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.

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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.

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