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Understanding the motivational factors influencing student teachers’ self-efficacy in adopting Artificial Intelligence (AI) is essential in technology-driven learning environments, but this area has received less research attention in resource-scarce settings like Ethiopia. To this end, this study was initiated to explore the motivational factors influencing the self-efficacy in adopting AI among Ethiopian student teachers. The study employed structural equation modeling to analyze data collected from 278 student teachers enrolled in teacher education programs to determine the relationship between motivational factors (commitment to the teaching profession, along with intrinsic, extrinsic, and altruistic motivations) and dimensions of self-efficacy (teaching AI skills, planning and classroom management, and student affective domains). The result demonstrated that strong and positive associations were found between affective commitment to teaching and self-efficacy (p < 0.01) in AI teaching skills, planning and managing the classroom, and addressing the student affective domain. In addition, positive and moderate associations were noted between extrinsic motivation and self-efficacy (p < 0.05) in the student affective and teaching AI skills domains. No significant relationships were observed for intrinsic or altruistic motivations. Thus, by highlighting the role of commitment and extrinsic motivation, the findings can inform teacher education programs aiming to enhance the holistic development and effectiveness of future educators and contribute to developing targeted recruitment and training strategies that nurture motivated and technologically proficient teachers.
Hunde et al. (Tue,) studied this question.