Factors Influencing Learning Adjustment of Vocational Undergraduates: An AI ChatGPT Perspective Using Smart PLS Model

In the era of digital education, using Artificial Intelligence ChatGPT as an example, this study aims to explore the primary influencing factors of learning adjustment among vocational undergraduates and establish a semi-structured model.Statistical analysis, modeling, and path analysis were conducted using SPSS 29.0 and SmartPLS 4. The study sample consisted of students from vocational universities to ensure the relevance of research results and policy recommendations.The research findings indicate: (1) There is minimal change in learning adjustment among freshman vocational college students over a semester, and it tends to be overall low; (2) The most influential factors affecting learning adjustment among vocational college students are teaching methods, followed by learning attitudes; (3) A semi-structured internal model of learning adjustment was constructed.Based on these findings, the study proposes suggestions for optimizing learning adjustment empowered by generative Artificial Intelligence ChatGPT.This research not only enriches theoretical studies in the intersection of vocational education and artificial intelligence but also provides empirical evidence and practical guidance for educational managers, policymakers, and educational practitioners to better support the learning and development of vocational college students.