Urban–Rural differences in teachers’ acceptance of artificial intelligence for teaching and learning: Evidence from indonesia using the technology acceptance model - Lusiwati Iriani Butar-Butar; Afandi; Reni Marlina; Eny Enawaty; Eva Faja Ripanti (CV. FOUNDAE, 2026)
Background: Artificial Intelligence (AI) offers significant opportunities to enhance teaching and learning through instructional support, automated assessment, and learning analytics. However, teachers’ acceptance of AI may vary according to perceived usefulness, ease of use, and differences in technological access across geographical settings. Aim: This study aimed to compare teachers’ acceptance of AI for teaching and learning between urban and rural schools in Melawi Regency, Indonesia. Method: A quantitative comparative design was employed using the Technology Acceptance Model (TAM), encompassing Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Using (ATU), and Behavioral Intention (BI). Data were collected from 100 teachers through an online Likert-scale questionnaire. Content validity was established through expert review, and the data were analyzed using descriptive statistics and an independent samples t-test. Results: The findings indicated that teachers in both settings demonstrated high levels of AI acceptance. However, urban teachers reported higher acceptance (M = 4.22) than rural teachers (M = 4.05). The difference was statistically significant (t = 7.35, p < 0.001) with a very large effect size (Cohen’s d = 2.32), suggesting a substantial influence of geographical context on AI acceptance. Conclusion: Urban teachers exhibit significantly greater acceptance of AI than rural teachers. Infrastructure availability, digital literacy, and institutional support appear to be key factors influencing this disparity. Strengthening digital capacity and improving technological infrastructure are essential to promote equitable AI integration in education.