3 papers
physics.ed-ph2026
AI-based scoring systematically underestimates conceptual understanding of linguistically weak students' explanations in physics
Markus S. Feser, Paul L. Tschisgale
Explaining physical phenomena is central to physics learning, because students' explanations provide evidence of their conceptual understanding. Because conceptual understanding ca…
stat.AP2026
Daily and Weekly Periodicity in Large Language Model Performance and Its Implications for Research
Paul Tschisgale, Peter Wulff
Large language models (LLMs) are increasingly used in research as both tools and objects of study. Much of this work assumes that LLM performance under fixed conditions (identical…
physics.ed-ph2025
Evaluating GPT- and Reasoning-based Large Language Models on Physics Olympiad Problems: Surpassing Human Performance and Implications for Educational Assessment
Paul Tschisgale, Holger Maus, Fabian Kieser +3
Large language models (LLMs) are now widely accessible, reaching learners at all educational levels. This development has raised concerns that their use may circumvent essential le…