Generative Artificial Intelligence Integration in Higher Education: A Constructivist Learning Theory Approach

Synthesis: Alam, Farhaz, Haq, and Ferdous (2026) investigate GenAI adoption in Bangladeshi universities through the lens of Constructivism Learning Theory, using surveys of 255 faculty members and focus-group discussions, with structural equation modeling in SmartPLS. Attitude toward GenAI use was a strong predictor of the extent of use (β = 0.556), itself influenced by competency, domain-specific technology relevance, and students' technology-use provision; domain-specific relevance had the strongest effect on attitude (β = 0.413), while subjective norms and institutional technology-use provision were not significant.

Key Findings

• Attitude toward GenAI use strongly predicted the extent of GenAI use (β = 0.556, p < 0.001) among 255 Bangladeshi faculty.
• Attitude was significantly influenced by user competency, domain-specific technology relevance, and students' technology-use provision.
• Domain-specific technology relevance had the strongest effect on attitude toward GenAI use (β = 0.413, p < 0.001).
• Subjective norms and institutional technology-use provision were not significant predictors in this context.
• Thematic analysis highlighted teachers' strong concerns about technology use, alongside positive contributions of students' technology-use provision.

What this means for practice

• Instructors. Tie GenAI use to your subject matter: domain-specific technology relevance was the strongest driver of faculty attitude (β = 0.413), so discipline-based demonstrations outperform generic tool promotion.
• Instructors. Build your own competency before scaling use, since competency significantly predicted attitude toward GenAI use, which in turn strongly predicted extent of use (β = 0.556).
• Faculty developers. Design training that targets capacity and domain relevance rather than general encouragement, because subjective norms were not significant predictors of attitude in this sample.
• Administrators. Weight capacity building and ethical guidelines above infrastructure provision: institutional technology-use provision did not significantly predict use, instead, enhance university capacity through training, integrate GenAI into Pedagogies and Teaching Strategies, and develop ethical guidelines that support students and teachers while attending to the concerns faculty raised in discussion, in this resource-constrained context.
• Researchers. Test these adoption paths longitudinally, since the study is cross-sectional and itself calls for examining long-term effects on teaching outcomes and institutional readiness.

Limitations

• The quantitative sample is 255 valid faculty responses drawn from four Bangladeshi universities, from 268 returned questionnaires, so the findings are bound to one national higher-education system.
• Although sampling began with proportional stratification across ranks and institutions, the survey link was circulated through teachers' social media groups during the final week to counter non-response, so the achieved sample is partly a convenience one.
• The qualitative component rested on four focus group discussions of six faculty members each across four selected universities, with transcripts translated from Bangla to English before thematic analysis.
• All adoption constructs are self-reported in a single cross-sectional survey; the authors call for future research on longitudinal effects that the design cannot support.

Connected Concepts

• Constructivism
• Generative AI
• Higher Education
• Global South
• Technology Adoption Models
• Educational AI Policy

Connected Articles

• [crompton-faculty-technology-integration-standards-2026] — faculty technology-integration standards
• [genai-higher-education-systematic-review-2026] — systematic review of GenAI in higher education

Citation

Alam, M. M., Farhaz, S., Haq, S. M. A., & Ferdous, M. (2026). Generative Artificial Intelligence integration in higher education: A constructivist learning theory approach. Computers and Education Open, 10, 100378.