← AI for Educators Daily with Dan Fitzpatrick
Loading summary
Transcript1 lines
- [00:00]
A
If this episode makes you think, please let us know in the comments and support us by subscribing and leaving a review. Thank you. Today we are exploring a really provocative piece from Forbes, an article called AI in Education Forming Citizens When AI can do the Work. It was written by Ray Ravalia, a contributor who focuses on technology and innovation in education. And what really jumped out at me from the start was this staggering insight from Leah Belsky, OpenAI's vice president of education. She points out that roughly 20% of all conversations on ChatGPT are related to education, learning or information, and a remarkable 40% of its users are under 24. That tells you young people are already using this technology on a massive scale, often without much guidance on how to use it constructively. Now, this article starts by talking about OpenAI's new ChatGPT work. And it's not just your average chatbot that answers a question or drafts an email. This is something designed to tackle bigger goals. You give it a project and it can go out, gather information from your files, your apps, the wider web, break that project down into steps, and then produce a whole coordinated set of outputs. OpenAI is imagining this being used by faculty and staff for things like revising courses, assembling accreditation materials, or managing complex projects. And yes, the immediate appeal here is absolutely efficiency. But Ray Ravaglia and Leah Belsky, in this piece, really push us to ask a much deeper question. As machines become incredibly adept at carrying out assigned work, what exactly should human beings be educated to do? This isn't a new challenge for education, but AI brings it to a head in a way we've not seen before. It forces us to reconsider the very purpose of our education systems, to think about what it means to foster AI education citizenship. The piece really makes a crucial distinction between using AI to avoid thought and using it to extend thought. Think about your classroom for a moment. If a student simply asks the AI a homework question and gets a fluent, well explained answer, they might feel like they've made progress. The answer makes sense, it's clear, it's concise. But as Belsky points out, that feeling of fluency is not the same as true mastery. It's like the analogy from the online course Learning how to Learn that Belsky recalls. The brain is like a rubber band. It has to stretch to change that struggle, to retrieve an idea, to connect concepts, to wrestle with a difficult problem. That's the work that makes learning durable. And this is where the subtle risk comes in. Students can complete an assignment, produce all the required work and still not have truly engaged their minds enough for learning to actually occur. The work disappeared quickly, the friction was gone, but so too was the productive struggle. We see this all the time, right? Give students a reading assignment with questions and many will just skim for the answers. The learning doesn't come from producing the work product, but from the process, the engagement that leads to it. This really highlights our signature phrase the real value is not in what the machine produces, but but in how the student responds. OpenAI actually introduced something last year called Study Mode specifically to try and counter this tendency. Instead of just giving an immediate answer, OpenAI study mode is designed to ask follow up questions. It probes what do you understand? Where did your reasoning break down? What approach might work next? It's a fantastic step towards building a human in the loop approach. But as the article makes clear, no product setting can solve this problem on its own. A student can always bypass it, open another chat, and just ask for the direct answer. So the burden falls back on schools and educators. We have to teach students something more fundamental how to recognize productive difficulty. How do they know when convenience crosses the line into evasion? Why isn't learning measured by how fast you finish something? This is about design and learning that cannot be faked because it demands depth, care, and imagination. It's about leveraging AI to create that cognitive stretch. Now here's a really compelling application that the article highlights. AI is a question partner for students. Imagine a classroom where every student could have a Yale Law School professor continually pressing them to clarify, defend, confront, reconsider. That kind of attention has always been incredibly scarce. But AI could make a version of that intellectual engagement available much more often to many more students. The article gives the example of professors at Harvard who flipped the case method. Instead of just reading a business case, students can interrogate a custom GPT loaded with the relevant materials. They arrive at class having already tested claims and explored alternatives. Or at Duke's Fuqua School of Business, a professor uses AI to analyze student group conversations, surfacing participation patterns and highlighting gaps in understanding. This makes the quality of the conversation visible, which can be a game changer for students who often feel frustrated by group work and want recognition for their contributions. This insight for me is absolutely crucial. The valuable resource isn't just information anymore. That's abundant is intellectual engagement. It's being pressed to clarify a claim, defend an inference, confront a contradiction, or reconsider an assumption. It's about thinking with AI, not just using tools. I hope this episode has sparked some ideas for you if you're finding these insights valuable, please do make sure you follow and subscribe so you don't miss future episodes on AI in education. But better questioning is only part of the journey. The deeper issue is, as Leah Belsky puts it, what kind of human do we want our education system to produce? She says she's often asked which skills schools should teach for the age of AI, but she believes that question starts too far downstream. The real question, the one that anchors our work in a true purpose over technology, is about the kind of person we want to cultivate. Her answer is a person who can identify a problem worth solving and who has the confidence, motivation, and the crucial AI learning agency to act on it. Think about a problem to prototype course her son attended, students start with a real problem. Then they figure out what they need to learn, who they need to work with, and what tools they need to build a response. That's a powerful reversal of traditional school logic. Usually we ask students to acquire knowledge first, and then we promise them that someday they might find a use for it. But an agency centered education, one focused on cultivating AI learning agency, starts with a purpose, and that purpose creates a demand for knowledge. In an age where knowledge acquisition is so easy with AI, this approach becomes a massive driver of motivation. It helps us teach students not to outsmart machines, but to outthink them. The article provides some fantastic examples. Students use an AI to redesign online games for blind players or using it to improve the distribution of unused cafeteria food to food banks. Schools have often confined this kind of meaningful creation to extracurriculars or special clubs. But AI allows a building and problem solving to become a much more ordinary part of everyday education. It helps us hold the complexity so we have capacity for creativity. And this brings us to the ultimate value and purpose of education citizenship. Public education has never been justified solely as job training in a republic. Schools are also expected to form citizens. Revoclia suggests that forming citizens is in an important sense forming agents, people able to examine claims, choose among competing ends, work with others, and act effectively in the world. An engaged citizen needs to do more than passively receive information. They need to ask where it comes from, what evidence supports it, and what the consequences are of acting on it. In a world of generated answers, provenance, knowing the origin becomes an essential part of agency. ChatGPT said so cannot be the end of an argument. This is at the heart of AI education citizenship. Leah Belsky connects this beautifully, describing AI enabled creation and as part of both 22nd century entrepreneurship and 22nd century citizenship these are two purposes of education, often seen as rivals, but agency links them. A person who can identify an important problem, gather evidence, persuade collaborators, and accept responsibility for a decision is equipped for both the economic and civic demands of our future. They're more than just executors of instructions. We have to help students see themselves as people who use tools, not as tools themselves. If their primary value is just carrying out a task someone else defined, then a faster, cheaper tool will always be a threat. The solution isn't to withhold the better tool, but to prepare students to operate at a much higher level. We need them to ask, what problem are we solving? Why does it matter? Which evidence should we trust? Who bears the cost? What should happen next? These are questions of judgment of purpose. They position the student as the author of purposes, not just an instrument of someone else's. Of course, agency isn't automatically virtuous. A capable person can pursue a foolish or destructive goal. A student can build something impressive without understanding the people it affects. And this is where the irreplaceable human domains come in. Judgment, wisdom, ethics, care. Education has to develop not only the power to act, but the judgment to decide what is truly worth doing. That's why citizenship gives agency its public and ethical direction. Students should be asked not just more, not if they can build something, but why it should be built, whose needs it would serve, and who would be accountable if it failed. ChatGPT work and AI tools like it can increasingly help gather evidence, organize projects and produce deliverables. They supply more of the means. Education's ultimate task is to form people capable of choosing the ends and citizens willing to answer for those choices. We need to focus on what AI cannot do. Wonder, care, judgment, relationship, imagination, wisdom, ethics. That is the core of AI education. Citizenship. For AI in education to be a net positive, it has to deliver on that promise. Ultimately, the most profound impact of AI isn't just about what it can do for us, but what it demands of us. A clear eyed redefinition of what it means to be truly human and truly educated. That's all for today. Thanks for listening.