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Benchmarking Educational LLMs with Analytics: A Case Study on Gender Bias in Feedback

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM
NeutralArtificial Intelligence
The study on benchmarking educational large language models (LLMs) addresses the critical issue of gender bias in feedback mechanisms. By analyzing 600 authentic student essays from the AES 2.0 corpus, researchers employed an embedding-based framework to detect biases through implicit and explicit cues. The findings indicate that implicit gender cues lead to more pronounced semantic shifts in responses compared to explicit cues, particularly in models like GPT and Llama. This research underscores the importance of evaluating AI tools in educational settings, as biases in feedback can significantly affect student learning experiences and outcomes. As educators increasingly integrate GenAI into their practices, understanding these biases is essential for fostering equitable learning environments.
— via World Pulse Now AI Editorial System
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