Techno-stress and teaching innovation among university teachers in the intelligent era: a path analysis of job burnout and AI self-efficacy.

BACKGROUND: In the context of deep integration of intelligent technologies into educational practice, how to balance the pressure from technology application with the intrinsic demands of teaching innovation, has become an important issue. Job burnout reflects an individual's state of emotional exhaustion under long-term techno-stress, and AI self-efficacy indicates an individual's confidence in their ability to use AI tools. There is still a lack of a clear explanation for how the two are jointly related to the relationship between techno-stress and teaching innovation. Based on the Job Demands-Resources Model, this study constructed a chain mediation model aimed at systematically examining the relationships among the variables. METHOD: The study adopted a stratified random sampling method to collect valid questionnaire data from a total of 819 university teachers, and conducted model checking and path effect analysis based on CB-SEM. RESULTS: The results showed that techno-stress had a significant negative correlation with teaching innovation. The results of the mediation analysis indicate that the mediation effects of job burnout and AI self-efficacy account for 33.60% and 20.00% of the total effect, respectively, while the chained mediation effect resulting from both factors accounts for 14.13% of the total effect. Significant differences in teaching innovation levels were found across teacher groups divided by gender and age. CONCLUSION: The research results clarify the synergistic mechanism of external organizational factors and internal psychological factors driving teaching innovation, and have important implications for promoting the continuous teaching innovation of university teachers in the intelligent era.