Artificial intelligence was nearly ubiquitous among medical students surveyed at a Turkish university, with 95.6% reporting previous use and 50.2% saying they used an AI tool daily. Yet the single-center cross-sectional study found no stable evidence that general AI-use frequency was associated with psychological distress. More complex associations appeared for students’ attitudes: perceived benefit was linked with lower general distress, while perceived risk and willingness to use AI were linked with higher distress. These patterns do not show that AI use causes psychological harm or benefit, because attitudes and symptoms were measured simultaneously and the survey cannot establish which came first.
The anonymous online survey was conducted from 1 April to 1 May 2026 at Bolu Abant Izzet Baysal University Faculty of Medicine. All 1,275 active students across the six-year medical program were invited, and 249 consenting participants completed the questionnaire, representing 19.5% of the eligible population. Their mean age was 21.49 years, with a range of 18 to 34; 69.1% were women, 30.5% were men, and one participant preferred not to report sex. Previous psychiatric treatment was reported by 24.9% of respondents and current treatment by 11.6%. The authors noted that the voluntary sample may have been enriched for students with greater mental-health concerns, so the results cannot be interpreted as population prevalence.
Students used AI for a mixture of academic and personal purposes. Among the 238 participants who had used AI, 84.5% selected course study or review, 71.8% selected examination preparation, 65.1% selected medical terminology or concept learning, and 62.6% selected research or literature searching. The same proportion, 71.8%, reported personal or non-educational use, while 38.2% used AI for clinical cases or reasoning practice. ChatGPT was the most commonly selected tool, followed by Google Gemini. Among users, 58.4% had been using AI for more than one year, indicating that the survey captured sustained engagement as well as possible recent adoption.
Psychological symptoms were measured with the validated Turkish 21-item Depression Anxiety Stress Scales, or DASS-21, covering the previous week. The primary outcome was a general-distress composite formed by summing all 21 items and doubling the result to match the DASS-42 metric, producing a possible range of 0 to 126. The mean was 56.97 with a standard deviation of 31.55. Internal consistency for the composite was high, with Cronbach’s alpha and McDonald’s omega both 0.951. A one-factor model fit moderately rather than strongly, while a proposed three-factor model for depression, anxiety and stress fitted better but also fell short of conventional absolute-fit benchmarks. The total was therefore treated as a shared symptom burden, not as proof that the three dimensions were identical. DASS severity categories were treated as screening descriptors, not psychiatric diagnoses or estimates of prevalence in the eligible student population.
The central regression used three blocks and ordinary least squares with HC3 robust standard errors. The first block included training year, sex and previous psychiatric treatment and explained 10.6% of the variance in general distress. Adding three categories of AI-use frequency increased explained variance by only 0.016, a change that was not statistically significant: the conventional test gave p = 0.111 and the robust global test p = 0.088. Adding the study’s three exploratory Artificial Intelligence Attitude Scale factors increased explained variance by another 0.052, producing an overall R-squared of 0.175 and an adjusted R-squared of 0.133. Thus, frequency did not improve the adjusted model as a block, whereas attitudes added exploratory explanatory information. Because the analytic models were specified after data collection and were not preregistered, all inferential results were hypothesis-generating rather than confirmatory.
Per one-standard-deviation increase in an attitude factor, Benefit was associated with a 4.90-point lower general-distress score, with a 95% confidence interval from −9.14 to −0.65 and a false-discovery-rate-adjusted q value of 0.036. Risk was associated with a 4.98-point higher score, with a confidence interval of 0.87 to 9.09 and q = 0.036. Use was associated with a 4.69-point higher score, with a confidence interval of 0.26 to 9.12 and q = 0.038. The apparently conflicting Benefit and Use relationships complicate any simple “helpful versus harmful” interpretation. A student might value AI’s educational benefits while also using it more frequently and perceiving greater risks; alternatively, distress and academic pressure could influence both attitudes and willingness to engage.
The findings were directionally similar across the depression, anxiety and stress subscales: Benefit had a negative coefficient and Risk and Use had positive coefficients in each model. Statistical support was much weaker, however, after correction for the 15 additional focal tests. Only the inverse relationship between Benefit and Anxiety survived that correction, with a coefficient of −2.39 points, a 95% confidence interval of −3.91 to −0.87, a nominal p value of 0.002 and an adjusted q value of 0.032. AI-use frequency remained nonsignificant at the block level for all three outcomes. These subscales came from the same participants and do not constitute independent replications, while the strong correlations among depression, anxiety and stress also limit the interpretation of separate domain-specific effects.
Measurement uncertainty was substantial. The 13-item attitude scale had three proposed domains—Benefit, Risk and Use—and was originally developed for Turkish pre-service teachers rather than medical students. Its three-factor structure performed much better than a one-factor alternative, with standardized loadings from 0.520 to 0.938. Internal consistency ranged from 0.775 to 0.931 for Cronbach’s alpha and from 0.774 to 0.933 for McDonald’s omega. Yet its absolute fit was mixed, with a comparative fit index of 0.898, Tucker–Lewis index of 0.872, root mean square error of approximation of 0.107 and standardized root mean square residual of 0.070. The wording was translated by one investigator from the English version rather than formally adapted for cross-cultural use. Nine of the 13 administered items differed lexically or in phrasing from the published Turkish form, and one difference may have broadened the item’s content. The authors therefore treated the factor scores as exploratory and conditional on the structural evaluation.
Several design features also limit what can be inferred. The study was cross-sectional, single-center, voluntary and based entirely on self-report. AI frequency did not capture session duration, task complexity, learning quality, verification of outputs or degree of reliance, and nearly four-fifths of respondents used AI at least a few times a week, leaving limited contrast at the lower end of exposure. Students with distress or strong views about AI may also have been more likely to participate. Training-year differences cannot demonstrate improvement over time because each cohort was assessed once; the survey overlapped a first-year midterm period, and individual workload and assessment timing were not recorded. The results also do not demonstrate that any single AI tool is responsible for the observed patterns. Prior research cited by the study found that mental-health problems predicted later AI dependence among adolescents, making reverse causation a plausible explanation for the positive Use association, but the current data cannot test that pathway.
The results point to a need for longitudinal, multicenter research rather than a simple verdict on AI in medical education. Future studies would need formally adapted attitude instruments, validated well-being measures, objective or diary-based records of AI engagement, and information about assessment timing and specific educational tasks. Qualitative work could help explain how students combine perceived benefits, risks and active engagement. Until stronger evidence is available, the survey’s most defensible conclusion is that AI use was widespread among these students, but its frequency alone did not provide stable evidence of an overall relationship with distress. The exploratory attitude associations require replication and cannot be interpreted as causal effects, psychological diagnoses or evidence that greater AI exposure is inherently advantageous or harmful.
Subject of Research: A cross-sectional survey of AI use patterns, attitudes and psychological distress among 249 medical students at one Turkish university.
Article Title: Artificial intelligence use patterns, attitudes, and psychological distress among medical students: a cross-sectional survey
Article References: Sari, M., İmrek, Y., & Tufan, A. E. (2026). Artificial intelligence use patterns, attitudes, and psychological distress among medical students: a cross-sectional survey. Frontiers in Medicine, 13, Article 1965749. https://doi.org/10.3389/fmed.2026.1965749
Image Credits: AI Generated
DOI: 10.3389/fmed.2026.1965749
Keywords: artificial intelligence, medical students, psychological distress, AI attitudes, medical education, DASS-21, cross-sectional survey, mental health, digital learning, survey research, Artificial, intelligence
