Self-directed research learning as a mediator between artificial intelligence utilisation and research productivity: A hierarchical regression approach

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Source: OpenAlex

Last checked: 2026-08-15 16:02:04

Abstract

This study examined how artificial intelligence (AI) utilisation and self-directed learning (SDRL) influenced research productivity among postgraduate students in public universities in Cross River State, Nigeria. A predictive correlational design was adopted to address four research questions. The population comprised 6,522 postgraduate students from two universities, and a sample of 450 was drawn using stratified random sampling to ensure fair representation across gender, degree levels, and age groups. Data were collected using a validated questionnaire that measured AI utilisation, SDRL, and research productivity, with high internal reliability (α > .80). Data were analysed using simple and hierarchical regression models. Findings revealed that AI utilisation significantly predicted SDRL (β = .32, p < .001) and research productivity (β = .23, p < .001). SDRL exerted a stronger influence on research productivity (β = .51, p < .001) and fully mediated the relationship between AI utilisation and productivity. The study concluded that AI tools contribute to higher research productivity mainly by fostering independent learning habits and motivation for self-directed inquiry. It was recommended that postgraduate curricula incorporate AI literacy, mentorship, and digital learning environments that encourage autonomy and sustained scholarly engagement.

Keywords

digital literacy, learning autonomy, postgraduate education, research engagement

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