Research on Path Design and Effect Verification of AI-Enabled Personalized Learning in Higher Vocational Education
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Abstract
Aiming at the prominent problems of unified teaching mode, inconsistent learning level of students, disconnection between teaching content and post competency requirements, and difficult personalized teaching implementation in higher vocational education, this paper constructs an artificial intelligence (AI) enabled personalized learning system framework for higher vocational colleges, and designs a four-stage closed-loop learning path of intelligent portrait diagnosis, adaptive path planning, personalized resource push, and dynamic effect iteration. Based on graph neural network (GNN) and large language model (LLM) technology, the system realizes multi-dimensional perception of students’ learning characteristics, precise matching of vocational skill knowledge graphs, and dynamic optimization of learning paths. This paper takes the core courses of higher vocational Biopharmaceutical Major as the research object, carries out a 16-week quasi-experimental study, and verifies the application effect of the model through data statistics such as academic performance, skill assessment results, learning engagement and autonomous learning ability. The experimental results show that compared with the traditional teaching mode, the AI-enabled personalized learning path can significantly improve students’ academic performance and vocational operational skills, effectively enhance learning initiative and independent planning ability, and solve the pain points of homogenized teaching in higher vocational education. The research can provide theoretical support and practical reference for the digital transformation of higher vocational teaching and the innovation of personalized talent training mode.
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