GROWTH MINDSET AND ACADEMIC RESILIENCE AS PREDICTORS OF STUDENTS’ ADAPTATION TO AI-ASSISTED LEARNING AMONG UNDERGRADUATES IN TERTIARY INSTITUTIONS IN ANAMBRA STATE

Authors

  • Victor Ugochukwu Ezeonwumelu PhD Department of Educational Foundations, Faculty of Education Nnamdi Azikiwe University, Awka, Anambra State,
  • Mary Nneka Nwikpo PhD Department of Educational Foundations, Faculty of Education Nnamdi Azikiwe University, Awka, Anambra State
  • Azukaego Ifeoma Eluemuno PhD Department of Educational Psychology, Guidance and Counselling, Alvan Ikoku University of Education, Owerri. Imo State, Nigeria
  • Gabriella Nneoma Ufearo Department of Educational Psychology, Faculty of Educational Foundations Studies. College of Education, University of Calabar, Cross River State, Nigeria

Keywords:

Growth mindset, Academic resilience, AI-assisted learning, Student adaptation, Higher education

Abstract

The integration of artificial intelligence (AI) in higher education is accelerating, yet student adaptation
remains a challenge, particularly in developing contexts like Nigeria. This study investigated growth
mindset and academic resilience as predictors of undergraduates' adaptation to AI-assisted learning in
Anambra State. The sample of the study comprised 350 undergradutes selected using Stratified
Random Sampling. The study employed a quantitative correlational design. Data were collected using
standardised scales; Dweck's Growth Mindset Scale and Connor-Davidson Resilience Scale, as well
as a 12-item AI Adaptation Scale, adapted from the Technology Acceptance Model. Data collected
were analyzed using Pearson correlations and multiple regression. Results revealed strong positive
correlations: growth mindset with adaptation (r = .62, p < .001), academic resilience with adaptation (r
= .58, p < .001), and growth mindset with resilience (r = .65, p < .001). Regression analysis indicated
that growth mindset (β = .35, p < .001) and academic resilience (β = .28, p < .001) collectively
explained 45% of the variance in adaptation (R² = .45, F(2, 347) = 140.23, p < .001). These findings
underscore the psychological mechanisms facilitating AI adoption in resource-limited settings.
Implications for educational policy, practice, and future research in African higher education are
discussed, emphasizing interventions to cultivate these traits.

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Published

2026-01-14

Issue

Vol. 12 No. 2 (2026): Unizik Orient Journal of Education (UNIOJE), January, 2026, ISSN: 0794-9525

Section

Articles