really isn't. People would be appalled if they knew how little AI understands what it's telling you, especially given how much people seem to be relying on it. But it is capable of taking over parts of what you do that are routine and predictable and, in turn, freeing up the creative and the innovative and the human parts that are really the rewarding part of both work the life.

EdCast: Chris Dede is a senior research fellow at the Harvard Graduate School of Education. He is also a co-principal investigator of the National Artificial Intelligence Institute in adult learning and online education. I'm Jill Anderson. This is the Harvard EdCast produced by the Harvard Graduate School of Education. Thanks for listening.
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Educating in a World of Artificial Intelligence

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Chris Dede discusses how education can evolve to work with - rather than fight against - artificial intelligence

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Behaviour & Information Technology - 2024 - The promise and challenges of generative AI in education

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Behaviour & Information Technology - 2024 - The promise and challenges of generative AI in education

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The promise and challenges of generative AI in education

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European Journal of Investigation in Health, Psychology and Education - 2023 - Drivers and Consequences of ChatGPT Use in Higher
Education: Key Stakeholder Perspectives

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European Journal of Investigation in Health, Psychology and Education - 2023 - Drivers and Consequences of ChatGPT Use in Higher
Education: Key Stakeholder Perspectives

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Drivers and Consequences of ChatGPT Use in Higher

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J. EDUCATIONAL COMPUTING RESEARCH - 2012 - AN INVESTIGATION OF FACTORS INFLUENCING STUDENT USE OF TECHNOLOGY IN K-12 CLASSROOMS USING PATH ANALYSIS

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J. EDUCATIONAL COMPUTING RESEARCH - 2012 - AN INVESTIGATION OF FACTORS INFLUENCING STUDENT USE OF TECHNOLOGY IN K-12 CLASSROOMS USING PATH ANALYSIS

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TLRP Technology Enhanced Learning - 2011 - What is AIED and why does Education need it?

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TLRP Technology Enhanced Learning - 2011 - What is AIED and why does Education need it?

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New York Times - 2023 - Don’t Ban ChatGPT in Schools. Teach With It.

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New York Times - 2023 - Don’t Ban ChatGPT in Schools. Teach With It.

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Don’t Ban ChatGPT in Schools. Teach With It.

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OpenAI’s new chatbot is raising fears of cheating on homework, but its potential as an educational tool outweighs its risks.