Knowledge Augmented Generation for Curriculum Planning

Kozhanov, Murat and Makó, Csaba and Póser, Valéria and Eigner, György and Mosavi, Amirhosein (2026) Knowledge Augmented Generation for Curriculum Planning. EURASIAN JOURNAL OF MATHEMATICAL AND COMPUTER APPLICATIONS, 14 (1). pp. 17-34. ISSN 2306-6172

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Abstract

Interdisciplinary education is positioned as a strategic response to complex technological and societal challenges. However, outcome-driven synthesis of complete study plans remains difficult to scale under heterogeneous competency standards and strict academic regulations. Retrieval-Augmented Generation (RAG) improves factual grounding by retrieving external evidence, while Knowledge-Augmented Generation (KAG) extends RAG by integrating structured knowledge representations for relational reasoning and domain robustness. This paper introduces Curriculum-KAG, a new method and implemented information system that mirrors KAG onto the macro-level task of interdisciplinary curriculum synthesis. A curriculum knowledge base integrates (i) a vector index for semantic retrieval and (ii) a curriculum knowledge graph encoding prerequisites, domains, and regulatory constraints. Hybrid retrieval with graph expansion selects candidate courses and enforces prerequisite closure. Constrained synthesis is formulated as multi-objective optimization with strict verification, while bridge modules are generated only under an evidence constraint when critical learning outcomes remain weakly covered. A prototype is reported on a two-domain case study (IT + Forensics) for the “Cyber Investigator” programme, including an 8-semester plan (240 ECTS) and outcome coverage diagnostics (LO1–LO7, with LO5 at 65%). Measured evaluation against baselines indicates improvements in retrieval and mapping (Recall@20=0.91 ± 0.02, nDCG@20=0.88±0.02, Macro-F1=0.85±0.02) while preserving feasibility (0 violations) and reducing redundancy (0.41 ± 0.03).

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