SELF-EFFICACY AND PRE-SERVICE TEACHERS BEHAVIORAL INTENTION TO USE GENERATIVE AI FOR LEARNING: A PLS-SEM APPROACH - Muhammad Abdullah; Jusriadi Jusriadi (PINUS: Jurnal Penelitian Inovasi Pembelajaran, 2026)
The development of generative artificial intelligence in higher education requires pre-service teachers to have self-efficacy, perceived ease of use, perceived usefulness, and positive attitudes to use it responsibly for learning. This study aims to analyze the effects of self-efficacy, perceived ease of use, perceived usefulness, and attitude toward using on behavioral intention to use generative AI for learning. This study employed a quantitative approach with an explanatory survey design. The respondents were 149 pre-service teachers at Sekolah Tinggi Keguruan dan Ilmu Pendidikan Muhammadiyah Kalabahi, Alor Regency, East Nusa Tenggara. Data were collected using a Likert-scale questionnaire and analyzed using partial least squares structural equation modelling. The results showed that self-efficacy positively influenced behavioral intention to use. Perceived ease of use positively influenced perceived usefulness and attitude toward using. Perceived usefulness and attitude toward using positively influenced behavioral intention to use. Perceived ease of use also indirectly influenced behavioral intention to use through attitude toward using and perceived usefulness. The R-square value of behavioral intention to use was 0.748, indicating that the model had strong explanatory power. These findings confirm that pre-service teachers’ intention to use generative AI is shaped by self-efficacy, perceived ease of use, perceived usefulness, and positive attitudes toward technology