Learning Management Systems (LMSs) remain central to digital education, but they still provide limited support for adaptive and personalized learning across heterogeneous platforms. This study proposes an interoperable smart learning architecture that integrates a Multi-Agent System (MAS), generative artificial intelligence, and learning analytics to support context-aware interventions while preserving LMS independence. Methodologically, the work follows a design-oriented research approach based on architectural modeling and scenario-based validation. The proposed framework combines Learning Tools Interoperability (LTI) 1.3, the Experience API (xAPI), Sharable Content Object Reference Model (SCORM), a Learning Record Store (LRS), and an asynchronous Extensible Messaging and Presence Protocol (XMPP)/JavaScript Object Notation (JSON) communication bus to connect intelligent services with existing LMS environments. The architecture includes tutor, assessment, recommendation, monitoring, collaboration, and profile agents coordinated through a microservices-based design. Its functional coherence is illustrated through four representative scenarios covering dropout-risk detection, targeted remediation, teacher dashboards with grade return, and collaborative feedback. The main contribution is a modular and standards-based architecture that connects analytics, agent-based orchestration, and generative AI within a closed-loop adaptation process for scalable smart learning environments.
Khaddar et al. (Thu,) studied this question.