Psychologist exposes adoption assumption and other fallacies in pro-AI education debates

Social psychologist Daniel Stalder argues that pro-AI educators are using flawed rhetorical strategies that may undermine productive discussions about artificial intelligence in education. Writing in Psychology Today, Stalder identifies several logical fallacies commonly employed by AI advocates, including false dichotomies, straw man arguments, and false equivalences that oversimplify the complex challenges facing educators as AI cheating surges.

The big picture: As AI-powered cheating becomes increasingly prevalent in schools, the debate between pro-AI and anti-AI educators has intensified, but Stalder suggests that those advocating for AI integration are relying on persuasive but logically flawed arguments that obscure legitimate concerns about assessment integrity.

Key fallacies identified: Stalder highlights several common rhetorical mistakes made by AI proponents that weaken their arguments.
• False dichotomies: Pro-AI educators often frame choices as all-or-nothing propositions, such as claiming we must either “lean in or run away” from AI, when multiple approaches exist between these extremes.
• Straw man arguments: AI advocates sometimes mischaracterize opposition as believing AI will “destroy” education or that critics are “putting our heads behind the curtain” and hoping AI disappears.
• Is-ought fallacy: The argument that because AI is “here to stay,” educators must embrace it ignores that existence doesn’t mandate acceptance.

False equivalences problem: Many AI advocates compare current AI tools to calculators, arguing that since calculator concerns proved unfounded, ChatGPT worries are similarly overblown.
• Stalder argues this comparison fails because calculators didn’t replace conceptual understanding, while AI can easily bypass it entirely.
• The comparison between professors using AI in teaching and students using it for assignments also represents a false equivalence, since professors aren’t trying to earn degrees.

Assessment challenges overlooked: Pro-AI educators may be underestimating the fundamental assessment crisis created by AI availability in unproctored settings.
• “AI availability makes valid and reliable assessment all but impossible” in take-home and online graded work, according to Stalder.
• Educators must not only teach but also fairly assess whether students have actually learned the material and are prepared for advanced coursework or employment.

The cognitive bias angle: Stalder suggests that educators closest to the AI crisis may be experiencing cognitive dissonance or normalcy bias, similar to how disaster-area residents often underestimate environmental threats.
• This psychological phenomenon could explain why some educators may not fully recognize the depth of assessment challenges posed by AI.

What’s at stake: The integrity of educational assessment systems may be fundamentally compromised by AI availability, potentially requiring a shift to fully proctored assessments as the only reliable evaluation method.