Generative artificial intelligence and physics education: what is changing, and how fast
Year: 2026
Type: preprint
Abstract: Generative artificial intelligence solves and “explains” university-level physics, and its capabilities are improving in a regular, predictable way. This undermines an assumption on which much of our teaching rests: that a correct answer is evidence of reasoning and understanding. In this paper we argue that the response to this rupture is not a new technology, but the practices that physics education research (PER) has advocated for decades: a focus on process, inquiry-based laboratory work, and the assessment of reasoning. One disposition becomes even more essential: epistemic vigilance, that is, treating AI outputs as hypotheses to be tested. We discuss what AI can do in physics today, what its scores actually measure, what we risk losing, and what, in concrete terms, is worth learning for those who teach and those who learn.
Author: Tufino, Eugenio
Institution: University of Modena and Reggio Emilia
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7224015316