From the 1 of 5 linked papers with an AI index.
5 papers
AI-based scoring systematically underestimates conceptual understanding of linguistically weak students' explanations in physics
Markus S. Feser, Paul L. Tschisgale
The study evaluates AI-based scoring of secondary students' physics explanations and finds that these systems systematically underestimate conceptual understanding for explanations…
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…
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…
Evaluating NLP Embedding Models for Handling Science-Specific Symbolic Expressions in Student Texts
Tom Bleckmann, Paul Tschisgale
In recent years, natural language processing (NLP) has become integral to educational data mining, particularly in the analysis of student-generated language products. For research…
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…