EduTwin AI A Learner-Centric Digital Twin and Retrieval-Augmented Generation platform for Personalized Higher Education
No Thumbnail Available
Date
2026
Journal Title
Journal ISSN
Volume Title
Publisher
Faculty of Sciences
Abstract
The rapid evolution of digital education has exposed the limitations of traditional, "onesize-
fits-all" learning management systems, highlighting an urgent need for intelligent,
adaptive environments. This thesis presents EduTwin AI, an innovative platform designed to
bridge the gap between static academic resources and the dynamic cognitive state of the
learner. By natively integrating a Learner-Centric Digital Twin with a Retrieval-Augmented
Generation (RAG) architecture, the system creates a continuous physical-digital feedback
loop. The Digital Twin employs Bayesian Knowledge Tracing (BKT) to autonomously model
and predict the student's concept mastery in real time. Simultaneously, the RAG pipeline acts
as a strict pedagogical filter, ensuring that all interactions generated by the Large Language
Model (LLM) are exclusively anchored to validated course materials, thereby eliminating AI
hallucinations. Through adaptive assessments, personalized study paths, and a
conversational tutor, EduTwin AI dynamically calibrates its complexity to match the
student's Zone of Proximal Development. This work demonstrates the profound potential of
combining real-time cognitive tracking with generative AI to deliver a truly personalized,
secure, and academically rigorous learning experience.