.png){kind=icon}
.png){kind=icon}
Return
KNOWLEDGE AUGMENTED GENERATION FOR CURRICULUM PLANNING
.png){kind=icon}
PRE
AI
DOI：10.32523/2306-6172-2026-14-1-17-34.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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).
Keywords:
Curriculum generation
interdisciplinary programs
knowledge graphs
evidence-constrained synthesis
graph expansion
artificial intelligence
machine learning
deep learning
augmented learning
syllabus design
data science
Journal
E
IF:
0.4
Papers:
5
Citations:
0
Organization
View more organizations