A DESIGN FRAMEWORK FOR PERSONALIZED INTELLIGENT TUTORING SYSTEMS INTEGRATING KNOWLEDGE TRACING AND RETRIEVAL-AUGMENTED GENERATION

This research presents a study on the design framework of an intelligent, personalized tutoring system to meet the individual needs of learners, overcoming the limitations of traditional learning management systems (LMS) that lack flexibility and brainstorming. The researchers designed a system architecture based on the Intelligent Tutoring System (ITS) concept, comprising seven main modules such as pre-assessment, intelligent learner grouping (AI-Classify), and blended learning, built on a cloud-based database structure. The developed system applies advanced techniques, including Retrieval-Augmented Generation (RAG) to increase accuracy and reduce AI hallucinations; Knowledge Tracing (KT) for continuous analysis of learner knowledge status; and an AI Chatbot to act as a continuous academic advisor. Expert evaluation of the conceptual framework's consistency showed an Index of Content Validity (IOC) for each module ranging from 0.89 to 0.97, with an average of 0.93, exceeding the acceptable criterion (≥ 0.50), indicating the appropriateness and consistency of the content with the research objectives. The overall system suitability assessment was at the highest level (x̅ = 4.71, S.D. = 0.45), and the technical suitability dimension of the AI innovation had a high average score of 4.73. In conclusion, this learning support system is designed to support deep learning, analytical thinking, and learner participation, with the potential to enhance educational practices aligned with 21st-century skills.