13 citations · 13 across the 3 of their papers we have counts for
3 papers · 1 filter
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…
Developing an LLM-Based Feedback System Grounded in Evidence-Centered Design to Support Physics Problem Solving
Holger Maus, Fabian Kieser, Stefan Petersen +2
Generative AI offers new opportunities for individualized and adaptive learning, e.g., through large language model (LLM)-based feedback systems. While LLMs can produce factually c…
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…