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arXiv · 2412.00970

Generating AI Literacy MCQs: A Multi-Agent LLM Approach

Jiayi Wang·Ruiwei Xiao·Ying-Jui Tseng
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Abstract

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.

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Jiayi Wang, Ruiwei Xiao, Ying-Jui Tseng. 2024-12-01. Generating AI Literacy MCQs: A Multi-Agent LLM Approach. https://doi.org/10.1145/3641555.3705189

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