Artificial intelligence use patterns, attitudes, and psychological distress among medical students: a cross-sectional survey
- 1Department of Child and Adolescent Psychiatry, Bolu Abant İzzet Baysal University Faculty of Medicine, Bolu, Türkiye
Abstract
Background: Artificial intelligence (AI) tools are widely used by medical students, but associations of general use frequency and AI-related attitudes with psychological distress remain unclear.
Methods: All 1,275 active students at one Turkish medical faculty were invited to an anonymous online survey conducted from 1 April to 1 May 2026; 249 of 251 respondents consented. Measures included general AI use, an investigator-translated 13-item Artificial Intelligence Attitude Scale (ATAI), and the validated Turkish Depression Anxiety Stress Scales-21. A general-distress composite was the primary outcome, and hierarchical regression used HC3 robust standard errors.
Results: AI use was reported by 95.6% of participants, and 50.2% reported daily use. Mean doubled DASS-21 total was 56.97 (SD 31.55). The ATAI three-factor model showed mixed fit (CFI = .898, TLI = .872, RMSEA = .107, SRMR = .070). AI-frequency categories did not improve the adjusted model at the block level (change in R-squared = .016, p = .111; robust global p = .088). Adding exploratory ATAI factors increased R-squared by.052 (p = .002; robust global p = .009); Benefit was inversely, and Risk and Use positively, associated with distress. Subscale sensitivity analyses preserved coefficient directions, but only the inverse Benefit-Anxiety association survived correction across 15 additional focal tests (q = .032).
Conclusion: AI use was nearly universal and served both educational and personal/non-educational purposes, but this sample did not provide stable evidence of an overall association between general use frequency and distress. ATAI associations are exploratory and may reflect measurement or common-method processes. Longitudinal multicenter studies should use formally adapted measures and objective indicators of AI engagement.
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BibTeX
@article{sari2026artificial,
title = {{Artificial intelligence use patterns, attitudes, and psychological distress among medical students: a cross-sectional survey}},
author = {Mesut Sari and Yasemin İmrek and Ali Evren Tufan},
journal = {Frontiers in Medicine},
year = {2026},
month = oct,
doi = {10.3389/fmed.2026.1965749},
url = {https://doi.org/10.3389/fmed.2026.1965749},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial intelligence use patterns, attitudes, and psychological distress among medical students: a cross-sectional survey
AU - Mesut Sari
AU - Yasemin İmrek
AU - Ali Evren Tufan
PY - 2026
DA - 2026/10/22
JO - Frontiers in Medicine
DO - 10.3389/fmed.2026.1965749
UR - https://doi.org/10.3389/fmed.2026.1965749
AB - Background: Artificial intelligence (AI) tools are widely used by medical students, but associations of general use frequency and AI-related attitudes with psychological distress remain unclear. Methods: All 1,275 active students at one Turkish medical faculty were invited to an anonymous online survey conducted from 1 April to 1 May 2026; 249 of 251 respondents consented. Measures included general AI use, an investigator-translated 13-item Artificial Intelligence Attitude Scale (ATAI), and the validated Turkish Depression Anxiety Stress Scales-21. A general-distress composite was the primary outcome, and hierarchical regression used HC3 robust standard errors. Results: AI use was reported by 95.6% of participants, and 50.2% reported daily use. Mean doubled DASS-21 total was 56.97 (SD 31.55). The ATAI three-factor model showed mixed fit (CFI = .898, TLI = .872, RMSEA = .107, SRMR = .070). AI-frequency categories did not improve the adjusted model at the block level (change in R-squared = .016, p = .111; robust global p = .088). Adding exploratory ATAI factors increased R-squared by.052 (p = .002; robust global p = .009); Benefit was inversely, and Risk and Use positively, associated with distress. Subscale sensitivity analyses preserved coefficient directions, but only the inverse Benefit-Anxiety association survived correction across 15 additional focal tests (q = .032). Conclusion: AI use was nearly universal and served both educational and personal/non-educational purposes, but this sample did not provide stable evidence of an overall association between general use frequency and distress. ATAI associations are exploratory and may reflect measurement or common-method processes. Longitudinal multicenter studies should use formally adapted measures and objective indicators of AI engagement.
SN - 2296-858X
ER -