Design and Implementation of an Intelligent Chatbot Using Large Language Model for Consistent Learning Support

This study developed an AI-enabled lecturer assistant system powered by Large Language Models (LLMs) to address challenges in the higher institutions, particularly high student-to-lecturer ratios and scheduling conflicts that hinder effective communication and timely academic support. An AI-driven chatbot platform was designed to provide continuous access to academic resources and personalized responses to student inquiries. Lecturers uploaded course materials, which the chatbot utilized through a retrieval-based model to generate context-aware explanations. MongoDB was employed to store interaction histories, enabling the system to adapt based on student feedback and learning patterns. The implemented system successfully delivered accurate, detailed responses aligned with academic content, improved accessibility to educational support, and enhanced learning efficiency through immediate feedback. It also reduced repetitive administrative tasks for lecturers, thereby allowing them to focus more on teaching and mentoring. The findings demonstrated the transformative potential of integrating advanced AI technologies into higher education, contributing to improved student engagement, equitable access to resources, and reduced instructional burdens. Overall, the study highlighted the societal impact of scalable, inclusive, and efficient AI-driven educational tools in fostering interactive and technologically adept learning environments

Chatbot, LLM, Learning, AI-driven, MongoDB

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