LLMs show stable performance in Bloom's taxonomy question classification

Researchers have evaluated the effectiveness of Large Language Models (LLMs) for classifying assessment questions according to Bloom's taxonomy, a task that can significantly reduce instructor workload. Traditional supervised machine learning and deep learning models showed a substantial drop in performance when applied to datasets they were not trained on. In contrast, LLMs demonstrated more stable performance across different datasets, suggesting they are a more robust option for this task. The study also introduced a user-friendly interface to assist instructors in classifying question banks, which was found to be highly usable and required minimal effort. AI

IMPACT LLMs offer a more generalizable solution for educational question classification, potentially reducing instructor workload and improving assessment consistency.

RANK_REASON Academic paper presenting novel research findings on LLM performance in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Bloom's taxonomy
• LLMs
• Mohammadreza Molavi

• paper
• product

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