13 citations · 14 across the 4 of their papers we have counts for
5 papers
Artificial Intelligence Driven Physics Assignments using Context Prompts
Miguel Rodriguez, Peter Wulff
Generative artificial intelligence (Gen AI) is increasingly shaping how students access information and engage with physics content, yet many instructional approaches still assume…
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…
Report on the Scoping Workshop on AI in Science Education Research 2025
Marcus Kubsch, Marit Kastaun, Peter Wulff +21
This report summarizes the outcomes of a two-day international scoping workshop on the role of artificial intelligence (AI) in science education research. As AI rapidly reshapes sc…
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…