Every field going through a technology moment eventually needs someone to ask the unglamorous question: what, exactly, is being automated, and is there any real evidence it belongs in the room? That's the job Øyvind Førland Standal takes on in his new article, "AI Hype: A Critical Approach to Artificial Intelligence in Physical Education," published in Sport, Education and Society. On a recent Article Club episode of the podcast, I sat down with Dr. Chad Killian (University of New Hampshire) to work through it. The conversation ended up being less about whether AI belongs in the gym and more about what we're actually asking it to replace.
The Evidence Problem
Standal starts from a paradox: some scholars treat AI in PE as unexplored territory, while systematic reviews already count well over 100 publications on the topic. His answer to that tension is the concept of "AI hype," borrowed from scholars like Neil Selwyn and Emily Bender — not a rejection of the technology, but a call to slow down and ask two questions: what is actually being automated, and is the evidence behind the claims even relevant to school-based PE? When Standal traces that evidence, most of it turns out to come from Chinese engineering and computer science journals studying university students, with almost nothing on kids in actual school PE programs. He also surfaces a retracted paper that keeps getting cited anyway — a detail that led to a genuinely useful side conversation between Chad and me about citation practices in our field. AI tools are happy to hand a researcher a DOI and a confident-sounding claim; they're not going to notice that the source has been formally retracted, or that a claim has been stretched well past what the cited study actually says. That's not an argument against using AI for lit searches — it's an argument for reading the paper before you cite it.
Teaching Is a Series of Decisions
The part of the conversation with the most direct classroom relevance was Chad's framing of teaching as, at its core, a series of decisions. If a program or teacher outsources curricular and instructional decisions — what to to teach, what and how to assess, how to sequence a unit, how to respond to a student in the moment — to a bot, the teacher becomes what Chad called "a delivery drone" for machine-driven pedagogy. That is essentially just “rolling out the bots.” That's worth sitting with regardless of where you land on AI generally, because it reframes the question away from "should we use this tool" and toward "who or what is driving the pedagogical decisions in this classroom, and why."
Chad also pointed to something he called invisible adoption: PE teachers may not be using AI visibly, in front of students, to assess skills or run drills. They're using it quietly on the back end — to write curriculum, build unit plans, draft assessments — because it's fast and everyone would rather go get to practice or get home for the summer. That's a different risk profile than the robots Standal's article opens with, and arguably a more immediate one for PETE programs to think through with candidates.
What This Means for Practice and Policy
None of this is a case for banning AI from PE (yet?). Both Standal's article and our conversation land somewhere more useful: stop assuming AI's arrival is inevitable, and start asking whether a specific tool actually improves the educational outcomes you're trying to foster. For practitioners, that means treating AI the way you'd treat any other unproven instructional resource — useful for saving time on planning and paperwork, but not a substitute for the professional judgment that decides what happens on the gym floor. For policymakers and administrators, it means resisting the pitch that AI-supported instruction justifies larger class sizes or fewer certified teachers; the research base for that claim in K-12 PE simply doesn't exist yet. And for anyone training future teachers, it means being explicit that AI-literacy instruction isn't the same as AI-adoption cheerleading — teaching candidates to interrogate a tool's evidence base is itself part of the job.
Standal closes his article as an "interested critic" rather than an opponent, and that might be the right posture for a field that has been burned before by technology promises that outran the evidence — smartwatches, apps, one-to-one devices — most of which never achieved the uptake their advocates predicted. AI may be different. But the burden of proof, as it should be, sits with the claim, not with the skepticism.
Standal, Ø. F. (2026). AI Hype: A Critical Approach to Artificial Intelligence in Physical Education. Sport, Education and Society.
This blog post was written with the assistance of AI to support clarity and accessibility. It is intended to help disseminate and discuss research findings with a broader audience. However, for the most accurate and reliable information—including conclusions and practical applications—please refer to the original peer-reviewed publication on which this blog is based. The peer-reviewed article remains the most authoritative source.