Abstract :

The rapid advancement of generative artificial intelligence (AI) is reshaping teaching practices in higher education, yet how university teachers adapt to these changes remains insufficiently understood. This study examines the relationships among technology anxiety, self-efficacy, and role adaptation, with a particular focus on the mediating role of career identity. Data were collected from 376 university teachers and analyzed using structural equation modeling. The results indicate that both technology anxiety (β = 0.356, p < 0.001) and self-efficacy (β = 0.371, p < 0.001) exert significant positive effects on teachers’ role adaptation to generative AI, with self-efficacy demonstrating a slightly stronger influence. Career identity also shows a significant direct effect on role adaptation (β = 0.237, p < 0.01). Furthermore, technology anxiety (β = 0.492, p < 0.001) and self-efficacy (β = 0.391, p < 0.001) significantly predict career identity, which partially mediates these relationships, accounting for approximately 25% and 20% of the total effects, respectively. These findings underscore the importance of not only enhancing teachers’ self-efficacy but also constructively addressing technology-related anxiety and strengthening career identity as an integrated pathway to support meaningful role adaptation in AI-integrated teaching contexts. The results offer empirical evidence for integrated professional-development strategies that simultaneously address anxiety, technological competence, and professional identity.

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Keywords :

Generative AI, Technology anxiety, Self-efficacy, Career identity, Role reconstruction, Structural equation modeling

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