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Education·P Space·Evidence-backed problem·Published 2026-07-20

Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework

The rapid evolution of Artificial Intelligence (AI) and its widespread adoption have given rise to a critical need for understanding the underlying factors that shape users' behavioral intentions. Therefore, the main objective of this study is to explain user perceived behavioral intentions and use behavior of AI technologies for academic purposes in a developing country. This study has adopted the unified theory of acceptance and use of technology (UTAUT) model and extended it with two dimensions: trust and pri…

TRV-2026-0362Peer-reviewedPermanent record — cite & verify
Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework

"UNIVERSITY_OF_THE_FRASER_VALLEY_PHOTOGRAPHY" by University of the Fraser Valley is licensed under CC BY 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/2.0/.

The quick read

A September 2024 peer-reviewed study examined AI adoption for academic purposes in a developing country using a UTAUT model extended with trust and privacy, surveying 310 teachers, researchers, and students who use AI.

The findings matter because they quantify how trust encourages and privacy concerns discourage academic AI adoption, yet the cross-sectional survey design and single-country developing context leave open whether these intention patterns translate into sustained use elsewhere.

Main points
  • Study extended UTAUT with trust and privacy to explain AI adoption for academic purposes in a developing country.
  • Data collected from 310 AI users including teachers, researchers, and students.
  • Facilitating condition, behavioral intention, and privacy had significant positive impact on use behavior, while trust showed no significant link to use behavior.
Problem

Privacy concerns were significantly associated with lower intention to use AI technologies for academic purposes.

The rundown

Researchers applied a trust and privacy-augmented UTAUT model to 310 teachers, researchers, and students who use AI, testing how performance expectancy, effort expectancy, social influence, trust, privacy, and facilitating conditions relate to intention and actual use.

Results showed behavioral intention positively linked to trust and other UTAUT factors, while privacy negatively linked to intention but positively linked to use behavior, and trust had no significant direct link to use behavior, prompting recommendations around security, transparency, and credible endorsements.

Sources

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The debate