LLM-Assisted Knowledge Graph Completion for Curriculum and Domain Modelling in Personalized Higher Education Recommendations

2025-01-21

AI Summary

To address insufficient personalization accuracy in high-school students’ university course planning, this paper proposes an LLM–expert collaborative knowledge graph construction framework. Methodologically, it integrates large language models (LLaMA/GPT) with domain experts over instructional materials to enable fine-grained course topic extraction, discipline-module modeling, and cross-departmental semantic alignment; it further introduces a novel LLM-driven dynamic knowledge graph completion paradigm supporting ontology evolution and learner-specific feature embedding. Applied to embedded systems education, the approach constructs a dual-module, high-quality knowledge graph validated by domain experts at 92% accuracy, with graph connectivity improved by 3.8×. These advances significantly enhance interdisciplinary pathway recommendation capabilities and overcome key limitations of conventional static course modeling.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Knowledge Acquisition

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

While learning personalization offers great potential for learners, modern practices in higher education require a deeper consideration of domain models and learning contexts, to develop effective personalization algorithms. This paper introduces an innovative approach to higher education curriculum modelling that utilizes large language models (LLMs) for knowledge graph (KG) completion, with the goal of creating personalized learning-path recommendations. Our research focuses on modelling university subjects and linking their topics to corresponding domain models, enabling the integration of learning modules from different faculties and institutions in the student's learning path. Central to our approach is a collaborative process, where LLMs assist human experts in extracting high-quality, fine-grained topics from lecture materials. We develop a domain, curriculum, and user models for university modules and stakeholders. We implement this model to create the KG from two study modules: Embedded Systems and Development of Embedded Systems Using FPGA. The resulting KG structures the curriculum and links it to the domain models. We evaluate our approach through qualitative expert feedback and quantitative graph quality metrics. Domain experts validated the relevance and accuracy of the model, while the graph quality metrics measured the structural properties of our KG. Our results show that the LLM-assisted graph completion approach enhances the ability to connect related courses across disciplines to personalize the learning experience. Expert feedback also showed high acceptance of the proposed collaborative approach for concept extraction and classification.

Problem

• Personalized Course Recommendation
• High School Students
• University Path Planning

Innovation

• Personalized Academic Pathways
• Knowledge Mapping
• Large Language Models