CODE-GEN: A Human-in-the-Loop RAG-Based Agentic AI System for Multiple-Choice Question Generation
📰 ArXiv cs.AI
CODE-GEN is a human-in-the-loop RAG-based agentic AI system for generating multiple-choice questions to develop student code reasoning and comprehension abilities
Action Steps
- Employ a human-in-the-loop approach to ensure generated questions are accurate and relevant
- Utilize a retrieval-augmented generation (RAG) framework to generate multiple-choice questions
- Implement an agentic AI architecture with Generator and Validator agents to produce and assess questions
- Integrate course-specific learning objectives to align generated questions with educational goals
Who Needs to Know This
AI engineers and educators on a team can benefit from CODE-GEN as it generates context-aligned multiple-choice questions, and educators can use it to develop student code reasoning and comprehension abilities
Key Insight
💡 CODE-GEN's human-in-the-loop RAG-based approach can effectively generate context-aligned multiple-choice questions for developing student code reasoning and comprehension abilities
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🤖 CODE-GEN: AI-powered multiple-choice question generation for coding comprehension 📚
Key Takeaways
CODE-GEN is a human-in-the-loop RAG-based agentic AI system for generating multiple-choice questions to develop student code reasoning and comprehension abilities
Full Article
Title: CODE-GEN: A Human-in-the-Loop RAG-Based Agentic AI System for Multiple-Choice Question Generation
Abstract:
arXiv:2604.03926v1 Announce Type: new Abstract: We present CODE-GEN, a human-in-the-Loop, retrieval-augmented generation (RAG)-based agentic AI system for generating context-aligned multiple-choice questions to develop student code reasoning and comprehension abilities. CODE-GEN employs an agentic AI architecture in which a Generator agent produces multiple-choice coding comprehension questions aligned with course-specific learning objectives, while a Validator agent independently assesses conte
Abstract:
arXiv:2604.03926v1 Announce Type: new Abstract: We present CODE-GEN, a human-in-the-Loop, retrieval-augmented generation (RAG)-based agentic AI system for generating context-aligned multiple-choice questions to develop student code reasoning and comprehension abilities. CODE-GEN employs an agentic AI architecture in which a Generator agent produces multiple-choice coding comprehension questions aligned with course-specific learning objectives, while a Validator agent independently assesses conte
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