<b>AI Acceptance and Learning Discipline in Vocational Mathematics Education</b><b></b>

Source abstract

This study examines the relationships among students’ acceptance of artificial intelligence (AI) for mathematics learning, self-regulated learning (SRL), and learning discipline. The proposed model extends a partial Technology Acceptance Model (TAM) by incorporating perceived ease of use (PEOU), perceived usefulness (PU), attitude toward use (ATU), and behavioural intention (BI), together with SRL and learning discipline. A quantitative cross-sectional survey was conducted with 206 vocational high school students (Sekolah Menengah Kejuruan/SMK) from DKI Jakarta, West Java, Central Java, and East Java, Indonesia. Data were analysed using partial least squares structural equation modelling (PLS-SEM). The results supported the TAM sequence: PEOU was positively associated with PU (β = .743, p < .001), PU with ATU (β = .738, p < .001), and ATU with BI (β = .851, p < .001). PU was positively associated with SRL (β = .393, p < .001), whereas BI was not significantly associated with SRL (β = .131, p = .195). SRL was positively associated with learning discipline (β = .593, p < .001; f² = .494). The model explained 24.1% of the variance in SRL and 45.2% of the variance in learning discipline. Mediation analysis showed a significant indirect association between PU and learning discipline through SRL (β = .233, 95% CI [.115, .348], p < .001), while the direct association between PU and learning discipline remained significant (β = .140, p = .030), indicating partial mediation. These findings suggest that perceived usefulness may be more educationally relevant than behavioural intention for understanding SRL and learning discipline in AI-supported mathematics learning.