Determinants of artificial intelligence tools use in research among postgraduate students: Evidence from selected public universities in Tanzania

Jeneth Edward Laizer; Nicholaus Mwalukasa; George Firmin Kavishe

doi:10.51867/ajernet.7.3.60

Authors

Jeneth Edward Laizer Sokoine University of Agriculture (SUA), Tanzania https://orcid.org/0009-0007-3145-4709
Nicholaus Mwalukasa Sokoine University of Agriculture, Tanzania https://orcid.org/0009-0001-2284-0639
George Firmin Kavishe Sokoine University of Agriculture, Tanzania https://orcid.org/0009-0006-5493-7829

DOI:

https://doi.org/10.51867/ajernet.7.3.60

Keywords:

AI Tools, Artificial intelligence, Research, Tanzania, UTAUT, University

Abstract

This study examines the determinants of the use of Artificial Intelligence tools in research among postgraduate students at Tanzanian universities, with two objectives: the extent of AI use in research and the factors influencing its use. This study was grounded in the extended Unified Theory of Acceptance and Use of Technology (UTAUT-3). The study adopted a convergent parallel mixed-methods design. The population comprised 2830 postgraduate students; a sample of 350 participants was selected using purposive, stratified, and simple random sampling techniques from the University of Dar es Salaam (UDSM) and Sokoine University of Agriculture (SUA). Quantitative data were collected using a structured questionnaire and analysed using descriptive statistics and multiple linear regressions. The findings showed that ChatGPT and SciSpace were the most frequently used AI tools for idea generation and literature searching, while Grammarly and QuillBot were mainly used for writing improvement and paraphrasing. The regression model was statistically significant (F(10, 310) = 15.311, p < .001) and explained 33.1% of the variance in AI tool use (R² = .331; Adjusted R² = .309). Performance expectancy (β = .345, p < .001), price value (β = .218, p = .004), year of study (β = .113, p = .025), and effort expectancy (β = −.201, p = .007) significantly influenced AI tool use. However, age, sex, social influence, facilitating conditions, habit, and personal innovativeness did not significantly predict AI tool use (p > .05). The study concludes that AI tool use among postgraduate students is still in early development, as they have not yet fully integrated them into all aspects of their research workflow. The study recommends that universities, through the Directorates of Research and Postgraduate (DRP), establish training and workshops on the effective use of AI tools for postgraduate students to maximise their benefits in research processes.

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