Construction of Personalized Learning Paths for Private Higher Vocational Colleges Driven by Lightweight AI: An Empirical Analysis Based on Innovation and Entrepreneurship Courses in China's Hainan Free Trade Port

Abstract

With the construction of the Hainan Free Trade Port accelerates, its demand has significantly increased for highly skilled talents.
By implementing a series of measures, such as large-scale subsidized vocational skills training and skills competition, Hainan Province
has increased the total number of high-skilled talents to 130, 500, and achieved the goal of the action plan for three-year "Skill-based Free
Trade Port" ahead of schedule. Due to the diverse student sources and significant differences in their foundation in private higher vocational colleges, it is difficult for the traditional "one-size-fits-all" teaching model to align with their needs. This study focuses on the application of lightweight AI technology in private higher vocational education. Firstly, it defines its connotation, that is, achieves efficient
deployment with limited resources by relying on technologies such as model compression and knowledge distillation; Secondly, it further
analyzes the characteristics of students in private higher vocational colleges and the current situation of innovation and entrepreneurship
course ecosystem in the Hainan Free Trade Port, and points out the problems of fragmented and unstructured curriculum system. Subsequently, it builds a personalized learning path model including "a multi-dimensional learner profile", "a knowledge graph of innovation
and entrepreneurship courses", "a lightweight AI path recommendation engine", and "a dynamic feedback closed loop", and designs a
quasi-experiment empirical scheme to verify its role in enhancing students' learning effect, satisfaction, and innovation and entrepreneurship awareness to provide theoretical and practical reference for colleges and universities to deepen their teaching reform and empower
regional economy.