Аңдатпа
Interdisciplinary education is positioned as a strategic response to complex tech- nological and societal challenges. However, outcome-driven synthesis of complete study plans remains difficult to scale under heterogeneous competency standards and strict academic reg- ulations. Retrieval-Augmented Generation (RAG) improves factual grounding by retrieving external evidence, while Knowledge-Augmented Generation (KAG) extends RAG by inte- grating structured knowledge representations for relational reasoning and domain robustness. This paper introduces Curriculum-KAG, a new method and implemented information sys- tem 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. Hy- brid 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 learn- ing 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)andoutcomecoveragediagnostics(LO1–LO7, withLO5at65%). Measuredevaluation 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).