AI Goes to College

Hosted by Craig Van Slyke

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Sep 2026

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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.

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October 26, 202637 min

How AI Is Changing Entry-Level Knowledge Work

Episode 40: How AI Is Changing Entry-Level Knowledge Work How is AI affecting entry-level knowledge work, and what should colleges do to prepare graduates? Craig and Rob compare emerging evidence on employment and earnings, then consider what students need from higher education as technical tasks become easier to automate. Craig Van Slyke and Rob Crossler begin with reports from the National Bureau of Economic Research and the U.S. Census Bureau’s Center for Economic Studies. The findings raise different concerns: broad unemployment figures suggest limited change, while results for graduates in highly AI-exposed fields point to difficulties that averages can obscure. The hosts explore how employer partnerships can help students develop knowledge that requires experience inside organizations. They also discuss faculty AI fatigue and the credibility of professors whose teaching increasingly incorporates AI, before closing with practical classroom applications. What you’ll hear Why unemployment averages may miss changes in knowledge work. Craig contrasts relatively stable unemployment figures with findings about employment and earnings in AI-exposed fields. The discussion considers what happens when graduates find work, but in jobs that pay less or make limited use of their education. Whether moving people toward more demanding work creates jobs for them. Rob sees opportunities for human ingenuity as AI takes over some technical tasks. Craig raises a practical question: will companies hire people for those opportunities, especially when economic uncertainty encourages them to reduce spending? Why employers need to participate in preparing graduates. The hosts distinguish classroom learning from the contextual and tacit knowledge people develop through experience. Rob describes Washington State University activities in which companies bring real business problems to students and executives provide feedback. How continual adaptation affects faculty and their students. Rob worries about the exhaustion of repeatedly redesigning teaching and assessment. The conversation also examines what students gain from engaging with a professor when instructional materials appear to have passed through an AI lens. Practical AI assistance with a clear educational purpose. Craig describes converting a printed spending table into a Google Sheet and creating course-specific instructional materials. Rob explains how AI helps him turn lecture content into active learning activities, making those approaches more feasible within limited preparation time. Preparing students for work that requires context Rob argues that AI can free people from technical tasks and create room for more valuable contributions. Craig agrees with the possibility while questioning whether employers will hire accordingly. That uncertainty matters for colleges making decisions about what students should learn. Their discussion points toward stronger connections between academic programs and employers. Craig argues that universities can only partly simulate the messiness of working inside an organization; employers have a responsibility to help students develop the knowledge that comes from participating in actual work. Rob’s examples suggest that companies can be willing partners when universities provide useful ways to engage. The closing teaching examples connect that concern to classroom practice. A handbook tailored to a particular course can help students develop skills without supplying assignment answers. An active learning exercise can give them experience applying concepts rather than only hearing about them. In both cases, the professor decides where AI assistance supports learning and where students need to do the difficult work themselves. Episode highlights Times are approximate because the transcript provides one-minute markers. Each includes the 41-second pre-roll adjustment. (Approx. 07:41) Craig on computing as an early indicator: “I think we’re seeing the leading edge of something that’s going to affect all of knowledge work.” (Approx. 09:41) Craig on the employment question: “But are companies gonna hire for that? Do they need more of that?” (Approx. 13:41) Craig on experiential learning: “We can’t provide that level of contextual knowledge or, more importantly, tacit knowledge that you get by actually doing the thing.” (Approx. 17:41) Rob on continual adaptation: “We’re gonna see some burnout from the continual change process that we’re going through.” (Approx. 21:41) Rob on faculty credibility: “What is the value of engaging with me, the professor, who should be an expert in this topical area that I’m educating them in?” References mentioned Bill McCumber's Applied Erudition: https://appliederudition.com/ Robert W. Fairlie and Jane Wu, The Early Impacts of AI on Employment among Recent College Graduates , NBER Working Paper 35796, September 2026. Cody Orr, Lee C. Tucker, and Lawrence Warren, Graduating into Disruption: Labor Market Outcomes for AI-Exposed College Majors , Center for Economic Studies Working Paper 26-56, September 2026. Episode 39: Assessment and Community in the Age of AI , featuring Bill McCumber. Tools discussed include Lovable , ChatGPT Sites and Claude Artifacts. Craig also describes using Gemini with Google Sheets and Codex for seminar preparation. About the show AI Goes to College is a podcast for higher education professionals trying to make sense of artificial intelligence in their classrooms and institutions. Co-hosted by Craig Van Slyke and Rob Crossler, the show offers practical, evidence-based perspectives on AI in higher education without the hype. Find episodes and listening options at aigoestocollege.com . Before publishing: The paper titles are researched matches to the reports described in the transcript; their identities need confirmation. The link to Bill McCumber’s new assessment system still needs to be added. Mentioned in this episode: AI Goes to College Newsletter

September 29, 202654 min

Assessment and Community in the AI Age: A Conversation with Dr. Bill McCumber

Episode 39: Assessment and Community in the Age of AI How can faculty assess learning when AI can produce most of the artifacts we used to grade, and what happens to campus community when students turn to chatbots instead of each other? Craig and Rob talk with Dr. Bill McCumber of Louisiana Tech University about both questions. Overview Bill McCumber, Associate Dean of Graduate Programs and Research at Louisiana Tech's College of Business, joins the show to describe two efforts: a voluntary, 17-person AI task force built around community rather than mandates, and a graduate finance pilot that replaces tests with AI-supported oral assessment at scale. The hosts press Bill on what oral exams actually measure, why faculty resist handing off grading, and what administrators should be doing right now. The conversation then turns to MIT's recent report on AI in teaching and learning, which the hosts argue buried its most important finding: AI's effect on the social fabric of campus life. The episode closes with a practical tip on AI model selection and usage quotas. What you'll hear Why an AI task force should be "culture work." Bill explains why he built a voluntary group of faculty, administrators, administrative assistants, and students (and why he kept Craig off it). His aim is community building and comparing notes rather than prescribing tools or syllabus policies, and the first brown bag features an AI skeptic. The barriers keeping faculty from useful AI. Rob points to a lack of time to experiment and to university bureaucracy, including FERPA constraints that leave Copilot as the only approved tool at Washington State University. Bill argues that faculty need enough domain knowledge to "earn the right to automate." Oral exams at scale, and their hidden noise. Bill describes a pilot in which students build projects with AI, then answer an unexpected question aloud; responses are de-identified, graded through a pipeline, and flagged for follow-up. Craig raises a concern that oral exams also measure verbal communication and stress response, and suggests a secondary one-on-one review for students who struggle. AI in the student-managed investment fund. Bill's students used AI to turn a raw download of portfolio positions into an interactive dashboard with Sharpe ratios and correlation matrices in short order, work that would once have taken weeks of coding. The MIT report's underreported finding. Craig notes that a New York Times headline focused on "cognitive surrender," which gets only a brief mention in the report; the more striking material concerns isolation and campus social cohesion. Rob and Bill share anecdotes about students returning to phone calls and office visits. From lecturing to coaching Bill's pilot moves foundational content (the "vocabulary lessons") online so class time can become coaching, group work, and "discussion under uncertainty." Rob connects this to his work as a soccer referee: practice builds the skills, and the match shows whether the drills paid off. Assessment in this model looks less like a test and more like the workplace, where, as Bill puts it, someone eventually checks whether you can do the thing. Craig pushes back on treating grading as binary, pointing out that faculty have delegated first-pass grading to teaching assistants for decades; AI could play a similar triage role for oral assessments while faculty keep judgment over the hard cases. References mentioned MIT Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training. (2026, August 13). Report of MIT's Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training . Report site: https://aiandeducation.mit.edu/ ; PDF: https://bpb-us-e1.wpmucdn.com/sites.mit.edu/dist/d/2418/files/2026/09/AI-Committee-Final-Report-Aug-13.pdf Socrates' critique of writing as a proxy for knowledge (recorded in Plato's Phaedrus ) Bélanger, F., Van Slyke, C., & Crossler, R. E. Information Systems for Business: An Experiential Approach . Prospect Press. https://www.prospectpressvt.com/textbooks/b%C3%A9langer-information-systems-for-business-an-experiential-approach-5-1 Previous AIGTC episode with Tom Stafford on AI and student isolation https://www.aigoestocollege.com/building-ai-literacy-in-higher-education-programs-practice-and-community/ Tools named: Microsoft Copilot, Claude, ChatGPT (Astra), Fable 5 Questions for reflection If oral assessment returns, how will you separate what a student knows from how comfortable they are speaking under pressure? What is actually stopping faculty at your institution from using AI to reclaim time, and has anyone asked them? If AI is thinning the social fabric of campus, what in your course gives students a reason to talk to each other? About the show AI 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. Website: https://aigoestocollege.com Newsletter: https://aigoestocollege.substack.com/ Email: craig@aigoestocollege.com and Rob.Crossler@aigoestocollege.com Mentioned in this episode: AI Goes to College Newsletter

August 25, 20261 hr 7 min

Building AI Literacy in Higher Education: Programs, Practice, and Community

Episode 38: Building AI Programs While the Ground Is Still Moving Should a business school build AI-specific degree programs, or work AI into the curriculum it already has? Tom Stafford, professor and chair of Management Information Systems at the University of Memphis, made his choice and explains why he made it. Craig and Rob are joined by Dr. Tom Stafford, who came back to Memphis after nine years at Louisiana Tech and is launching a four-course graduate certificate in applied AI this fall, plus a five-course concentration in the MSIS program built on the same courses. The conversation starts with the mechanics of standing up a program quickly (prompt engineering and AI-assisted application development first; AI-assisted database and analytics in spring) and widens into the harder questions: whether AI will diffuse into everything the way e-commerce did, why the "haters" may not be the problem people think they are, and what universities actually owe faculty and staff who want to learn this. Along the way, Rob describes a staff AI workshop he ran at Washington State, Craig coins "setting the context for discovery," and the cool uses segment turns up a dynamic news site, an interactive game about professional dress, and a blues band website built by someone who calls himself an AI neophyte. What you'll hear Why Memphis went program-specific rather than curriculum-wide. Tom is candid that the driver was enrollment. International graduate enrollment fell hard after visa issues, and his brief coming in was to build something that would draw local students. Being first through the state curricular approval process mattered; so did the fact that International Paper, FedEx, AutoZone, and an xAI footprint sit in the same city. The e-commerce analogy, and where it breaks. Rob asks the five-to-ten-year question: won't AI just become the way business is done, the way e-commerce did? Tom, who arrived at Memphis as the e-commerce expert and watched exactly that happen before tenure, agrees it's the medium-term outcome. Craig pushes back; e-commerce spread incrementally, while the jump from chatbot to agentic AI is a discontinuous change whose horizon keeps moving. Requiring AI instead of policing it. Tom decided years ago not to chase students who use AI. He requires a term paper produced with an LLM, and if it isn't excellent, the student hasn't done the work. His qualitative data on that requirement shows students finding it useful and also finding it harder than expected; "way more work than I expected" is a common response. Where administration helps and where it gets in the way. Rob makes the case for grassroots communities of practice over top-down workshops that promise to teach everything. Craig agrees but worries organic groups stay small, and argues administration's real jobs are spreading the practice past the initial circle and signaling that experimentation is an acceptable use of faculty time. Cool uses. Craig walks through the AI Goes to College daily briefing site he built with ChatGPT's Sites feature, fed by a scheduled task that scans higher ed AI news every weekday. Rob built an interactive game with the same tool to teach students what business casual actually means. Tom built and now runs bluetattooband.com himself. The time-and-a-half problem The most useful idea in the episode may be Tom's comparison of AI adoption to the distance education buildout of the past two decades. Learning to use AI for your work costs more time than simply doing the work; universities recognized that with online course development and paid for it in course releases and stipends. They have mostly not done the same for AI. Tom's point is that faculty resistance often isn't ideological at all; it's a schedule problem dressed up as one. Craig's related concern lands on staff rather than faculty. Faculty are effectively independent contractors, so efficiency gains stay with them. Staff efficiency gains get noticed, and the reward is more work. His suggestion is that this is manageable at the department chair or associate dean level: when AI takes the tedious parts of someone's job, fill the space with work they find worth doing rather than more tedium. Episode highlights Timestamps include the 41-second pre-roll; verify against the audio before publishing. (03:51) Tom on scoping the program: "We're not equipped in a business school to help anybody become an AI wizard and build algorithms and design platforms." (20:31) Craig on why this isn't e-commerce: "The knowledge work of the future is not gonna be the knowledge work of the past." (23:01) Tom on academic integrity: "I decided a long time ago, I wouldn't worry about trying to catch people doing it, I'd force them to do it." (30:01) Craig coining a phrase: "I think we need to set the context for discovery." (44:01) Tom on the real barrier: "It's more work to learn how to use AI to do your work than it is to simply do your work." (48:56) Rob on breaking down silos: "None of us should be inventing the same wheel all by ourselves." Questions worth sitting with If your institution paid for AI learning time the way it once paid for online course development, who on your campus would take it? Who are the people at your school who genuinely enjoy experimenting with AI, and does anyone in administration know who they are? When staff free up time with AI, what happens to that time? People, programs, and research mentioned Dr. Tom Stafford , professor and chair, Department of Management Information Systems, University of Memphis; co-host of the Cyber Ways Podcast. https://www.memphis.edu/mis/faculty_research/faculty-directory/stafford-tom.php University of Memphis MIS Department . https://www.memphis.edu/mis/ MSIS program, Fogelman College (home of the new applied AI concentration). https://www.memphis.edu/fcbegrad/specialized/msis.php AI for All undergraduate minor , University of Memphis. https://www.memphis.edu/aiforall/ Graduate certificate in applied AI , University of Memphis. [Craig: no public page found; ask Tom for the URL] Yajung "Lucky" Shui , new Memphis hire bringing the AI-assisted analytics course. [Craig: verify name spelling before publishing] Huigang Liang , University of Memphis, mentioned as doctoral co-coordinator. Cyber Ways Podcast , Tom's cybersecurity podcast with Craig. https://podcasts.apple.com/us/podcast/cyber-ways-podcast/id1584638901 Miklos Vasarhelyi and Chanyuan (Abigail) Zhang , Rutgers, on the auditor supervising AI-run automation in real time. Gorry and Scott Morton's framework on what to automate and what to leave to human judgment. Granovetter's strength of weak ties , referenced by Craig as Tom's area of expertise. Van Slyke and Stafford's grassroots diffusion work , the 20-year-old paper Tom's current model builds on. Rob's staff AI workshops at the Carson College of Business and the Voiland College of Engineering and Architecture, Washington State University. Centers for Instructional Development as the institutional home for AI literacy; Tom's provost is modeling Memphis's on Purdue. Professor Jacob at Louisiana Tech , building a similar faculty support program. [Craig: full name and program name needed] AI Assisted Author , Craig's project with fellow podcaster Brad Miller helping content creators write books with AI. [Craig: URL needed if you want it linked] Epic Systems , where Tom's son worked as a C# programmer before leaving for a master's in AI. https://www.epic.com/ Memphis Blues Society , founded by Tom in 2005. https://www.memphisbluessociety.com/ The Cooper Young Sessions , the Blue Tattoo album in progress. References (APA 7) Gorry, G. A., & Scott Morton, M. S. (1971). A framework for management information systems. Sloan Management Review, 13 (1), 55–70. https://dspace.mit.edu/entities/publication/8de60684-115e-46b5-99b7-5ca3b802f1f5 Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78 (6), 1360–1380. https://doi.org/10.1086/225469 Van Slyke, C., & Stafford, T. (2004). Grassroots diffusion: A research agenda and propositional inventory. DIGIT 2004 Proceedings, 1 . https://aisel.aisnet.org/digit2004/1/ Zhang, C., Thomas, C., & Vasarhelyi, M. A. (2022). Attended process automation in audit: A framework and a demonstration. Journal of Information Systems, 36 (2), 101–124. https://doi.org/10.2308/isys-2020-073 Tools mentioned ChatGPT Sites (used for the AIGTC daily briefing site and Rob's professionalism game). https://chatgpt.com/ Claude. https://claude.ai/ Suno, used for Craig's border collie bluegrass experiment. https://suno.com/ Wix, used to build and manage bluetattooband.com. https://www.wix.com/ SAS Viya with Copilot integration, the analytics platform behind Memphis's applied AI...

July 13, 202649 min

What students actually do with AI

Episode 37: AI as the Learned Colleague, Not the Ghostwriter What does it look like to write an academic paper with AI without letting AI write a single word of it? Craig and Rob open with that question, then move through a new UK survey on how student AI use is evolving, ChatGPT's shift from Pulse to Scheduled Tasks, and fresh labor market data on what AI is doing to entry-level jobs. Craig describes using Codex to co-produce a 25-page conference paper in about three days, not by asking the tool to write sections, but by writing them himself and requesting feedback, the same back-and-forth he'd have with a co-author. That framing anchors a wider conversation about inconsistent AI disclosure rules across journals and conferences, then the hosts turn to the Higher Education Policy Institute's 2026 GenAI survey of UK students, which shows near-universal AI adoption alongside a narrowing of specific use cases. They close with ChatGPT's new Scheduled Tasks feature, a PwC report on entry-level jobs requiring senior-level skills, and updates to NotebookLM's video and slide tools. What you'll hear Writing as a colleague, not a ghostwriter. Craig used Codex to draft a 25-page conference paper by writing sections himself and requesting feedback, rather than asking the AI to generate text, plus using it to locate and format references and diagnose a thin section of the argument. Inconsistent AI policies across academic venues. Rob raises the problem of conferences with looser AI disclosure norms and journals with stricter ones, including an editor's account of a publisher flagging words like "consequentially" as a telltale sign of AI use. The HEPI 2026 GenAI survey results. Any AI use among UK students rose from 66% to 92% year over year, while text generation specifically dropped from 64% to 56%. Barriers to use are falling too: fear of being accused of cheating dropped from 53% to 42%, fear of hallucinations from 51% to 35%, and institutional bans as a deterrent from 31% to 21%. Least-skilled, most-confident. A case study in the HEPI report found that medical students working with AI-tuned virtual patient cases who were least skilled were also most confident in their AI use, prompting a discussion of algorithmic trust and critical thinking. The loneliness question. Of surveyed students, 59% said generative AI has no impact on their loneliness, with the rest split almost evenly between feeling less lonely and more lonely. Rob raises concerns about long-term effects, drawing a parallel to how long it took research and regulation to catch up with social media's impact on youth. ChatGPT Scheduled Tasks and the PwC jobs data. The hosts compare ChatGPT's Scheduled Tasks (successor to ChatGPT Pulse) to Claude Code routines and Codex automations, then turn to PwC's 2026 Global AI Jobs Barometer, which found AI-exposed entry-level jobs are seven times more likely to require senior-level skills like judgment, leadership, creativity, and face-to-face interaction. Episode highlights (03:34) Craig on model convergence: "The top model of yesterday is now the mediocre model of today." (09:40) Craig on his conference paper workflow: "I want to really emphasize that AI did not write anything... it was really just having that colleague there." (19:34) Craig on falling barriers to AI use in the HEPI data: fear of being accused of cheating "dropped to 42% from 53%," and fear of "getting false results and hallucinations dropped from 51% to 35%." (20:37) Craig on AI detectors: "So stop. If you're doing that, stop. They don't work. They're bad." (34:53) Craig reading PwC's finding: "AI exposed entry level jobs are now seven times more likely to require traditional senior skills such as judgment, leadership, creativity and face to face interaction." (38:32) Rob on tacit knowledge: the real concern is "how do you learn the norms of an institution and how they operate and how you get things done," something he doesn't think AI can teach at the individual level. References mentioned Higher Education Policy Institute (HEPI), 2026 GenAI student survey, UK, more than 1,000 students surveyed (freely available report) PwC, 2026 Global AI Jobs Barometer , based on more than 1 billion job ads globally and 2.4 million US entry-level roles An unnamed arXiv preprint from a few months prior, arguing official labor statistics undercount AI disruption because the real impact falls on tacit knowledge transmission through early-career work Grok (xAI), used by Rob to draft a family lease agreement Codex (OpenAI), used by Craig as a writing collaborator for a conference paper Microsoft Copilot (M365), used by Rob for document review and editing feedback ChatGPT Scheduled Tasks and its predecessor, ChatGPT Pulse (OpenAI) Claude Code routines and Codex automations, comparison points for scheduled AI briefings NotebookLM (Google), including its short-video and slide-editing features AI Resilient Learning Activities Database, an upcoming free repository from AI Goes to College , funded by a Just Business grant through the Louisiana Tech College of Business Main takeaways AI as colleague, not author: Craig used Codex to draft a conference paper by writing sections himself and asking for feedback, not by having it generate text Academic AI disclosure rules vary sharply by venue, creating real friction for multi-venue submissions UK student AI use hit 92%, even as basic text generation use dropped, pointing to more varied use cases Fear-based barriers (cheating accusations, hallucinations, institutional bans) are all declining as students gain experience Least-skilled medical students showed the most confidence in AI-tuned tools, a warning sign for critical thinking Student-reported loneliness effects from AI split almost evenly between better, worse, and no change PwC data shows entry-level jobs now require senior-level skills like judgment and creativity seven times more often The apprenticeship pipeline that once taught tacit workplace knowledge may not survive as that entry-level work disappears Links Book of Common Prayer: https://www.episcopalchurch.org/wp-content/uploads/2021/02/book-of-common-prayer-2006.pdf HEPI report: https://www.hepi.ac.uk/reports/student-generative-ai-survey-2026/ --- AI 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. For all things AI Goes to College, including the podcast, go to https://www.aigoestocollege.com/. Mentioned in this episode: AI Goes to College Newsletter

June 18, 202642 min

Fable 5's Cost Gap, AI "Cheating" at Scale, and a 70-Page Handbook in 20 Minutes

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 hear The 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 story The 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 mentioned Anthropic's Fable 5 and Mythos models. Discussed as a guardrailed public release (Fable 5) built on the restricted Mythos family, later pulled following federal import controls. (Link to Anthropic announcement / status page: see "What I need from you.") Study on student generative-AI use in Science (data collected 2024). (Full citation and DOI to be provided: see "What I need from you.") Pangram : AI-text detector Craig used to test a lightly edited, skill-generated draft. (Link: see "What I need from you.") OpenAI Codex : GPT-based coding agent Craig used to generate the course handbook. Skills in Claude / Cowork and Microsoft Copilot : discussed as reusable, callable tools for tasks like generating active-learning activities. Van Slyke & Crossler textbook (with co-author Franz Boulanger): referenced as a point of comparison for AI-assisted content creation. (Title / link if you want to include it: see "What I need from you.") Questions to consider If two students submit work produced with very different models, are you assessing their learning or their access? Where in your own courses is a graded artifact still a trustworthy signal of what a student knows? What's one tedious faculty task this summer that an agent could take off your plate? AI 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 follow: https://www.aigoestocollege.com/follow · Newsletter: https://aigoestocollege.substack.com/ Links Science article Chirikov, I., Smirnov, I., & Kizilcec, R. F. (2026). Generative AI use and misuse call for assessment reform in higher education. Science, 392(6800), 818-820. https://www.science.org/doi/10.1126/science.aec5115 Anthropic Fable 5/Mythos announcement https://www.anthropic.com/news/fable-mythos-access Pangram (AI "detector") https://www.pangram.com/ Information Systems for Business: An Experiential Approach (Belanger, Van Slyke & Crossler) https://www.prospectpressvt.com/textbooks/b%C3%A9langer-information-systems-for-business-an-experiential-approach-5-0 Mentioned in this episode: AI Goes to College Newsletter

May 18, 202643 min

AI's Underused Capabilities and Hidden Risks

Episode 35: AI's Underused Capabilities and Hidden Risks What 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 hear The 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 accessibility The 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 effectively and more efficiently. But it's not AI slop. It's still something that was done with human oversight, kind of like we've been doing with GAs for a long time." (40:22) Craig on AI limitations: "These AI tools are great until they're not." References mentioned ASU Atomic (also called ASU Atom, internal codename "Project Atomizer"): Arizona State University's AI-powered course builder, launched as a pilot in April 2026 Canvas ransomware attack (May 2026): attack on Instructure's Canvas LMS affecting approximately 8,800 institutions during final exam season OpenAI Codex : OpenAI's autonomous coding agent Claude Code : Anthropic's coding agent (Craig's primary tool for the tasks described in the episode) Google Antigravity : Google's coding agent (mentioned but not tested for the tasks Craig describes) Gemini : Google's AI assistant (used in the networking troubleshooting story) NCAA Name, Image, and Likeness (NIL) rights : invoked by Rob as a parallel to faculty IP concerns Arizona Board of Regents intellectual property policy : the work-for-hire framework under which ASU claims ownership of faculty-created course materials Eero 7 mesh network devices (Amazon): the hardware in Craig's networking troubleshooting story AI 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: [link to platforms] | Read more: [link to AIGTC Substack] Takeaways: The reliance on learning management systems like Canvas exposes institutions to vulnerabilities during outages, especially during critical academic periods. AI tools, while enhancing productivity, present significant risks if not managed with proper oversight and backup strategies. The recent developments at Arizona State University demonstrate a growing trend of institutions utilizing AI in ways that may undermine faculty autonomy and intellectual property rights. The integration of AI into educational settings necessitates a shift in teaching methodologies towards experiential learning and greater student engagement. Backup protocols are essential when utilizing AI tools to prevent loss of critical data and ensure continuity of work in educational environments. The evolving landscape of AI requires educators to actively engage with these technologies to better understand their implications and guide student use effectively. Mentioned in this episode: AI Goes to College Newsletter

April 28, 202636 min

When You Bring AI to the Party Matters More Than Whether You Bring It

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 hear A 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 access The 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 references Computer Human Interaction (CHI) conference paper on AI access timing and decision quality (researchers from Chicago and Toronto): https://dl.acm.org/doi/pdf/10.1145/3772318.3791796 IEEE Spectrum reporting on entry-level technology hiring trends: https://spectrum.ieee.org/ai-effect-entry-level-jobs Herbert Simon's concept of satisficing (1956) Microsoft Office Agent, Claude Cowork (Design feature), ChatGPT, Gemini For faculty: questions worth sitting with Where 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 show AI 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

March 31, 202647 min

Accessibility Hacks, 81,000 Interviews, and the Choppy Waters of Academic AI

AI Goes to College, Episode 33: Accessibility Hacks, 81,000 Interviews, and the Choppy Waters of Academic AI Higher education is drowning in accessibility deadlines, grappling with what 81,000 AI interviews reveal about how people actually use these tools, and watching the academic publishing system creak under new pressures. In this episode, Craig and Rob dig into all three, with practical advice, a few uncomfortable truths, and their usual mix of optimism and healthy skepticism. The Accessibility Crunch Is Here (and AI Can Help) The episode opens with a problem that's top of mind for faculty everywhere: the April 24 federal deadline requiring public-facing digital content to meet WCAG accessibility guidelines. Universities have been scrambling, and many of the contracted tools designed to help have been, as Craig diplomatically puts it, hit and miss. Craig shares a concrete example from his own workflow. He took three image-heavy slide decks from his Principles of Information Systems course and handed them to Claude Cowork with a simple instruction: add alt text for all the images. Within about 30 minutes, the job was done. The accuracy? Roughly 75 to 80 percent. A handful of images needed corrections, but instead of writing alt text for 40 or 50 images from scratch, he only had to fix six or eight. Rob tried something similar with Microsoft Copilot on a keynote presentation he gave at the SAIS conference in Asheville; two images, 30 seconds, done. Rob makes the important point that accessibility isn't just a PowerPoint problem. It extends to whiteboard files, videos, and essentially everything faculty communicate digitally. The burden is real, and it lands on faculty who are already overwhelmed by the changes AI is bringing to their professional lives. Craig adds a note of personal sensitivity here; his wife has a profound hearing disability, which makes these issues more than abstract compliance for him. The larger takeaway? When you hit one of these friction points in your work, try AI. It won't always solve the problem, but it often will, and the time savings can be substantial. What 81,000 Interviews Tell Us About How People Actually Use AI Link: https://www.anthropic.com/features/81k-interviews Craig's article: https://open.substack.com/pub/aigoestocollege/p/what-81000-people-told-anthropic The conversation shifts to Anthropic's large-scale qualitative study, where Claude was used to conduct and analyze 81,000 interviews about how people use AI tools. Rob, who has spent considerable time doing qualitative research the traditional way (36 interview transcripts with families, a labor-intensive process), finds the scale almost hard to believe. Craig wrote a separate article about this study for the AI Goes to College newsletter. The phrase that catches both hosts' attention is one from the report: "the light and the shade are tangled together." It captures the tension between excitement about AI's possibilities and anxiety about what those possibilities mean for how people work, learn, and think. Craig connects this to a concept from technology studies: this is not technological determinism. The outcomes aren't dictated by the tools themselves. They emerge from the sociotechnical space where human choices and technological capabilities intersect. Rob observes that most current AI use cases still amount to doing what we've always done, just faster. The real transformation will come when people start imagining entirely new approaches (he draws an analogy to cloud computing, which started as a backup solution and eventually reshaped how people interact with technology in ways nobody initially anticipated). One quote from the Anthropic study lands hard. A freelance software engineer in Pakistan says: "I want to learn skills, but learning deeply is of no use. Ultimately I can just use AI." Craig points out that if a working professional thinks this way, the implications for students who may not yet appreciate the long-term value of deep learning are sobering. Rob agrees but pushes back slightly: people who lean too far into this mindset will eventually hit a wall where they lack the critical thinking skills to know when or why AI has gotten something wrong. The hosts converge on what's becoming a running theme for the podcast: higher education's central task is helping students understand the long-term value of cognitive engagement, because without that understanding, the default will always be to let AI handle it. Academics Need to Wake Up: 10 Theses on a Shifting Landscape Link: https://substack.com/home/post/p-189705626 The second major discussion centers on Alexander Kustoff's Substack article, "Academics Need to Wake Up on AI: 10 Theses for Folks Who Haven't Noticed the Ground Shifting Under Their Feet." Rob sees it as a useful prompt for conversations the research community needs to have. Craig appreciates the ambition but pushes back on some of the claims. Take thesis number one: AI can already do social science research better than most professors. Craig's reaction is nuanced. The claim is probably technically true if "most" is read literally, since many professors don't publish much (Rob notes the median number of publications for business school professors may be as low as one). But the implication that AI can replace skilled researchers? Not yet. Craig estimates that a knowledgeable researcher can use AI to cut research production time by about three-quarters, but that knowledge is the key ingredient; without research skill, you'll just produce publishable garbage faster. Rob raises something interesting: colleagues who are brilliant thinkers but never thrived in research because they didn't enjoy writing may now have a path to contribute. AI could genuinely democratize parts of the research process. Craig extends this point to data analysis; tools like Cowork can run Python and R analyses without expensive specialized software, which matters enormously for under-resourced institutions and researchers in developing countries. The conversation turns to the strain AI is putting on the peer review system. More submissions (many of them better written thanks to AI) are flooding journals, but finding reviewers was already difficult. Craig, speaking from his role as a journal editor, argues that well-trained AI could do a better job reviewing than roughly half of current human reviewers. Rob agrees but emphasizes that journal leaders need to come together and define norms for what's acceptable. Right now, the rules are either nonexistent or unrealistically restrictive ("just don't use AI for anything"), which creates the same kind of confusion faculty have imposed on students with inconsistent classroom policies. One of the most provocative moments comes when Craig reads a quote from the Kustoff article: "I don't envision a research assistant role in my workflow anymore. What I want from collaborators is original thinking, domain expertise, and intellectual challenge. This is a genuine loss for the traditional apprenticeship model, and I don't have a clean answer for how to replace it." Both hosts take this seriously. Craig argues that senior scholars will need to accept some suboptimal results in the short term to continue mentoring the next generation. Rob suggests the apprenticeship model isn't dying; it's transforming. The mentorship shifts from teaching students how to do tasks to teaching them how to direct AI tools and critically evaluate what those tools produce. Craig closes with a characteristically honest observation: senior scholars get stuck in their ways of thinking, and one of the real values of working with early-career doctoral students is the occasional moment when their unformed, messy thinking reveals a perspective that nobody in the room had considered. That's worth protecting. AI-Generated Lesson Plans and the Bloom's Taxonomy Problem Link: https://citejournal.org/volume-25/issue-3-25/social-studies/civic-education-in-the-age-of-ai-should-we-trust-ai-generated-lesson-plans/ The final segment covers a paper by four researchers from UMass Amherst, "Civic Education in the Age of AI: Should We Trust AI-Generated Lesson Plans?" The study found that roughly 90 percent of AI-generated lesson plans hit only the lower levels of Bloom's taxonomy (remembering, understanding) rather than the higher-order thinking skills like analyzing, evaluating, and creating. Craig's first reaction was that the prompts used in the study were terrible. But he acknowledges the researchers had a reason: they were mimicking how most teachers would actually prompt. And that's the real finding. The problem isn't that AI can't produce sophisticated lesson plans; the problem is that untrained users produce unsophisticated prompts, and the output reflects the input. Rob agrees and broadens the point: if even a fraction of teachers are prompting this way, that's affecting a lot of students. Craig shares a personal anecdote from his one year as a high school teacher. He diligently wrote lesson plans; a veteran teacher (whom he describes as one of the best he'd ever seen) simply copied his plans to satisfy an administrative checkbox. The experienced teacher didn't need detailed plans because she could read the room and adapt in real time. Some lesson planning, Craig suggests, falls...

March 3, 202648 min

We're On Our Own: Academic Integrity through AI Resilience

Craig and Rob kick off this episode with a deep dive into Claude's Constitution — the 84-page document Anthropic released to explain how Claude is governed. The document lays out a four-part hierarchy of priorities: be broadly safe, be broadly ethical, follow Anthropic's guidelines, and be genuinely helpful — in that order. Craig walks through the key language, and both hosts zero in on the uncomfortable questions it raises. Who gets to define "broadly ethical"? Whose values count? Craig points out that collectivist and individualist cultures would answer those questions very differently, and Rob raises the example of how privacy has historically carried different social weight in China versus the United States. They give Anthropic credit for the transparency. Rob notes that he has no idea what governs ChatGPT by comparison, and Craig argues the openness could become a real differentiator for universities evaluating which AI tools to bring in-house. But the Constitution also includes some curious language — the phrase "during the current phase of development" gives Anthropic significant room to evolve these guardrails over time, and a section on emotional support states that Claude should "show that it cares," which both hosts flag as a strikingly anthropomorphic choice of words. Craig shares a fun aside: he used Claude Code to build a clone of the classic Colossal Cave Adventure game — reframed around understanding large language models — using just a few sentences as a prompt. The game was up and running in about an hour. That kind of capability would have been unthinkable a couple of years ago, and it underscores why the Constitution's language about the "current phase" matters so much. The big takeaway from the Constitution discussion lands hard: higher ed is on its own when it comes to academic integrity. Anthropic — arguably the most transparent of the major AI companies — has no interest in blocking students from misusing its tools. Rob mentions a new product called Einstein that will watch your Canvas videos, write your discussion posts, reply to classmates, and complete your assignments. All you have to do is hand over your login credentials. That sets up the episode's second major topic: AI resilience. Rob explains the concept as designing learning outcomes that hold up regardless of what AI can do. If a major portion of a student's grade depends on writing an essay that AI could produce in seconds, that assignment has very little resilience. The shift Rob advocates moves evaluation toward process — asking students for the prompts they used, reflections on how they refined their approach, and demonstrations that they understand what was produced. He shares the example of a colleague whose programming class now requires students to record videos explaining their code rather than just submitting it. Craig raises the scaling problem. He regularly teaches 90 to 100 undergraduates. Rob suggests that AI itself can help with formative feedback on scaffolding assignments, freeing faculty to focus their grading energy on fewer, higher-stakes assessments. Craig uses an analogy from music: scaffolding assignments are like playing scales — you do them to build toward performance, and they don't need to carry grade weight. Both hosts agree this represents a move away from the grade economy, where students rationally minimize effort because every small assignment is a transaction. Craig pushes the conversation further by proposing live client projects — or AI-simulated client projects — as a way to create the messiness and ambiguity that real work demands. Rob's initial reaction is skepticism (live client projects are logistically brutal), but he warms to the idea of using AI to simulate clients with realistic fuzziness and scope creep. The broader point: AI could be the lever higher ed needs to fix problems that have been accumulating for decades. The episode wraps with an update on NotebookLM. Craig walks through the recent changes — more user control over reports, slide decks, flashcards, quizzes, and other outputs in the Studio panel. You can now specify the structure and focus of custom reports rather than relying solely on canned formats. Slide decks can be exported (though editing remains clunky since each slide is essentially an image). Craig's recommendation: if you have a Google account and you work with knowledge in any form, you should be using NotebookLM. Rob notes that Microsoft Copilot has added a similar notebook feature worth exploring, and they float the idea of a future head-to-head comparison episode. Links referenced in this episode: notebooklm anthropic claude google canvas einstein Mentioned in this episode: AI Goes to College Newsletter

February 16, 202640 min

Students Are Confused About AI and It's Our Fault (with Dr. Bette Ludwig)

Dr. Bette Ludwig spent 20 years in higher ed working directly with students before leaving to build something different — a Substack (AI Can Do That), a consulting practice, and most recently, the Socratic AI Method, an AI literacy program that teaches students how to think critically alongside AI while keeping their own voice intact. That last part is the hard part. Craig opens with the question that drives the whole episode: Socratic dialogue requires you to already know enough to ask good questions. So what happens when a student doesn’t know enough to push back on what AI is telling them? Bette’s answer is both practical and unsettling — younger students literally don’t know what they don’t know, and that gap is where the real danger lives. The conversation moves into dependency territory when Craig shares a moment from his own morning: Claude froze while he was editing a manuscript, and he felt a flash of genuine panic. Two seconds later, he remembered he could just… write. But he names the uncomfortable truth — his students won’t have that fallback. Bette compares it to the panic we feel when the wifi drops, which is both funny and a little alarming when you sit with it. From there, the three dig into the policy mess — teachers across the hall from each other running opposite AI rules, students confused about what’s allowed, and educational systems moving at what Bette calls “a glacial pace” while the technology sprints ahead. Craig shares his own college’s approach: you have to have a policy, it has to be clear, but how restrictive or permissive it is remains your call. The non-negotiable? You can’t leave students in the dark. The episode’s most surprising thread might be Bette’s observations about how students actually use AI. It’s not just homework. They’re using it for companionship, personal problems, cooking questions, building apps — ways that don’t even register as “AI use” to most faculty. Her closing point lands hard: students have never used technology the way adults assume they should, and they’re going to do the same thing with AI. Key Takeaways 1. The Socratic method has an AI prerequisite problem. You need existing knowledge to know what questions to ask, which means younger students are especially vulnerable to accepting AI output uncritically. Bette and Craig agree that junior/senior year of high school is roughly where the cognitive capacity for meaningful pushback begins. 2. AI dependency is already happening to experienced users. Craig describes a two-second panic when Claude froze mid-editorial. He recovered by remembering he could just write the way he always has. His concern: students who grew up with AI won’t have that muscle memory to fall back on. 3. The “helpful by default” design is a subtle problem. Craig raises the point that AI systems are programmed to be agreeable, which means they can lock students into a single mode of thinking without anyone noticing. The hallucinations get all the attention, but the quiet steering might be worse. 4. Policy chaos is the norm, not the exception. Teachers in the same hallway can have opposite AI rules. Bette recommends clarity above all: whatever your policy is, make it explicit. In K–12, she argues for uniform policies. In higher ed, where faculty governance complicates things, Craig’s approach works — require a policy, let faculty own the specifics. 5. Grace matters more than enforcement right now. Both Craig and Bette push back on the “AI cop” mentality. Students sometimes cross lines they didn’t know existed, just like past generations plagiarized without understanding citation rules. Teaching moments beat punitive responses, especially when the rules themselves are still being written. 6. Students use AI in ways faculty don’t expect. Companionship, personal problems, everyday questions, building apps. Bette’s observation: students are as likely to use AI for roommate conflicts as for essay writing. Faculty who don’t use AI themselves can’t begin to understand these patterns. 7. Education isn’t moving fast enough. New York got an AI bachelor’s program launched in fall 2025, which Bette calls “Mach speed for higher ed.” Most institutions are still in the resistance-or-denial phase. The shared worry: AI across the curriculum could become another empty checkbox, like ethics across the curriculum before it. Links Dr. Ludwig's website: https://www.betteludwig.com/ AI Can Do That Substack: https://betteconnects.substack.com/ AI Goes to College: https://www.aigoestocollege.com/ Craig's AI Goes to College Substack: https://aigoestocollege.substack.com/ Mentioned in this episode: AI Goes to College Newsletter