Title: Determinants of academic performance among students using generative AI tools in higher education institutions in Tanzania

Authors: Julius Raphael Athuman Mhina; Deogratius Mathew Lashayo

Addresses: Faculty of Computing and Mathematics, Department of Computer Science, Institute of Finance Management (IFM), P.O. Box 3918, 11101 Dar es Salaam, Tanzania ' Faculty of Computing and Mathematics, Department of Mathematics and Actuarial Studies, Institute of Finance Management (IFM), P.O. Box 3918, 11101 Dar es Salaam, Tanzania

Abstract: The rapid adoption of generative artificial intelligence (AI) tools in higher education has raised questions about their influence on students' academic performance. This study investigates the determinants of academic performance in AI-enabled learning environments within Tanzanian middle-level higher education institutions (MLHEIs). A quantitative cross-sectional survey was conducted among 677 students from five institutions. The data were analysed using partial least squares structural equation modelling (PLS-SEM). The results show that task-technology fit (TTF), perceived usefulness and perceived enjoyment significantly influence students' academic performance when using generative AI tools. In contrast, perceived ease of use (PEOU) does not directly translate into improved performance. These findings highlight the importance of aligning AI tools with academic tasks rather than focusing solely on usability features. The study contributes to the emerging literature on artificial intelligence in higher education and provides insights for institutions seeking to integrate AI tools effectively into teaching and learning environments.

Keywords: artificial intelligence; generative artificial intelligence; generative AI tools; academic performance; higher education institutions; middle-level higher education institutions; technology acceptance model; task-technology fit; technology-to-performance chain; perceived usefulness; perceived enjoyment; partial least squares structural equation modelling; Tanzania.

DOI: 10.1504/IJIIE.2026.154764

International Journal of Innovation in Education, 2026 Vol.9 No.3, pp.244 - 263

Received: 09 Mar 2026
Accepted: 11 May 2026
Published online: 13 Jul 2026 *

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