Applying the Technology Acceptance Model to AI Integration in Early Childhood Education: Evidence from Malaysian Preschools

The rapid diffusion of artificial intelligence (AI) into education has outpaced empirical understanding of how early childhood educators perceive and respond to it, particularly within Malaysia and the wider ASEAN region. This study examined the roles of perceived usefulness, AI self-efficacy, and institutional support in predicting early childhood educators’ behavioural intention to integrate AI into preschool classrooms, using the Technology Acceptance Model as the guiding framework. A mixed-methods sequential explanatory design was employed, combining a structured survey of 150 preschool educators in the Klang Valley with semi-structured interviews of 12 purposively selected educators. All three constructs correlated strongly and significantly with behavioural intention (perceived usefulness, r = .99; AI self-efficacy, r = .99; institutional support, r = .96; all p < .01). However, multiple regression analysis, which explained 98.6% of the variance in behavioural intention, revealed that AI self-efficacy was the only variable to retain a robust, independent, positive effect (β = 1.80, p < .001) once the overlap among predictors was accounted for; perceived usefulness showed an unexpected negative coefficient (β = -.79, p = .023), and institutional support lost statistical significance (β = -.02, p = .698), a pattern consistent with high multicollinearity among the three intercorrelated constructs. Thematic analysis of the interviews identified personalised learning, time efficiency, and student engagement as key motivators, alongside persistent barriers of training gaps, unstable infrastructure, and parental scepticism about screen time. Educators consistently framed AI as a complement to, rather than a substitute for, human interaction. The findings suggest that building educators’ confidence through sustained, hands-on professional development is a more actionable lever for AI adoption than generic usefulness messaging or infrastructure investment alone, with implications for teacher training curricula and early childhood technology policy in Malaysia.

artificial intelligence; early childhood education; AI self-efficacy; Technology Acceptance Model; teacher perceptions

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