DOI: 10.4103/jehp.jehp_261_26 ISSN: 2277-9531

Motivation to learn artificial intelligence applications and mental well-being in university students: A cross-sectional study

Afaf Abdelah Othman, Huda Saleh Abdul Rahman Al-Shimeimri, Wafaa Mohamed Ibrahim El Bana, Abdelrahim Fathy Ismail
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BACKGROUND:

Artificial intelligence (AI) is increasingly integrated into higher education, while students’ mental well-being has become a critical concern. The relationship between AI learning motivation and mental well-being was investigated.

MATERIALS AND METHODS:

A cross-sectional correlational study was conducted among 195 students at the Faculty of Education, Najran University, Saudi Arabia, during the first semester of 2025–2026. Data were collected using the motivation to learn AI applications scale and a researchers-developed mental well-being scale (Cronbach’s α = 0.792). Data were analyzed using Statistical Package for the Social Sciences version 26 with Pearson correlation, independent samples t -test, and linear regression ( P ≤ 0.050).

RESULTS:

Motivation to learn AI applications showed a significant positive correlation with mental well-being ( r = 0.458, P < 0.001). Students with higher motivation reported significantly higher mental well-being ( P < 0.001). Female students showed higher motivation ( P = 0.007), while no significant gender differences were found in mental well-being ( P = 0.421). Motivation significantly predicted mental well-being ( β = 0.233, R 2 = 0.184, P < 0.001).

CONCLUSION:

Motivation to learn AI applications was positively associated with students’ mental well-being and explained a modest proportion of its variance.

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