Social and cognitive drivers of generative AI adoption: A unified socio-cognitive model for engineering education

Generative artificial intelligence (AI) redefines cognitive and social processes in learning environments, yet its psychological and contextual determinants in developing countries remain underexplored. This study investigates engineering students’ adoption of generative AI by integrating the Technology Acceptance Model (TAM) with the Unified Theory of Acceptance and Use of Technology (UTAUT) to develop a unified socio-cognitive framework. Survey data from 378 Bangladeshi engineering students were analysed using PLS-SEM and multi-group comparisons to evaluate structural, mediating, and demographic effects. Results demonstrate that behavioural intention is the strongest predictor of AI use, with perceived usefulness and ease of use functioning as key mediators linking cognitive, social, and attitudinal factors. Job relevance, result demonstrability, and subjective norms exert significant direct and indirect influences, revealing a multilayered decision-making process. The model explains 64% of the variance in usage behaviour, affirming the robustness of this integrated framework. Multi-group analysis (MGA) results indicate that domestic students perceive a higher level of perceived ease of use than their international peers, while the pattern is reversed in terms of students’ image when interacting with generative AI tools. Gender shows no significant differences for either group. The study contributes to human–technology interaction research by clarifying the cognitive and social drivers underlying generative AI acceptance. It emphasises the role of contextual constraints and user perceptions in shaping technology-driven learning behaviour. Theoretically, the study extends AI acceptance literature by contextualising model validation within a resource-constrained, multicultural higher education environment. Practically, it offers evidence-based guidance for designing culturally responsive and cognitively supportive AI learning ecosystems. The findings underscore the need for cross-cultural, longitudinal research to unpack evolving patterns of AI adoption in education.