The impact of AI technology integration and digital innovation self-efficacy on vocational students’ adaptability: Dual moderation by innovation competition intensity - Huang, Dan; Li, Weipeng (Edelweiss Applied Science and Technology, 2025)
This study contributes to the expanding body of research on adaptability development in vocational education by examining the interaction between cognitive, technological, and contextual factors. While existing literature has explored the individual effects of artificial intelligence (AI) integration and digital self-efficacy on learning outcomes, few studies have investigated their combined impact on student adaptability, particularly under varying levels of innovation competition. Drawing on Social Cognitive Theory, this study develops a theoretical model that positions AI technology integration ability and digital innovation self-efficacy as predictors of adaptability intensity, with innovation competition intensity serving as a moderator. A quantitative survey was conducted among 412 students from vocational colleges across China. The data were analyzed using Structural Equation Modeling (SEM) via SmartPLS 4.0. Results demonstrate that both AI integration and digital self-efficacy positively influence adaptability intensity, with innovation competition intensity significantly enhancing these relationships. These findings underscore the importance of embedding AI tools in vocational curricula and fostering learners’ confidence in digital innovation within competitive educational environments. Practical implications include the need for institutions to align technology adoption with student-centered strategies and to develop resilience-driven pedagogical models that prepare learners for dynamic, technology-intensive work environments.