High-Order Question Generation in a Multilingual Educational Context
For educators in multilingual settings, this work provides alternative frameworks for generating high-order questions, though the recognition rate by teachers is only about 50%.
The study introduces prompts based on Claim-Evidence-Reasoning and Divergent Questioning frameworks to generate high-order questions in Basque, Spanish, and English using LLMs. Results show that about half of answerable questions are recognized as high-order by teachers, and alternative frameworks produce varied questions that can complement Bloom's Taxonomy.
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.