Optimizing Language Model-Based Educational Assistants Using Knowledge Graphs: Integration With Moodle LMS - William Villegas-Ch; Jaime Govea; Rommel Gutierrez (IEEE Access)
Chatbots in educational settings have grown significantly, facilitating interaction between students and learning platforms. However, current systems, such as Rasa, Moodle Integrated Chatbots, and ChatterBot, present significant limitations in precision, adaptability, and response time, affecting their effectiveness in resolving academic queries and personalizing learning. To address these shortcomings, this work proposes the development of an advanced educational chatbot that combines large language models (LLMs) with knowledge graphs, allowing for more accurate and contextualized responses and offering valuable suggestions to enrich the learning process. The system is evaluated based on its ability to adjust to different student profiles and offer fast and accurate responses. The results show that the proposed chatbot achieves a precision of 85%, outperforming Rasa and ChatterBot, which achieved accuracies of 83% and 81%, respectively. Furthermore, the chatbot reduces response times to 0.41 seconds, improving efficiency compared to other solutions. The system also demonstrates adaptability, effectively adjusting to students’ learning styles and academic levels. This work indicates that knowledge graph integration and hyperparameter optimization are crucial to improving educational chatbots’ precision, speed, and adaptability, presenting an innovative solution that overcomes the limitations of current systems.