Generating AI Literacy MCQs: A Multi-Agent LLM Approach
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929609719676928 |
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| author | Wang, Jiayi Xiao, Ruiwei Tseng, Ying-Jui |
| author_facet | Wang, Jiayi Xiao, Ruiwei Tseng, Ying-Jui |
| contents | Artificial intelligence (AI) is transforming society, making it crucial to prepare the next generation through AI literacy in K-12 education. However, scalable and reliable AI literacy materials and assessment resources are lacking. To address this gap, our study presents a novel approach to generating multiple-choice questions (MCQs) for AI literacy assessments. Our method utilizes large language models (LLMs) to automatically generate scalable, high-quality assessment questions. These questions align with user-provided learning objectives, grade levels, and Bloom's Taxonomy levels. We introduce an iterative workflow incorporating LLM-powered critique agents to ensure the generated questions meet pedagogical standards. In the preliminary evaluation, experts expressed strong interest in using the LLM-generated MCQs, indicating that this system could enrich existing AI literacy materials and provide a valuable addition to the toolkit of K-12 educators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00970 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generating AI Literacy MCQs: A Multi-Agent LLM Approach Wang, Jiayi Xiao, Ruiwei Tseng, Ying-Jui Human-Computer Interaction Artificial intelligence (AI) is transforming society, making it crucial to prepare the next generation through AI literacy in K-12 education. However, scalable and reliable AI literacy materials and assessment resources are lacking. To address this gap, our study presents a novel approach to generating multiple-choice questions (MCQs) for AI literacy assessments. Our method utilizes large language models (LLMs) to automatically generate scalable, high-quality assessment questions. These questions align with user-provided learning objectives, grade levels, and Bloom's Taxonomy levels. We introduce an iterative workflow incorporating LLM-powered critique agents to ensure the generated questions meet pedagogical standards. In the preliminary evaluation, experts expressed strong interest in using the LLM-generated MCQs, indicating that this system could enrich existing AI literacy materials and provide a valuable addition to the toolkit of K-12 educators. |
| title | Generating AI Literacy MCQs: A Multi-Agent LLM Approach |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2412.00970 |