An integrative study of factors influencing university students’ acceptance of AI chatbot technology: a MASEM analysis based on TAM, TPB, and UTAUT frameworks

AI chatbot adoption in higher education has generated growing empirical evidence, yet findings remain fragmented and theoretically inconsistent. This study synthesizes prior research and develops an integrated model of university students’ AI chatbot adoption across cultural contexts. Using meta-analytic structural equation modeling, we integrated TAM, TPB, and UTAUT based on 99 peer-reviewed empirical studies published between 2020 and 2025, covering 48,993 university students from 33 countries. Random-effects meta-analysis was used to estimate pooled correlations, which were then used to test the structural model in AMOS. Results showed strong associations among all key constructs and identified four complete mediation pathways linking perceived ease of use, social influence, and facilitating conditions to actual use through perceived usefulness, attitude, and behavioral intention. Perceived usefulness was the most proximal predictor of adoption intention, while perceived ease of use operated through both cognitive and attitudinal routes. Social influence and facilitating conditions mainly influenced adoption through behavioral intention. This study is the first to apply MASEM to an integrated TAM–TPB–UTAUT framework in an educational chatbot context, providing a cross-culturally robust synthesis and cumulative evidence for AI chatbot adoption in higher education.