A Framework for Integrating Intelligent Conversational Chatbots into Learning Management Systems (MOODLE) to Im-prove Teaching Learning Evaluation Pro-cess

Abstract

Problem Statement: The potential for improving the teaching and learning assessment process in Learning Management Systems (LMS) like MOODLE through the integration of conversational chatbots powered by AI is yet largely untapped. Research Method and Design: This study presents a comprehensive framework that is well-organized to make it easier to incorporate intelligent chatbots into LMS. The approach follows a sequential procedure that includes gathering user requests, applying Natural Language Understanding (NLU), producing and choosing responses, and controlling discourse. Major Findings: When properly applied, the proposed framework can greatly enhance the process of evaluating teaching and learning. The chatbot's abilities to manage repetitive activities, give personalized learning resources, monitor student progress, and offer insights into student sentiment and engagement have all been improved. Conclusions: Effective error and ambiguity management techniques, regular monitoring, and updates, as well as high-quality initial training data all play a significant role in the framework's performance. This study lays the groundwork for further investigation into more complex cognitive services, improved entity and intent extraction, and sophisticated dialogue management techniques.