A tool-augmented dual-agent RAG system generates and validates coding MCQs, reaching 79.9–98.6% human-validated success and showing AI handles computational checks while humans remain essential for pedagogical depth.
Generating AI Literacy MCQs: A Multi-Agent LLM Approach
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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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cs.AI 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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CODE-GEN: A Human-in-the-Loop RAG-Based Agentic AI System for Multiple-Choice Question Generation
A tool-augmented dual-agent RAG system generates and validates coding MCQs, reaching 79.9–98.6% human-validated success and showing AI handles computational checks while humans remain essential for pedagogical depth.