COLLABORATIVE MULTI-AGENT CHATBOT FRAMEWORK FOR INTELLIGENT TUTORING AND FEEDBACK
Description
However, traditional single-agent chatbots are susceptible to technological hallucination, non-consistent pedagogical depth, and lack of a verification system, as they usually offer ready-made answers without promoting conceptual thinking. This paper suggests a Collaborative Multi-Agent Chatbot Framework that will facilitate intelligent tutoring and feedback of high quality for Java programming. In particular, we propose to use a modular design with specialized agents, such as a generative tutor agent and a verification agent who is tasked with verifying the generated answers. The verifier uses real-time execution of JUnit 5 tests to validate hints generated by other agents. As far as we know, this is one of the first attempts to use test-based verification in the process of LLM tutoring. The proposed system was tested using 15 selected Java programming problems that covered basic
algorithms and object-oriented programming principles. The experimental findings indicate that the new method is considerably superior to the single-agent models, with 90% success rate. Although the multi-agent architecture may result in longer response times, it requires fewer interaction iterations, thus providing a more productive and educational experience.
Files
Deutsche internationale Zeitschrift für zeitgenössische Wissenschaft, №126 2026-142-147.pdf
Files
(612.4 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:cb345b2fc0733992f1f5ad3e1785f836
|
612.4 kB | Preview Download |
Additional details
References
- 1. J. Li et al., "More Agents Is All You Need," arXiv preprint arXiv:2402.05120, 2024. E. Huang et al., "AgentCoder: Multi-Agent based Code Generation with Iterative Testing and Op timisation," arXiv preprint arXiv:2312.13010, 2023. 2. L. Wang et al., "Mixture-of-Agents Enhances Large Language Model Capabilities," arXiv preprint arXiv:2406.04692, 2024. 3. X. Zhang et al., "Self-Adaptive Large Lan guage Model (LLM)-Based Multiagent Systems," arXiv preprint arXiv:2307.06187, 2023. 4. Y. Sun et al., "Adapting LLM Agents with Universal Feedback in Communication," arXiv pre print arXiv:2310.01444, 2023.
- 5. Y. Wu et al., "Chain of Agents: Large Lan guage Models Collaborating on Long-Context Tasks," arXiv preprint arXiv:2406.02818, 2024. 8. R. C. Cardoso and A. Ferrando, "A review of agent-based programming for multi-agent systems," Computers, vol. 10, no. 2, p. 16, 2021. 6. T. Wang et al., "Language Agents as Optimi zable Graphs," arXiv preprint arXiv:2402.16823, 2024. 7. Z. Wang et al., "XUAT-Copilot: Multi-Agent Collaborative System for Automated User Acceptance Testing with Large Language Model," arXiv preprint arXiv:2401.02705, 2024. 9. Jiang et al., "Large Language Model En hanced Multi-Agent Systems for 6G Communica tions," arXiv preprint arXiv:2312.07850, 2023.