『AI Goes to College』のカバーアート

AI Goes to College

AI Goes to College

著者： 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
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  • What students actually do with AI

    What students actually do with AI
    2026/07/13
    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 分
  • 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
    2026/06/18
    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 分
  • AI's Underused Capabilities and Hidden Risks

    AI's Underused Capabilities and Hidden Risks
    2026/05/18
    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 分
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