5 papers · 1 filter
Testing the Validity of Embedding-Based Similarity and Clustering for Handwritten Physics Solutions
Maike Tauschhuber, Gerd Kortemeyer
Text embeddings are increasingly used in physics education research to organize, compare, and cluster large collections of written text. Their appeal is clear: once student respons…
When AI Evaluates Its Own Work: Validating Learner-Initiated, AI-Generated Physics Practice Problems
Tobias Geisler, Gerd Kortemeyer
Large language models (LLMs) can now generate physics practice problems in real time, yet the educational value of these items hinges on rapid, reliable post-generation vetting. In…
The Boiling-Frog Problem of Physics Education
Gerd Kortemeyer
It is astonishing how rapidly general-purpose AI has crossed familiar thresholds in introductory physics. Comparing outputs from successive models, GPT-5 Thinking moves far beyond…
Assessing Confidence in AI-Assisted Grading of Physics Exams through Psychometrics: An Exploratory Study
Gerd Kortemeyer, Julian Nöhl
This study explores the use of artificial intelligence in grading high-stakes physics exams, emphasizing the application of psychometric methods, particularly Item Response Theory…
Grading Assistance for a Handwritten Thermodynamics Exam using Artificial Intelligence: An Exploratory Study
Gerd Kortemeyer, Julian Nöhl, Daria Onishchuk
Using a high-stakes thermodynamics exam as sample (252~students, four multipart problems), we investigate the viability of four workflows for AI-assisted grading of handwritten stu…