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Evaluating GPT- and reasoning-based large language models on Physics Olympiad problems: Surpassing human performance and implications for educational assessment

Paul Tschisgale1, Holger Maus1, Fabian Kieser2, Ben Kroehs3, Stefan Petersen1, and Peter Wulff4

Superstudent intelligence in thermodynamics

Rébecca Loubet, Pascal Zittlau, Marco Hoffmann, Luisa Vollmer, Sophie Fellenz, Heike Leitte, Fabian Jirasek, Johannes Lenhard, and Hans Hasse

Phys. Educ. 61 025004 (2026)

First-class AI? Assessing LLMs on undergraduate physics exams

Ben Dowsett, Martin P Rey, and Michele Pizzochero

Phys. Educ. 61 033007 (2026)

Semiotic problem framing: a new framework to guide students and teachers in conceptual understanding and teaching of physics

M Tuveri, A Steri, and V Fanti

Eur. J. Phys. 47 015712 (2026)

How physics professors use and frame generative AI tools

Vidar Skogvoll and Tor Ole Odden

Eur. J. Phys. 47 025713 (2026)

Olympiad-Level AI Performance Is Here—What Now?

Paul Tschisgale

Physics 18 147 (2025)

Multimodal large language models and physics visual tasks: comparative analysis of performance and costs

Giulia Polverini and Bor Gregorcic

Eur. J. Phys. 46 055708 (2025)

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