Generative Large Language Models for Knowledge Representation: A Systematic Review of Concept Map Generation

The rise of generative large language models (LLMs) has opened new opportunities for automating knowledge representation through concept maps, a long-standing pedagogical tool valued for fostering meaningful learning and higher-order thinking. Traditional construction of concept maps is labor-intensive, requiring significant expertise and time, limiting their scalability in education. This review systematically synthesizes the emerging body of research on LLM-enabled concept map generation, focusing on two guiding questions: (a) What methods and technical features of LLMs are employed to construct concept maps? (b) What empirical evidence exists to validate their educational utility? Through a comprehensive search across major databases and AI-in-education conference proceedings, 28 studies meeting rigorous inclusion criteria were analyzed using thematic synthesis. Findings reveal six major methodological categories: human-in-the-loop systems, weakly supervised learning models, fine-tuned domain-specific LLMs, pre-trained LLMs with prompt engineering, hybrid systems integrating knowledge bases, and modular frameworks combining symbolic and statistical tools. Validation strategies ranged from quantitative metrics (precision, recall, F1-score, semantic similarity) to qualitative evaluations (expert review, learner feedback). Results indicate LLM-generated maps hold promise for scalable, adaptive, and pedagogically relevant knowledge visualization, though challenges remain regarding validity, interpretability, multilingual adaptability, and classroom integration. Future research should prioritize interdisciplinary co-design, empirical classroom trials, and alignment with instructional practices to realize their full educational potential.