arXiv:2605.26819v1 Announce Type: cross Abstract: We present RAGEAR (Retrieval-Augmented Graph-Enhanced Academic Recommender), a neurosymbolic recommender system for academic course recommendation. RAGEAR combines dense retrieval over full lecture transcripts with a symbolic Knowledge Graph modelling courses, lessons, transcript chunks, credits, study plans, and curricular information. The Knowledge Graph supports symbolic filtering and contextualisation based on structured constraints, such as credits, academic disciplines, study plans, and prerequisites. Unlike metadata-based approaches, it
The proliferation of advanced AI techniques, particularly neurosymbolic approaches and retrieval-augmented generation, is enabling more sophisticated and context-aware recommender systems.
This development indicates a move towards more intelligent and personalized learning experiences, potentially revolutionizing education and professional development platforms.
Academic recommendation systems can now move beyond simple metadata matching to provide highly contextualized suggestions based on detailed course content and structured curricular knowledge.
- · Educational institutions
- · E-learning platforms
- · Students
- · AI-driven education companies
- · Traditional, metadata-only recommender systems
- · Generic online course aggregators
More effective and personalized academic pathways for students and professionals.
Increased engagement and completion rates in online learning, leading to a more skilled workforce.
The application of similar neurosymbolic, graph-enhanced retrieval approaches across other complex domains beyond education.
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Read at arXiv cs.AI