Motivation and technology in voice-AI adoption: an empirical validation of the integrated model in social studies classrooms

Abstract This study proposes and empirically tests an integrated model combining Self-Determination Theory (SDT), the Technology Acceptance Model (TAM), and the Theory of Planned Behavior (TPB) to investigate social studies teachers’ behavioral intentions to adopt voice-activated artificial intelligence (AI) in education. Data were collected from 1,590 teachers, comprising both pre-service and in-service educators, using a validated instrument. Measurement analyses confirmed strong construct reliability, convergent validity, and discriminant validity across all scales. Structural equation modeling demonstrated a good overall model fit, supporting the hypothesized relationships among constructs. The findings revealed that SDT-based motivational variables—perceived competence, autonomy, and relatedness—significantly predicted TAM variables, namely perceived usefulness and perceived ease of use of voice-activated AI. These technology acceptance factors, in turn, influenced attitudes toward use, which—together with subjective norm and perceived behavioral control from TPB—significantly shaped behavioral intention. The integrated model explained 48% of the variance in behavioral intention, outperforming models based solely on TAM or TPB. Multi-group analysis by professional status confirmed full metric invariance, with only one significant path difference observed, indicating broad applicability across teacher demographics. Overall, the results underscore the value of integrating motivational, cognitive, and social–behavioral perspectives in educational technology research and offer empirically grounded implications for professional development, policy design, and curricular innovation supporting AI integration in social studies education.