A Prompt-Driven Modular Framework for LLM-Based Agents in Scalable Interactive Learning Systems
Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105031133463
Journal Title
2025 5th International Conference on Advanced Algorithms and Neural Networks Aann 2025
Start Page
459
End Page
466
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SCOPUS
Bibliographic Citation
2025 5th International Conference on Advanced Algorithms and Neural Networks Aann 2025 (2025) , 459-466
Suggested Citation
Li Y., Kusakunniran W., Wiratsudakul A., Intagorn S. A Prompt-Driven Modular Framework for LLM-Based Agents in Scalable Interactive Learning Systems. 2025 5th International Conference on Advanced Algorithms and Neural Networks Aann 2025 (2025) , 459-466. 466. doi:10.1109/AANN66429.2025.11257597 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115549
Title
A Prompt-Driven Modular Framework for LLM-Based Agents in Scalable Interactive Learning Systems
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Author's Affiliation
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Abstract
This paper presents a prompt-driven modular architecture for integrating Large Language Model (LLM)-powered agents into adaptive learning systems. The framework enables real-Time, context-Aware dialogue and feedback through natural language configuration, eliminating the need for retraining or code changes. It features a four-layer design-covering environment control, agent logic, UI/UX, and feedback-implemented in a Unity-based learning application. A case study in medical education demonstrates how structured prompt templates support agent role assignment, behavior definition, and fallback strategies. Evaluation includes prompt reusability, dynamic role switching, and LLM accuracy comparison across multiple models, which show high correctness on domain-specific tasks. The system also supports low-cost deployment, making it feasible for scalable and accessible intelligent learning environments. This work offers a generalizable and configurable agent design method, contributing to the development of LLMintegrated tutoring systems and broader applications in natural language-driven interactive learning.