Construction and Application of Intelligent Adaptation System for Higher Vocational Training Courses Based on AI Large Model
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Abstract
Aiming at the prominent problems of solidified training content, single teaching mode, inconsistent individual learning levels of students, and disconnection between training teaching and industrial post demands in higher vocational practical training teaching, this paper constructs an intelligent adaptation system for higher vocational training courses based on AI large model. Combined with the characteristics of vocational education skill training, the system builds a four-layer overall architecture including data perception layer, large model algorithm layer, intelligent adaptation service layer and teaching application layer, and designs a closed-loop adaptation mechanism of “student portrait modeling - post demand matching - course resource dynamic adjustment - teaching effect feedback optimization”. Based on fine-tuning the industry vertical large model, the paper realizes personalized learning path planning, intelligent matching of training resources, real-time error diagnosis in training operation and dynamic iteration of course content. Through the teaching application experiment of multiple vocational majors, the results show that the system can effectively solve the problem of heterogeneous learning adaptation in vocational training, significantly improve students’ practical operation ability and independent learning efficiency, and realize the precise docking of training courses and industrial post competency requirements. The system provides a new technical scheme and teaching reform path for the digital and intelligent upgrading of higher vocational training teaching, and has good popularization and application value.
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References
Zhang Y, Li J, “Research on Digital Transformation Path of Higher Vocational Education Under the Background of Artificial Intelligence[J],” Journal of Vocational Education, vol. 12, no. 3, pp. 45–52, 2024.
Wang H, Liu S, “AI Empowered Personalized Teaching Mode Reform of Vocational Training Courses[J],” Computer Engineering and Applications, vol. 61, no. 8, pp. 231–238, 2025.
Li M, Zhang Q, “Application and Prospect of Generative AI in Education and Teaching[J],” Journal of Educational Technology, vol. 33, no. 2, pp. 18–25, 2024.
Chen L, Wang X, “Research on Intelligent Adaptive Learning System Based on Large Model[J],” Modern Educational Technology, vol. 35, no. 4, pp. 36–43, 2025.
Liu J, Zhao Y, “Construction of Student Learning Portrait Model for Vocational Students Based on Big Data[J],” Vocational Education Research, vol. 9, no. 5, pp. 78–84, 2024.
Zhou B, Wu H, “Post-demand Oriented Dynamic Optimization of Higher Vocational Training Curriculum[J],” Higher Vocational Education Forum, vol. 13, no. 6, pp. 56–62, 2024.
Sun T, Li Y, “Exploration of AIGC Empowered Vocational Course Intelligent Teaching[J],” Frontiers in Educational Research, vol. 8, no. 3, pp. 112–118, 2025.
Wang Z, Chen J, “Industry Large Model Fine-tuning Method for Vocational Education Scenarios[J],” Journal of Intelligent Computing, vol. 10, no. 2, pp. 89–96, 2024.
Zhang H, Xu L, “Research on Personalized Learning Path Adaptation Based on Student Portrait[J],” Journal of Computer-Assisted Education, vol. 22, no. 1, pp. 29–35, 2025.
Guo S, Yang F, “Competency-based Intelligent Curriculum Adaptation Mechanism in Vocational Education[J],” Chinese Journal of Vocational and Technical Education, vol. 15, no. 7, pp. 41–47, 2024.