From Memorization to Creation: Evaluating the Cognitive Depth of LLM-Generated Educational Questions
While LLMs show promise in automating educational content creation, their ability to generate questions that stimulate higher-order thinking remains understudied. This work evaluates six widely-used LLMs through a Bloom's Taxonomy lens, focusing on their capacity to transcend rote memorization and achieve cognitive leaps. Using a hybrid human--AI evaluation protocol, we generate and analyze 20{,}700 questions across computer science, K--12 math, and social-science domains. Key contributions include: (1) a fine-grained prompting strategy that reduces question repetitiveness by 24.45\% for Qwen2.5-7B-Instruct, and increases the proportion of higher-order cognitive level outputs by 11.53\% f...
| Authors | Xiaolong Wang, Zhe Zhao, Song Lai et al. |
|---|---|
| Primary category | cs.HC |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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