Social and Cognitive Drivers of Generative AI Adoption: A Unified Socio-Cognitive Model for Engineering Education

Mazen Abdulwahab Asag & Md Abdullah Al Mamun (2026) investigated 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.

Key Findings

• Method: Survey data from 378 Bangladeshi engineering students, analyzed using PLS-SEM and multi-group comparisons.
• Behavioral 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.
• Multilayered decision-making: Job relevance, result demonstrability, and subjective norms exert significant direct and indirect influences on adoption.
• Strong explanatory power: The model explains 64% of the variance in usage behavior, affirming the robustness of the integrated framework.
• Contextual differences: Multi-group analysis shows domestic students perceive higher ease of use than their international peers, while the pattern reverses for students' image when interacting with GenAI. Gender showed no significant differences for either group.

Implications for AI in Education

The study contributes to human–technology interaction research by clarifying the cognitive and social drivers of generative AI acceptance, and contextualizes model validation within a resource-constrained, multicultural higher education environment (Global South context). It offers evidence-based guidance for designing culturally responsive and cognitively supportive AI learning ecosystems, and underscores the need for cross-cultural, longitudinal research on evolving patterns of AI adoption.

Connected Concepts

• Technology Acceptance Model
• Generative AI
• Engineering Education
• Higher Ed
• Student Experience
• Global South
• AI Literacy
• Equity In AI Education

Connected Articles

• Tam Critical Use GenAI Engineering 2026 — Extended TAM with critical use for engineering/CS students
• AI Adoption Training Public Sector — The Main Barrier to AI Adoption in the Public Sector is Lack of Training

Citation

Asag, M. A., & Al Mamun, M. A. (2026). Social and cognitive drivers of generative AI adoption: A unified socio-cognitive model for engineering education. Computers and Education: Artificial Intelligence, 10, 100614.