Students’ Acceptance and Adoption of Generative AI Tools: Evidence from a Bangladeshi Engineering University
| dc.contributor.author | Asag, Mazen Abdulwahab Mahyoub Salem | |
| dc.date.accessioned | 2026-08-20T05:17:17Z | |
| dc.date.issued | 2025-10-25 | |
| dc.description | Supervised Prof. Dr. Abdullah Al Mamun, Department of Technical and Vocational Education (TVE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Technical and Vocational Education, 2025 | |
| dc.description.abstract | Generative AI is quickly spreading across higher education, but adoption remains inconsistent in low-resource settings where institutional support and infrastructure lag behind student demand. This study explores how social cues and cognitive evaluations affect engineering students’ adoption of generative AI at an international university in Bangladesh. Using an integrated UTAUT–TAM2 framework, weanalyzethecombinedeffectsofsubjective norms andstudent imagewithjobrelevance, output quality, result demonstrability, perceived usefulness, and perceived ease of use on the intention to use. A cross-sectional survey of 378 undergraduate and graduate engineering students employed 31 validated Likert-scale items; data were analyzed in SmartPLS using PLS-SEM with 5,000-sample bootstrapping after establishing reliability and validity. Group comparisons tested for gender and domestic–international differences. The model explained 64% of the variance in usage behavior. Sixteen of twenty hypotheses were supported: subjective norms significantly influenced perceived usefulness and perceived ease of use, and had a small direct effect on intention; student image affected perceived usefulness only; job relevance strongly predicted perceived usefulness, perceived ease of use, and intention; output quality impacted perceived usefulness and perceived ease of use (but not intention); result demonstrability increased intention and perceived ease of use (but not perceived usefulness). Aligned with TAM, perceived usefulness and perceived ease of use influenced intention, which was the strongest predictor of Usage Behavior. Gender effects were minimal, while domestic students reported higher perceived usefulness, perceived ease of use, job relevance, demonstrability, intention, and use than international peers. Findings underscore the importance of integrating AI into authentic coursework, fostering faculty and peer support, and offering inclusive training to close participation gaps. Limitations include the cross-sectional, single-site design. | |
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SAGE Open. doi: https://doi.org/10.1177/21582440241289835 | |
| dc.identifier.uri | https://repository.iutoic-dhaka.edu/handle/123456789/2778 | |
| dc.language.iso | en | |
| dc.publisher | Department of Technical and Vocational Education (TVE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh | |
| dc.title | Students’ Acceptance and Adoption of Generative AI Tools: Evidence from a Bangladeshi Engineering University | |
| dc.type | Thesis |
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