Development of University Teacher AI Competence Self-efficacy (TAICS) Scale in China’s Border Ethnic Regions

AI holds substantial and profound implications for teaching and learning, as well as for the enhancement of teachers' roles and capabilities. The integration of AI in educational contexts demonstrates considerable potential to enable innovative approaches to instruction, learning, and administration, while also improving learning experiences and facilitating teachers' professional responsibilities. As AI continues to transform modes of production, daily life, and educational practices, it is steering human society into a new developmental phase (Zhu & He, 2012).This research employs the Delphi method and integrates the "AI Competency Framework for Teachers" (AI CFT; Miao, 2024) with the Teacher AI Competence Self-efficacy (TAICS) scale, initially explored by Thomas et al. (2024) in their study on teacher competency self-efficacy. Grounded in Thomas’s scale and Bandura’s self-efficacy theory, the present study aims to develop a scale for assessing Teacher AI Competence Self-efficacy (TAICS) among university faculty. The scale design process will incorporate the perspectives of educators and account for the specific contextual factors of AI-mediated teaching and learning within higher education institutions in China’s border ethnic regions.