← AI for Educators Daily with Dan Fitzpatrick
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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 compelling opinion piece from the Chicago Tribune titled AI Rollout begs the Question what is Education for the author Eunice Emre Tozel dives into the University of Chicago's decision to roll out Claude Enterprise, an artificial intelligence tool developed by Anthropic, to all its students, faculty and staff. And he unpacks the critical questions this raises, particularly concerning the very purpose of education. What really grabbed my attention here is the underlying concern that a university charging students over $90,000 a year might be unwittingly hollowing out the very intellectual work it's meant to foster. Now, Tozel's piece starts by referencing University of Chicago President Paul Alevisatos announcement about this new institutional wide access to Claude Enterprise. The language in the President's letter, according to the article, carefully suggests these are powerful tools that are being made available to help individuals find ways to improve our pursuit of knowledge. Sounds good on the surface, right? But then Tyler Austin Harper, a staff writer at the Atlantic and a former professor, really pushed back on this on X, asking for a Steel man version of the rationale behind this give everyone AI university strategy. He was essentially asking, what's the theory of the case here? Do universities think it's sustainable to ask students to pay that eye watering $90,000 a year to, as he puts it, cheat their way through college? Tozel makes it clear that Harper isn't some technophobe. He's written thoughtfully about AI and education for years. His concern isn't that students are using these tools backrooms because they already are, but that institutions are becoming complicit in something that could hollow out the very core of what they claim to offer. And I think that's a crucial point that goes beyond the typical AI as a cheat machine narrative. It moves into a deeper, more philosophical territory. What exactly is the university selling? And by extension, what is any educational institution truly selling? The article points out three distinct promises convergent in President Olivisartos's letter. First, there's the potential for for AI in research and knowledge production. Second, the commitment to teach students how to think with machines, how to think without them, and how to think about them. And third, the ambition to relieve staff of administrative burden. Tozel suggests these goals don't necessarily contradict each other, but they don't automatically reinforce each other either. And the university, it seems, hasn't quite noticed that tension. This really highlights one of my core philosophies. We have to start with purpose over technology. You have to start with why, not how. It feels like the University of Chicago has embraced the how, giving everyone a powerful tool like Claude Enterprise, but perhaps hasn't fully articulated the why for its educational philosophy. In this new era, this is something K12 schools can learn from directly. Before we roll out any new tool or approach, especially with AI, we need to be crystal clear on what learning goal it serves and how it aligns with our pedagogical purpose. Now the article delves into the historical value proposition of higher education, which has always rested on three acquiring knowledge, learning to think, and connecting with a community. AI, of course, radically changes access to information Tozel paints a picture. A student asking Claude to summarize John Rawls before a seminar hasn't learned to read his work. A student using it to draft the opening paragraph of an argument hasn't learned to face the blank page. And this is the bit that really got me thinking. The capacity to sit with a hard problem before reaching for assistance, to tolerate the discomfort of not knowing is not a byproduct of education. It's close to the point of it. This is precisely where we need to protect process and productive struggle in our classrooms. From Year eight geography to Advanced Placement English. If students outsource the thinking, they gain cognitive debt instead of building capacity. Luciano Floridi and digital ethics scholar even warns in his book the Ethics of Artificial Intelligence that AI risks not just replacing old skills, but devaluing them. His concern isn't that humans will stop knowing things, but that de skilling in sensitive, skill intensive domains creates fragilities that only become visible when the technology fails. A university classroom, and indeed any classroom, is exactly such a domain. The ability to construct an argument under pressure to read a difficult text without a summary that's one click away. These are not quaint habits. They are capacities that, once lost at scale, are not easily recovered. Equipping students with those capacities remains something a school can still do. The question, as Tozel puts it, is whether it will. What Tyler Austin Harper Mrs. Tozel argues, is that the problem isn't that the University of Chicago gave students AI. The Enterprise license For Claude Enterprise actually carries a real practical benefit. It offers data protection and helps overcome the tiered access problem that often lets wealthier students use more powerful tools. This speaks to the equity potential of AI, where accessibility is not an afterthought but a foundation. So there's a good intention there. But the genuine problem, Tozel claims is is that this decision obligated the university to redefine its own educational philosophy. And the letter, he finds, doesn't do that. Instead, it presents ambiguity as flexibility. Policies vary by course, it says, and instructors at all levels are navigating a fast evolving landscape. These statements might be true, but they sound less like leadership and more like a transfer of responsibility. This echoes my thoughts on change leadership in K12. When we ask teachers to implement new tools or practices, we can't just hand them a technology and say, figure it out. We need to provide clear vision, purpose and support. Teachers often get labeled as resistant to change, but more often they just need time and space alongside a clear instructional framework. Given that they become the best drivers of innovation, this institutional approach from the University of Chicago, as described in the Chicago Tribune article, feels like a missed opportunity to truly activate teachers and faculty as change agents with a shared vision. If you're finding these conversations valuable, please consider following this podcast for more insights on AI in education. So what does this mean for K12 education leaders and teachers? Well, it provides a crucial cautionary tale and a powerful framework for our own university AI strategy. We can't simply adopt new AI tools for our students and staff because everyone else is doing it or because they promise efficiency. We must connect AI to existing friction points in our teaching and learning, but crucially, always anchor it to our core educational purpose. This isn't just about doing what we do better. That's linear innovation. This is about asking the bigger questions, engaging in non linear innovation, and truly reimagining education for the AI era. We need to design learning that cannot be faked because it demands depth, care and imagination. The real value is not in what the machine produces, but in how the student responds, how they critically evaluate, revise and transform the AI's output. We're teaching students not to outsmart machines, but to outthink them. This means focusing on AI literacy that goes beyond simply using tools. It's about collaborative reasoning ability, understanding AI limitations and failure modes, and managing AI conversations with precision. Think about a year 10 history projects. If a student uses Claude Enterprise to draft an entire essay, have they truly engaged with the primary sources? Or should the AI be used to help them brainstorm different angles, research counter arguments, or even evaluate the bias in historical texts. The latter is where the cognitive stretch lies. Tozel closes his piece by sharing his own journey from civil engineering to theology, noting that what drew him to graduate study wasn't access to information he already had, that it was the experience of being made uncomfortable by ideas I could not yet hold of writing through confusion rather than around it. That kind of formation, he argues, does not survive a curriculum built around convenience. This is a profound point. As we explore the incredible potential of AI in higher education, and indeed across all education, we must remember that machines can compute. They cannot wonder, they cannot care, they cannot judge, build relationships, imagine, or acquire wisdom. These are uniquely human domains we must preserve and protect. The author concludes that Harper is right about the urgency of the question, but wrong that the answer is obvious. No one yet knows what a university or a school looks like when it takes AI seriously and takes learning seriously at the same time. If this partnership with Claude Enterprise is to mean anything beyond a licensing agreement, the University of Chicago, and by extension, all of us in education, need to say clearly what we believe education is for, and then build a policy that protects it. Our job as educators and leaders is to create environments where students are pushed into that productive struggle, where they develop the very human capacities that AI cannot replicate, ensuring that AI is always an enhancement, not a replacement for deep, meaningful learning. That's all for today. Thanks for listening.