Patterns and Extent of Artificial Intelligence Utilisation for Academic Activities Among Undergraduates

Abstract

Background: The rapid proliferation of digital educational technologies has reshaped higher education, encouraging undergraduate students to incorporate automated applications into their study routines. Despite widespread discourse on digital transformation in Nigerian universities, empirical assessments of how students navigate specific functional tiers of artificial intelligence (AI) across diverse disciplines remain scarce.

Objective: Anchored on the Uses and Gratifications Theory (UGT), this study assessed the extent to which undergraduate students utilise distinct functional categories of AI—spanning basic computational tools, automated writing and language verifiers, adaptive tutoring platforms, and emerging autonomous systems—for their academic activities.

Methodology: A descriptive survey research design was adopted. From a population of 35,578 undergraduate students at the University of Port Harcourt, a sample size of 396 was determined using the Taro Yamane formula. An institutional network distribution yielded 381 valid responses via an online questionnaire. Data were analysed using frequency counts, percentage distributions, weighted mean scores, and a standard decision criterion benchmark of 2.50.

Result: Undergraduate students exhibited an overall low extent of AI utilisation for academic purposes (grand mean = 2.32). Relatively higher utilisation was restricted to basic mobile grammar checkers (mean = 2.61), instant computational tools (mean = 2.88), and digital scientific calculators (mean = 2.77). Conversely, institutional plagiarism detection suites (mean = 2.20), adaptive tutoring platforms (mean = 2.35), and advanced algorithmic algebra systems (mean = 1.97) recorded low to very low adoption. Awareness of fully autonomous systems remained conceptual, reflecting their absence from everyday academic workflows.

Conclusion: Undergraduate engagement with AI remains in an early developmental stage, characterised by a pragmatic reliance on accessible, zero-cost mobile applications to address immediate study tasks rather than advanced, research-driven systems. Consequently, university authorities should establish structured AI literacy programmes, secure institutional software licences, and formulate coherent policy frameworks for the ethical integration of emerging technologies into undergraduate learning.