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

Abstract

This study examined how artificial intelligence (AI) utilisation and self directed research learning influenced research productivity among postgraduate students in public universities in Cross River State, Nigeria. A predictive correlational design was adopted to answer four research questions. The population comprised 6,522 postgraduate students from two public universities, while a sample of 450 participants was selected through stratified random sampling to ensure adequate representation across gender, degree level, and age groups.

Data were collected using a validated questionnaire that measured AI utilisation, self directed research learning, and research productivity, with all scales showing high internal consistency (α > .80). Simple and hierarchical regression analyses were used to test the proposed relationships.

The findings indicated that AI utilisation significantly predicted self directed research learning (β = .32, p < .001) and research productivity (β = .23, p < .001). Self directed research learning exerted a stronger direct effect on research productivity (β = .51, p < .001) and fully mediated the relationship between AI utilisation and research productivity.

The study concluded that AI tools contribute to improved research productivity primarily by encouraging independent learning behaviours and sustained scholarly engagement. It recommended that postgraduate programmes strengthen AI literacy, research mentorship, and digital learning environments that promote autonomous and responsible research practice.

Keywords

Artificial intelligence, digital literacy, learning autonomy, postgraduate education, research engagement, research productivity, self directed research learning

Citation