Literature Review: Personalized Learning Recommendation System in Educational Scenarios: XAI-Driven Student Behavior Understanding and Teacher Collaboration Mechanism

Abstract

This literature review delves into personalized learning recommendation systems (PLRSs) within educational contexts. It places a significant emphasis on the understanding of student behavior that is driven by Explainable AI (XAI). Additionally, it focuses on the mechanisms of teacher collaboration.

The traditional educational models are not without their drawbacks. These limitations have instigated a transition towards personalized learning. This movement has, in turn, propelled the development of PLRSs. These systems are designed with the dual objectives of boosting learning efficiency and enhancing learning outcomes. To accomplish these goals, they offer customized learning resources and strategies.

There are key trends. One trend is dynamic and adaptive recommendation strategies. Another trend is the use of explainable AI (XAI). XAI builds trust. XAI also builds transparency. In - depth student behavior understanding is a trend. Performance modeling is also a trend. Advanced content understanding is a trend. Semantic analysis is a trend as well. The application of collaborative filtering is a trend. The application of hybrid approaches is a trend. The emphasis on teacher collaboration is a trend. The emphasis on human - AI interaction is a trend too.

Dynamic systems can adapt to students' changing needs. XAI makes AI - driven recommendations understandable and trustworthy. Precise student models improve the relevance of recommendations. Semantic analysis of educational content does the same. Hybrid approaches enhance the performance of collaborative filtering. Teacher - AI collaboration is important for the effective implementation of PLRSs.

However, there are several challenges. Future research should focus on different things. It should develop more comprehensive student models. It should enhance XAI techniques for educational contexts. It should empirically study the impact of XAI on learning outcomes. It should design effective teacher collaboration mechanisms. It should create human - AI interaction strategies. It should address ethical issues and fairness in personalized learning. It should explore the integration of multimodal data and learning analytics.

By dealing with these challenges, PLRSs can be optimized. This can create more intelligent, transparent, and human - centered personalized learning environments. In the end, this will enhance learning outcomes. It will also empower students and educators.