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<p>Generative artificial intelligence (GenAI) is becoming part of everyday academic work, yet empirical evidence on student adoption remains concentrated in comparatively well-resourced higher education systems. This study examines GenAI adoption among surveyed Somali university students, a context where access to AI tools is shaped by mobile connectivity, device availability, institutional policy development, trust, and academic-integrity norms. Drawing on an extended Technology Acceptance Model (TAM), the study tests the effects of academic integrity, trust, motivation, institutional support, Somali access conditions, perceived ease of use, and perceived usefulness on behavioral intention to adopt GenAI. A quantitative cross-sectional survey of 310 students was analyzed using partial least squares structural equation modeling (PLS-SEM). The measurement model showed acceptable reliability and convergent validity, with composite reliability values ranging from 0.884 to 0.941 and average variance extracted values ranging from 0.605 to 0.762. Discriminant validity was generally supported, although the HTMT value between behavioral intention and trust was marginally above 0.90 and is interpreted cautiously. The structural model explained 63.7% of the variance in behavioral intention. Perceived ease of use strongly predicted perceived usefulness (β = 0.646, <i>p</i> &lt; 0.001) and behavioral intention (β = 0.411, <i>p</i> &lt; 0.001), while perceived usefulness also predicted intention (β = 0.404, <i>p</i> &lt; 0.001). Motivation, trust, academic integrity, and Somali access conditions were significant positive predictors of intention, whereas institutional support was not significant. The findings suggest that GenAI adoption in this setting is driven more by usability, perceived academic value, trust, motivation, and practical access than by visible institutional support. The study extends TAM by locating GenAI adoption within a fragile, infrastructure-constrained African higher education context and offers implications for mobile-first AI literacy, academic-integrity guidance, and context-aware governance.</p>

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Trust, academic integrity, and access constraints in generative AI adoption: an extended TAM study of Somali higher education

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月24日

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  • Abdullahi Salad Abdi,
  • Abdulkadir Mohamed Abdullahi

摘要

Generative artificial intelligence (GenAI) is becoming part of everyday academic work, yet empirical evidence on student adoption remains concentrated in comparatively well-resourced higher education systems. This study examines GenAI adoption among surveyed Somali university students, a context where access to AI tools is shaped by mobile connectivity, device availability, institutional policy development, trust, and academic-integrity norms. Drawing on an extended Technology Acceptance Model (TAM), the study tests the effects of academic integrity, trust, motivation, institutional support, Somali access conditions, perceived ease of use, and perceived usefulness on behavioral intention to adopt GenAI. A quantitative cross-sectional survey of 310 students was analyzed using partial least squares structural equation modeling (PLS-SEM). The measurement model showed acceptable reliability and convergent validity, with composite reliability values ranging from 0.884 to 0.941 and average variance extracted values ranging from 0.605 to 0.762. Discriminant validity was generally supported, although the HTMT value between behavioral intention and trust was marginally above 0.90 and is interpreted cautiously. The structural model explained 63.7% of the variance in behavioral intention. Perceived ease of use strongly predicted perceived usefulness (β = 0.646, p < 0.001) and behavioral intention (β = 0.411, p < 0.001), while perceived usefulness also predicted intention (β = 0.404, p < 0.001). Motivation, trust, academic integrity, and Somali access conditions were significant positive predictors of intention, whereas institutional support was not significant. The findings suggest that GenAI adoption in this setting is driven more by usability, perceived academic value, trust, motivation, and practical access than by visible institutional support. The study extends TAM by locating GenAI adoption within a fragile, infrastructure-constrained African higher education context and offers implications for mobile-first AI literacy, academic-integrity guidance, and context-aware governance.