Modeling an Adaptive and Collaborative E-Learning System with Artificial Intelligence Tools - Zargane, Kawtar; Kemouss, Hassane; Khaldi, Mohamed (International journal of advanced computer science & applications, 2025)
In an educational environment that is undergoing digital transformation, the need to create smarter, learner-centred learning environments is becoming increasingly urgent. This article presents a conceptual modeling of an e-learning system that integrates the adaptive and collaborative dimensions, relying on the tools of artificial intelligence (AI), which occupies a central place, both as a dynamic adaptation engine and as a collaboration facilitator and automaton of certain pedagogical activities. This methodical and structured approach makes it possible to develop a hybrid environment capable of adjusting to individual needs while promoting the co-construction of knowledge between peers. Based on instructional design principles and the 2TUP (Two Tracks Unified Process) process, this approach aims to develop a systematic architecture, illustrated by UML (Unified Modeling Language) diagrams, of classes, use cases, activities and sequences, integrating AI (Artificial Intelligence) through adaptive learning, conversational agents and intelligent tutoring systems that make it possible to personalize learning, Provide targeted feedback, optimize learner performance, and guide learners more accurately. This combination of standardized modeling and AI improves the synergy between stakeholders and increases the efficiency of online learning environments. Finally, this model paves the way for a new era of more flexible, inclusive and responsive techno-pedagogical systems, capable of facing the contemporary challenges of online training.