Determinants of Higher Education Learners’ Behavioral Intention Toward Generative AI Tools: A Hybrid SEM–Machine Learning Approach
As generative artificial intelligence (GenAI) increasingly permeates educational contexts, understanding the factors driving learners’ Behavioral Intention (BI) toward GenAI-powered tools has become critical. This study integrates the Technology Acceptance Model (TAM), the Task-Technology Fit (TTF) framework, and privacy and ethical risk considerations to explore the determinants of Chinese higher education students’ Behavioral Intention to adopt these tools. Data were collected from 716 students via a structured self-reported questionnaire. A multi-stage analytical approach was employed by integrating structural equation modeling (SEM) with artificial neural networks (ANN) and support vector regression (SVR). SEM was first utilized to validate the theoretical hypotheses and the measurement model. Subsequently, ANN and SVR models were constructed to explore non-linear relationships and rank the importance of core predictors for Behavioral Intention, including Perceived Ease of Use (PEU), Privacy and Ethical Concerns (PEC), Perceived Technical Features (PTF), and TTF. The modeling performance of the two algorithms was then rigorously compared. The SEM results indicate that PTF exerts an indirect impact on Behavioral Intention via the sequential mediation of Task-Technology Fit and Perceived Usefulness (PU), while PEU positively influences both Perceived Usefulness and Behavioral Intention. Notably, PEC did not exhibit a significant negative effect on users’ Attitude (ATT) or Behavioral Intention. These findings were further elucidated by the machine learning analyses, where PTF and PEU emerged as the dominant predictors, whereas the non-linear contribution of PEC was marginal. Furthermore, SVR outperformed ANN in terms of predictive accuracy and model stability. This study demonstrates the efficacy of combining theoretical modeling with machine learning techniques to elucidate the adoption mechanisms of GenAI in higher education. In addition, preliminary teaching observations in undergraduate mathematics and logistics management courses link quantitative results with actual learning scenarios. We acknowledge that future research should validate these patterns using observed behavioral data.