Cultivating College Students' Adaptive Learning Ability Based on LLM-Enhanced Knowledge Graph

Abstract

With the rapid development of internet technology, the field of education is undergoing profound changes. As the main force of future society, college students' adaptive learning ability is particularly important. This article aims to explore how to enhance college students' adaptive learning ability by combining large-scale models and knowledge graph technologies. By constructing a knowledge graph system enhanced by large-scale models, it can be achieved in-depth mining of learning content and personalized recommendations, providing college students with more accurate and efficient learning paths, thereby cultivating their ability for autonomous and lifelong learning.