AI 中文总结
研究在多语言教育背景下，针对高阶问题生成的挑战，引入基于主张-证据-推理和发散性提问的提示，对比布鲁姆分类法。结果显示开源和专有模型能生成多语言问题，部分问题被教师认可为高阶，替代框架可补充布鲁姆分类法。
AI 中文摘要
批判性思维是帮助学习者超越简单记忆的基本技能，通过高阶提问可培养该技能，但教育工作者设计此类问题仍具挑战，课堂实践多依赖低阶问题。大语言模型在生成高阶问题方面能力较强，尤其在基于布鲁姆分类法的提示引导下。现有研究多集中于此框架且仅关注英语。本研究引入基于主张-证据-推理和发散性提问的提示，在多语言环境（巴斯克语、西班牙语和英语）中解决这些差距。结果表明，开源和专有模型能有效用三种语言生成问题，但教师仅将约一半可回答问题视为高阶问题。积极发现是，替代框架产生结构和概念各异的问题，可相互补充，为布鲁姆分类法提供可行替代方案。
英文摘要
Critical thinking is a fundamental skill that helps learners move beyond simple memorization. One way to develop this skill is through high-order questioning. However, crafting such questions remains a challenge for educators, and classroom practices tend to rely on low-order questions. Large Language Models have demonstrated strong capabilities in generating high-order questions, especially when guided by prompts based on Bloom's Taxonomy. Yet, existing research has largely centered on this framework and focused only on English. This study addresses these gaps by introducing prompts grounded in two alternative frameworks: Claim-Evidence-Reasoning and Divergent Questioning within a multilingual context using Basque, Spanish, and English. Results indicate that while both an open-source and a proprietary model rather effectively generate questions in all three languages, only about half of the answerable questions are recognized by teachers as high-order. A positive finding is that the alternative frameworks produce structurally and conceptually varied questions, suggesting they could complement each other and provide viable alternatives to Bloom's Taxonomy.
CommentsThis paper was accepted at the 15th edition of the Language Resources and Evaluation Conference (LREC 2026)
Journal refProceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026), pp. 760-769
