AI Insight
This study of 786 university engineering teachers in China found that artificial intelligence readiness was positively associated with innovative work behavior, with this relationship partially explained through two pathways: creative self-efficacy and technology integration self-efficacy. Both indirect effects were statistically significant, indicating that teachers' confidence in their creative abilities and technology integration skills serve as mechanisms linking AI readiness to workplace innovation. The findings are grounded in Social Cognitive Theory and Conservation of Resources Theory.
Why it matters
The results suggest that universities seeking to foster innovation among engineering faculty should not only provide AI training but also intentionally build teachers' confidence through guided practice integrated with actual teaching tasks such as course design, laboratory instruction, and research projects. This approach may be more effective than AI training alone for promoting innovative teaching practices.
Understand the Science
IntroductionArtificial intelligence (AI) is becoming part of university teachers’ teaching and research, yet its relationship with innovative work behavior in engineering education remains unclear.MethodsDrawing on Social Cognitive Theory and Conservation of Resources Theory, this cross-sectional study surveyed 786 university engineering teachers in Chongqing, China. We examined the association between AI readiness and innovative work behavior and tested parallel indirect effects through creative self-efficacy and technology integration self-efficacy. Gender and educational attainment were included as covariates.ResultsAI readiness was positively associated with innovative work behavior. The indirect paths through creative self-efficacy and technology integration self-efficacy were both significant. AI readiness remained significantly associated with innovative work behavior after both mediators were included, indicating partial mediation.DiscussionThese findings suggest that AI readiness and efficacy beliefs may support innovative work among engineering teachers. Universities can support quality education by linking AI training to course design, laboratory teaching, project development, and research tasks, while using guided practice and feedback to build teachers’ confidence.