Journal of Academic Research for Humanities (JARH) is a double-blind, peer-reviewed, Open Free Access, online Multidisciplinary Research Journal
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Vol. 5 No. 4 (2025)
The International "Journal of Academic Research for Humanities"

Research Articles

Academic Self-Efficacy, Academic Stress, and AI Dependency: : A Predictive Model for Undergraduate Thesis Students

Published 31-12-2025

  • Umm e Habiba
    • ,
  • Zuhaa Hassan
    • ,
  • Fatima Sahar

Umm e Habiba
PhD Scholar, Institute of Applied Psychology, Social Sciences, University of Punjab, Lahore, Punjab, Pakistan

Zuhaa Hassan
PhD Scholar, Institute of Applied Psychology, Social Sciences, University of Punjab, Lahore, Pakistan

Fatima Sahar
Alumni, School of Psychology, Faculty of Social Sciences, Ivy College of Management Sciences. Arden University (UK), Lahore, Punjab, Pakistan

Abstract

The increasing use of artificial intelligence (AI) tools in higher education has raised concerns about students’ growing dependency on AI, particularly during academically demanding tasks such as undergraduate thesis writing. Excessive reliance on AI may undermine independent thinking and critical reasoning, making it essential to understand the psychological factors that contribute to AI dependency. Academic self-efficacy and academic stress play a central role in shaping how students engage with AI tools. Purposive convenience sampling was used to select 120 individuals, ages 21-24 (22.08 (.98)). The correlational design was used in the study. The General Academic Self-Efficacy Scale (GASE-5) by Perception of Academic Stress Scale (PAS-18) by Dalia Bedewy & Gabriel (2015), and a modified version of the Technology Acceptance Model TAM Questionnaire (AITU-5) by Oluwanife-Falebita (2024) were used. The primary research variables met normality assumptions. Correlational results revealed that academic self-efficacy and AI use were significantly positively related, while academic stress and AI use were significantly negatively correlated. Multiple Hierarchical regression analysis revealed 10% variance in AI dependency founding academic self-efficacy as a significant positive predictor (B = .32, p < .001). All the dimensions of academic stress remained non-significant predictors of AI dependency in model 2, while increasing the explained variance to 14%. Practically, the study underscores the need for students to develop balanced AI use habits, for educators to integrate AI literacy while promoting independent academic skills, and for universities to design learning environments that encourage ethical and responsible use of AI without compromising academic integrity and originality.

Keywords

Academic self-efficacy, Academic stress, Use of Artificial Intelligence (AI), Undergraduate, Theses Students

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