Enigma of generative AI literacy, self-efficacy, attitude, interest and dependence-induced task completion among university students: evidence from Ghana

Abstract

This research aims to investigate how generative AI literacy, self-efficacy, attitude, interest and dependence interact to influence academic work completion among university students in Ghana. It also seeks to identify the psychological pathways through which AI competence is associated with students' behaviour in the context of increasing generative AI adoption. This research used a quantitative cross-sectional study to collect the data from 466 undergraduate students at KNUST. Partial least squares structural equation modelling (PLS-SEM) was used to analyse the data among five constructs. The results reveal a progressive behavioural pathway in which generative AI literacy positively predicts students' attitudes toward AI, which in turn strengthens their interest. This heightened interest significantly enhances students' self-efficacy in using AI, ultimately leading to dependence-induced task completion. Notably, self-efficacy emerged as the strongest predictor of task completion, underscoring both the empowering potential of AI use and the risk of increasing reliance on AI for academic work. The cross-sectional design of this study limits its ability to interpret causality between the variables examined. Again, self-report measures are subject to common-method bias. Since the sample involved only KNUST undergraduate students, generalisation of the results cannot be assumed for other academic institutions. Finally, Partial Least Squares Structural Modelling assumes a linear relationship amongst all factors, thus may miss a nonlinear relationship that occurs within students' behaviours. The findings provide actionable guidance for universities and policymakers seeking to advance SDG 4 (Quality Education) through responsible generative AI integration. Higher education institutions should embed structured AI literacy and ethical-use training within curricula to strengthen student self-efficacy while preventing unhealthy dependence. Assessment practices should be redesigned to emphasise critical thinking, creativity and reflective engagement rather than automated task completion. At the policy level, national higher education authorities are encouraged to develop AI-use guidelines that promote equity, academic integrity and learner autonomy. These practices support Emerald's impact agenda by translating empirical evidence into scalable educational interventions that enhance learning quality, student welfare and sustainable digital transformation in higher education. The study underscores the need for institutional and national policies that guide responsible generative AI use in higher education while protecting student welfare. As AI literacy and self-efficacy increase, so does the risk of excessive dependence, with potential consequences for independent thinking, academic integrity and long-term cognitive development. Universities, particularly in developing contexts such as Ghana, must establish clear AI governance frameworks, embed ethical AI literacy into curricula and redesign assessments to prioritise critical engagement over automated outputs. Policy interventions should also ensure equitable access to AI training and support systems that promote student autonomy, well-being and sustainable learning practices in AI-mediated academic environments. This study provides an understanding of GenAI use in African higher education by integrating AI literacy, self-efficacy, attitude, interest and dependence into one behavioural model that has been clearly tested in the Ghanaian context. The study provides new insights into how psychological factors and usage-based patterns shape students' dependence on Generative AI for class task completion, providing great insight for teachers who seek to continuously integrate AI into their work. The findings also support curriculum design and policy development by identifying the competencies and behavioural risks that must be addressed to guide effective AI use within universities.