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AI Goes to College

AI Goes to College

Written by: Craig Van Slyke
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Generative artificial intelligence (GAI) has taken higher education by storm. Higher ed professionals need to find ways to understand and stay up with developments in GAI. AI Goes to College helps higher ed professionals learn about the latest developments in GAI, how these might affect higher ed, and what they can do in response. Each episode offers insights about how to leverage GAI, and about the promise and perils of recent advances. The hosts, Dr. Craig Van Slyke and Dr. Robert E. Crossler are an experts in the adoption and use of GAI and understanding its impacts on various fields, including higher ed.2024
Episodes
  • Fable 5's Cost Gap, AI "Cheating" at Scale, and a 70-Page Handbook in 20 Minutes

    Fable 5's Cost Gap, AI "Cheating" at Scale, and a 70-Page Handbook in 20 Minutes
    Jun 18 2026
    When Better Models Widen the Gap: AI's Cost Divide in Higher Ed (AI Goes to College, Ep. 36)What happens to students when the best AI models cost ten times more than the basic ones? That is the question Craig and Rob keep circling in this episode, prompted by Anthropic's brief and strange release of Fable 5.Fable 5 arrived as a guardrailed version of Mythos, a model so good at exposing software vulnerabilities that Anthropic had restricted it to a small set of secure organizations. For about a week it was freely available to paid users; then federal import controls landed and Anthropic pulled it, with no clear word on when, or whether, it returns. The hosts use that whiplash to get at the questions that actually matter for higher ed: who can afford the most capable tools, what that does to learning, and why none of it changes the deeper problem with how we assess students. They also dig into a large new study on student AI use, the agents Rob is building for faculty this summer, and a 70-page course handbook Craig generated in an afternoon.What you'll hearThe cost gap, in real numbers. Craig walks through Anthropic's tiers (Haiku, Sonnet, Opus, Fable) and what they cost to run: a task that runs free under his Opus subscription would have cost roughly $50 in Fable 5, while Haiku sits around $5. His worry is that this turns into an SAT-prep dynamic on steroids, where score gaps come from resource access rather than ability.Rob's counterintuitive flip. Rob raises the possibility that students stuck on weaker models might actually learn more, because they cannot offload as much of the cognitive work and have to stay involved in it. Neither host claims to know; they treat it as a real open question.A large study on student AI use. The hosts dig into a Science paper covering more than 95,000 students across 20 major U.S. public research universities. About two-thirds reported using generative AI in the prior year; roughly 9% of those users said they turned in AI-generated work knowing it wasn't allowed. The inappropriate-use rates run higher in non-STEM fields even though adoption there is lower.Faculty tools built over the summer. Rob describes agents his student interns are building: a syllabus-comparison tool that flags where a faculty member's syllabus diverges from the new template, an active-learning brainstorming assistant, and an AI-resilience checker for assignments and assessments.A textbook-grade handbook in an afternoon. Craig recounts handing OpenAI's Codex a couple of syllabi and one-shotting a 70-page course handbook for a freshman business course, then refining the activities. He pledges to release the finished version under a Creative Commons license.Why the gap is the real storyThe Fable 5 saga is good copy, but the hosts keep pulling it back toward something more durable. When the most capable models cost an order of magnitude more than the entry-level ones, the divide isn't only between rich and poor institutions; it reaches into a single classroom, where one student on a free model and another paying for the frontier model are turning in work that no longer means the same thing.Craig's answer isn't to chase the frontier. It's to teach students to match the model to the task; you don't pay for the expensive employee to do routine work, and you don't burn Fable 5 on something Haiku can handle. Rob extends the point to policy: banning AI outright is folly, both because it's nearly impossible to detect without introducing bias and because it leaves you with a classroom where you have no idea who learned what. Craig demonstrates the detection problem directly, running lightly edited AI text through Pangram and getting a "100% human" verdict. The shared conclusion is one they've made before and make again here: the urgent work is assessment reform, because a graded artifact is no longer a trustworthy signal of what a student actually knows.Episode highlights(12:11) Rob on weaker models and learning: "I wonder if the people who aren't using these highly capable models might actually learn more, because they're not going to be able to cognitively offload as much of the things that they're doing, and they'll need to be more involved in it."(15:51) Craig on Anthropic's rollout: "Anthropic really came off like a drug dealer that gives you a little taste before they try to get you hooked."(20:01) Craig on the study's central finding: "About 9% of those users turned in AI-generated work knowing it wasn't allowed."(26:31) Craig on why assessment has to change: "It's no longer a trustworthy signal of what they know if we keep doing things the way we've been doing them."(37:21) Craig on the AI-built handbook: "It was a 70-page handbook with learning activities; good, not great, in about 20 minutes."(45:31) Craig's top tip: "Handoff documents and memos will make your life so much easier when it comes to AI."References mentionedAnthropic's Fable 5 and Mythos models. Discussed as a guardrailed public ...
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    42 mins
  • AI's Underused Capabilities and Hidden Risks

    AI's Underused Capabilities and Hidden Risks
    May 18 2026
    Episode 35: AI's Underused Capabilities and Hidden RisksWhat happens when a university scrapes faculty lectures from its LMS, feeds them into an AI course builder, and sells the result for five dollars a month without telling the professors whose faces appear in the videos?Craig and Rob cover a packed news cycle in this episode, anchored by two stories about institutional vulnerability. The Canvas ransomware attack that disrupted final exams at thousands of schools opens a conversation about single points of failure; ASU Atomic, Arizona State University's new AI-powered course builder, raises harder questions about who controls faculty content and what happens when AI strips the context out of teaching. The episode also features Craig's deep dive into what coding agents like Codex and Claude Code can actually do for faculty (spoiler: it goes well beyond writing code), and a cautionary tale about Gemini failing spectacularly on a home networking problem.What you'll hearThe Canvas ransomware attack and what it reveals about AI dependency. The attack took down learning management systems at roughly 8,800 institutions during final exam season. Rob connects this to the broader security landscape for AI tools, arguing that the same single-point-of-failure problem applies to the AI agents and workflows faculty are starting to build. Craig's own Claude outage, which wiped out one of his custom skills mid-edit, underscores the point.ASU Atomic and the faculty backlash nobody saw coming. ASU's new platform uses an AI system called Atom to pull faculty lectures, assignments, and slide decks from Canvas, chop them into short clips, and reassemble them into personalized learning modules. Faculty weren't consulted. Rob immediately draws a parallel to NCAA name, image, and likeness rights. Craig argues the program will push faculty to pull their materials off the LMS entirely, hurting the most vulnerable students who depend on recorded lectures and posted materials.A practical showcase of coding agents for non-coders. Craig walks through a series of tasks he completed using Codex and Claude Code: de-identifying and structuring messy focus group transcripts, running text analysis algorithms, auditing and reorganizing doctoral seminar materials, and renaming over 130 PDFs with no coherent naming scheme. None of it required writing a single line of code. Rob pushes back on trust and sandboxing, and the two discuss the "middle ground" between AI slop and untouched human work.When AI hits a wall. Craig recounts an hour-and-a-half failure trying to use Gemini to troubleshoot a mesh network failover setup. The AI kept providing outdated instructions because the ISP had changed default settings without documenting the changes. The fix required a human tech support agent who could reset the modem remotely. The lesson: AI tools are great until they encounter the kind of hidden institutional knowledge that every organization has.The chilling effect on accessibilityThe ASU Atomic discussion surfaces a consequence that hasn't gotten enough attention in the broader coverage. Craig argues that the predictable faculty response to programs like Atomic is to minimize what they post to the LMS. No more recorded lectures, fewer slide decks, assignments handed out in person rather than uploaded. This is a rational defensive move for faculty, but it disproportionately harms students who depend on those digital materials: working students, parents, students with disabilities. The lifelong learning mission that ASU Atomic claims to serve gets undermined by the very mechanism used to pursue it. Rob extends this to the tension between financial incentives and student interests at land-grant institutions, noting that the populations these universities were built to serve may not be well-served by this model.Episode highlights(09:42) Craig on ASU Atomic: "They started up ASU Atomic, which uses something called ASU Atom, which is an AI course builder that goes out into the learning management system, pulls content from all these different courses, and repackages them into something that is going to be a $5 a month consumer-facing web app."(11:22) Rob on the NIL parallel: "I can totally see where faculty feel that they own their name, image, likeness, right? Much like our athletes deal with."(13:22) Craig on the chilling effect: "If you're worried about this, okay, I'm just not gonna have my lectures recorded. I'm gonna minimize what I put on the LMS... that's gonna have a detrimental effect on the most vulnerable students."(17:03) Craig on deepfakes and harassment: "You throw that in with deepfakes and forget about harassment. You could have considerable misinformation and disinformation campaigns built around legitimate faculty members."(30:22) Craig on the middle ground for AI in research: "There's this huge middle ground that we're gonna have to figure out where we're using AI to let us do better research and produce knowledge more ...
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    44 mins
  • When You Bring AI to the Party Matters More Than Whether You Bring It

    When You Bring AI to the Party Matters More Than Whether You Bring It
    Apr 28 2026
    When in your thinking process should AI show up? A new study suggests the timing matters more than the access.In this episode, Craig and Rob work through a recent CHI (Computer Human Interaction) conference paper that found a counterintuitive pattern: participants who had AI access from the start of a 30-minute task wrote weaker reports than those who got AI late or had no access at all. Same tool, same task, opposite result. The hosts connect the finding to Herbert Simon's satisficing concept and ask what it means for how faculty should teach AI use in their classrooms.The conversation also covers entry-level hiring trends in tech (which look better than the headlines suggest), Microsoft Office Agent's strange refusal to generate slides on a textbook chapter about AI, and why Rob worries the floor in higher education is rising while the ceiling may be coming down.What you'll hearA four-tool slide deck experiment. Craig made the same presentation in Microsoft Office Agent, Claude Cowork, ChatGPT, and Gemini. The differences in output quality, refusal behavior, and editability are larger than the marketing suggests.The CHI satisficing study. Researchers from Chicago and Toronto ran almost 400 participants through a civic decision-making task. With ten minutes, early-AI access helped. With thirty minutes, it hurt. The hosts unpack why and what it means for any knowledge work that requires actual thinking.Why "good enough" is now a problem. When AI can produce a serviceable draft in seconds, the differentiator shifts to what happens after the first draft. Craig and Rob discuss why the floor is rising for entry-level work and why the ceiling may not be rising with it.Entry-level hiring data. Recent IEEE Spectrum reporting suggests entry-level tech roles are growing in some categories, contradicting the prevailing narrative. The hosts walk through which roles and what the trend means for university programs preparing students for those jobs.AI sycophancy in the wild. Rob shares why the tools' tendency to agree with the user's framing is more dangerous in high-stakes situations than in low-stakes ones, and what that means for how we should be using them.Why timing matters more than accessThe dominant question in higher education has been whether students should use AI. The CHI study suggests that's the wrong fight. The better question is when AI should appear in a student's thinking process.Participants with late AI access in the study produced the same number of arguments as those without AI, but with more balanced pro-and-con reasoning. The tool became a counterweight to their own thinking rather than a substitute for it. That's a different mental model than the one most faculty (and most knowledge workers) default to, and it has practical implications for course design, assignment structure, and how we coach students to work with these tools.Episode highlights(approx. 5:41) Craig on the four-tool slide experiment: "A lot of times with AI, 50% is better than 100%, because you can get the 50% really quickly."(approx. 17:10) Craig on what entry-level workers need now: "The solution to helping our students get jobs is to show them how to lean into their humanity."(approx. 19:55) Rob on the floor-and-ceiling tension: "The floor is going up, but the fear is that our ceiling is coming down."(approx. 31:15) Craig on the satisficing finding: "It's not literally where you stopped — it's where your engagement stopped."(approx. 37:15) Rob's end-of-semester challenge to faculty: "Pick one thing. One thing that you're going to engage with over the summer."Links and referencesComputer Human Interaction (CHI) conference paper on AI access timing and decision quality (researchers from Chicago and Toronto): https://dl.acm.org/doi/pdf/10.1145/3772318.3791796IEEE Spectrum reporting on entry-level technology hiring trends: https://spectrum.ieee.org/ai-effect-entry-level-jobsHerbert Simon's concept of satisficing (1956)Microsoft Office Agent, Claude Cowork (Design feature), ChatGPT, GeminiFor faculty: questions worth sitting withWhere in your course design does AI currently show up, and would your students be better off if it appeared later in their process?How would you redesign one assignment so that students engage with the problem cold before the AI shows up?What does excellence look like in your discipline now that "good enough" is trivially achievable? How will you recognize it, and how will you teach students to reach for it?About the showAI Goes to College is a podcast for higher education professionals trying to make sense of artificial intelligence in their classrooms, their research, and their institutions. Co-hosted by Craig Van Slyke and Rob Crossler, the show focuses on practical, evidence-based perspectives on AI in higher education without the hype.Subscribe and listen: https://www.aigoestocollege.com/ Newsletter: https://aigoestocollege.substack.com/Mentioned in this episode:AI Goes to College Newsletter
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    36 mins
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