Yasemin Gulbahar

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2025

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Automatic Grading of Student Work Using Simulated Rubric-Based Data and GenAI Models
Yiyao Yang | Yasemin Gulbahar
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress

Grading assessment in data science faces challenges related to scalability, consistency, and fairness. Synthetic dataset and GenAI enable us to simulate realistic code samples and automatically evaluate using rubric-driven systems. The research proposes an automatic grading system for generated Python code samples and explores GenAI grading reliability through human-AI comparison.
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