A multi-agent AI classroom based on dual-process reasoning hazards: a pilot with prospective physics teachers

Synthesis: Tufino (2026) pilots a simulated multi-agent AI classroom where five AI students each enact distinct dual-process theory (DPT) reasoning hazards, giving prospective physics teachers rare practice in responding to authentic student reasoning. Fifteen graduate students showed significant improvement in diagnostic scores (p=0.014, r=0.79), but during the simulation itself used predominantly uniform guiding questions — revealing a knowing-doing gap on the developmental trajectory toward responsive teaching.

Responding productively to authentic student reasoning is among the most difficult teaching skills to develop, and prospective teachers get few opportunities to practice it. This pilot study created a simulated class of five AI students, each consistently enacting a distinct dual-process theory reasoning hazard. Fifteen graduate students in a physics teacher preparation course diagnosed vignettes before/after interacting with the simulated class, showing significant diagnostic improvement — but their actual questioning during the simulation revealed a gap between knowing DPT vocabulary and applying it in real-time.

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Tufino, E. (2026). A multi-agent AI classroom based on dual-process reasoning hazards: a pilot with prospective physics teachers.