Exploring the Impact of AI Use on Students' Mental Health and Academic Outcomes: A Structural Equation Modelling Approach

Abstract

ABSTRACT Background: The rapid adoption of artificial intelligence (AI) in higher education has raised concerns regarding its effects on students’ mental well-being. Understanding how AI use contributes to psychological strain is essential for safeguarding student health. Objective: This study examines the impact of AI use on university students’ mental health, specifically stress, confusion, and peer pressure, and investigates whether these factors mediate the relationship between AI use and academic outcomes. Methods: A cross-sectional online survey was conducted, and data were analysed using Structural Equation Modelling (SEM). Confirmatory factor analysis validated the measurement model, and bootstrapped mediation analysis assessed indirect effects through mental health indicators. Results: AI use showed a weak positive association with psychological strain, indicating that higher engagement with AI tools may heighten stress and related mental health concerns. While AI use had a significant negative direct effect on academic outcomes, the mediating effect of mental health was non-significant. Conclusion: Increased AI use may pose emerging mental health risks for students, even though these psychological effects do not fully mediate academic performance. The findings highlight the need for health-informed AI literacy initiatives and student support mechanisms to promote safe and balanced use of AI in educational settings.

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