Xiaolong Wang, Zhe Zhao, Song Lai, Chaoli Zhang, Zijie Geng, Yu Tong, Ye Wei, Qingsong Wen (2026) — Institution.
LLM-generated educational questions show varying cognitive depth; models excel at factual recall but struggle with higher-order thinking questions per Bloom's taxonomy.
Synthesis
From Memorization to Creation: Evaluating the Cognitive Depth of LLM-Generated Educational Questions investigates llm-generated educational questions show varying cognitive depth; models excel at factual recall but struggle with higher-order thinking questions per bloom's taxonomy. This work connects to existing research on a4l-analytics-pipeline by demonstrating that Abstract: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 memor....
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- a4l-analytics-pipeline - ai-literacy-assessment-misalignment - metacognitive-learning-scenarios-taxonomy - agentic-ai-ecosystems-higher-education - writing-educationCitation
APA: Xiaolong Wang, Zhe Zhao, Song Lai, Chaoli Zhang, Zijie Geng, Yu Tong, Ye Wei, Qingsong Wen (2026). From Memorization to Creation: Evaluating the Cognitive Depth of LLM-Generated Educational Questions. arXiv:2606.18257.