AI Intervention Strategies for Personalized Learning Planning for College Students - Implementation and Optimization Based on Large Models

Abstract

 The rise of personalized learning planning technology for college students is redefining the training mode of higher education, especially in the field of intelligent education. The rapid development of large-scale pre-training models provides strong technical support for personalized learning systems, which not only improves the accuracy of learning behavior analysis, but also greatly enriches the adaptability and interactivity of educational resources. In this paper, we review the development history of learning planning technology for college students, analyze the key applications of large model-based intervention strategies in academic diagnosis, path optimization, career matching, etc., explore the educational value and social significance of personalized learning systems, and at the same time, conduct an in-depth analysis of the technical challenges faced by them, such as data privacy, model bias, etc., and put forward the direction of future development, with a view to providing the intelligent education field with thetheoretical support and practical guidance.