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AI Goes to College

AI Goes to College

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

    Assessment and Community in the AI Age: A Conversation with Dr. Bill McCumber
    Sep 29 2026
    Episode 39: Assessment and Community in the Age of AIHow 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.OverviewBill 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 hearWhy 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 coachingBill'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 mentionedMIT 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.pdfSocrates' 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-5-1Previous 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 5Questions for reflectionIf 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 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 ...
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    54 min
  • Building AI Literacy in Higher Education: Programs, Practice, and Community

    Building AI Literacy in Higher Education: Programs, Practice, and Community
    Aug 25 2026
    Episode 38: Building AI Programs While the Ground Is Still MovingShould 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 hearWhy 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 problemThe 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 highlightsTimestamps 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 ...
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    1 ora e 7 min
  • What students actually do with AI

    What students actually do with AI
    Jul 13 2026
    Episode 37: AI as the Learned Colleague, Not the GhostwriterWhat 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 hearWriting 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 mentionedHigher 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 rolesAn 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 workGrok (xAI), used by Rob to draft a family lease agreementCodex (OpenAI), used by Craig as a writing collaborator for a conference paperMicrosoft Copilot (M365), used by Rob for document review and editing feedbackChatGPT Scheduled Tasks and its predecessor, ChatGPT Pulse (OpenAI)Claude Code routines and Codex automations, comparison points for scheduled AI briefingsNotebookLM (Google), including its short-video and slide-editing featuresAI Resilient Learning Activities Database, an upcoming free repository from AI Goes to College, funded...
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    50 min
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