How are Iranian medical students using AI?
In a stratified survey of 384 students at one medical university, research, writing and self-directed learning attracted the highest AI-use preference scores. The study measured stated preferences, not competence, accuracy or patient safety.
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At a glance
- 1The 2025 cross-sectional survey used stratified random sampling to recruit 384 undergraduate and postgraduate students at Shiraz University of Medical Sciences.
- 2Research and writing scored 2.66 on a four-point scale and self-learning 2.61, while design and programming scored 1.54 and art 1.68.
- 3The findings describe one university's preferences; they do not measure actual usage logs, AI literacy, quality of work, clinical competence or patient outcomes.
Research topic
Which academic, clinical, technical and creative uses of AI students at Shiraz University of Medical Sciences say they prefer, and how preferences vary by level and demographic factors
The answer: students preferred academic help, but the survey cannot say whether the help was good
Medical students at Shiraz University of Medical Sciences reported their strongest AI-use preferences in research and writing, followed by self-directed education. Research and writing averaged 2.66 on the study's four-point scale, while self-learning averaged 2.61; both were above the researchers' 2.5 reference point. Health counselling and curiosity or entertainment were lower, and design, programming and art received the weakest scores.
These figures should guide questions for curriculum teams, not certify competence. The study did not inspect prompts, verify generated answers, score research quality or test whether a student could recognise a dangerous clinical error. Preference is also not frequency: a respondent can like an activity without using it often, or use a tool often while distrusting it. The useful finding is where students say demand exists and where structured teaching may be most urgent.[1]
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Who answered and how the study measured preference
The descriptive cross-sectional study was conducted in 2025 at one Iranian medical-sciences university. Researchers used stratified random sampling and recruited 384 undergraduate and postgraduate students. Data were collected from July to October 2025 using a self-administered questionnaire called AI-UPSS. The instrument covered eight domains and reported high internal consistency, with Cronbach's alpha of 0.935. Students rated items on a four-point Likert scale.
The analysis compared each domain with a mean of 2.5 and used independent t-tests and one-way analysis of variance to examine group differences. That produces a clear map of responses but also creates several interpretive cautions. A midpoint comparison is not a direct behavioural benchmark, many subgroup tests increase the chance of chance findings, and statistical significance does not show that a difference is educationally important. The article reports associations, not causal effects of age, gender, grade point average or stage of study.[1]
What the eight domains show
Research and writing recorded a mean of 2.66 with a standard deviation of 0.79, and self-learning or education averaged 2.61 with a standard deviation of 0.81. Curiosity and entertainment averaged 2.36, while health counselling averaged 2.40. Design and programming was lowest at 1.54, followed by art at 1.68. The pattern suggests that students primarily associate AI with studying and producing academic material rather than building systems or creating media.
That pattern does not reveal which tools students used or whether they distinguished summarisation, translation, literature searching, coding, drafting and answer generation. It also does not show whether health-counselling use involved hypothetical questions, personal advice or information about a patient. Each use carries different privacy and safety risks. Curriculum planners therefore need task-level follow-up rather than treating a domain average as permission to approve every tool or workflow inside it.[1]
Subgroup differences are signals for support, not labels for students
The paper reports gender differences in art, self-learning and health counselling, and associations between grade point average and content development, design and programming, and art. Students with lower GPAs reported higher preference in those latter domains. Undergraduates also reported stronger preferences across several areas, including curiosity and health counselling. Age and field of study were not significant in the reported comparisons.
It would be a mistake to infer that lower-performing students are replacing study with AI, or that undergraduates are less safe users. The cross-sectional questionnaire does not establish direction, motivation or outcome. Students with lower grades might be seeking additional support; stronger users might be exploring more formats; and access, workload or teaching style may explain part of the pattern. Universities should use the differences to target inclusive support and qualitative inquiry, not to automate suspicion or disciplinary surveillance.[1]
What teachers and clinical supervisors can do now
The strongest demand sits close to assessed work. Teachers can respond with assignment-level rules that distinguish permitted brainstorming, language support, evidence searching, coding and drafting. Students need examples of how to document assistance, verify citations and preserve their own reasoning. Research methods courses should explicitly cover fabricated references, biased summaries, confidential data, authorship expectations and when an AI output must be checked against a primary clinical source.
Clinical supervisors need firmer boundaries. Students should not enter identifiable patient information into unapproved systems, and an AI-generated explanation must not replace escalation to a qualified clinician. Health-counselling interest makes this especially important: a tool can sound confident while missing red flags or local care pathways. Safe education should include adversarial examples, uncertainty and correction—not only efficient prompting. Human feedback and non-AI alternatives should remain available so access or disability needs do not become reasons for unfair assessment.[1]
Limits, disclosures and evidence that would change the assessment
The study covers one university and one data-collection period, so it cannot represent all Iranian medical students. The questionnaire is self-reported and may be influenced by recall, social desirability and respondents' own interpretation of AI. Internal consistency does not prove that the scale captures safe or effective use. The study does not measure actual tool logs, response accuracy, research integrity, exam performance, clinical reasoning, patient communication or later professional behaviour.
The authors report no funding and no competing interests. Ethics approval came from the Shiraz University of Medical Sciences biomedical research committee, and participation was anonymous and voluntary with electronic consent. Confidence would rise with multi-university studies using the same transparent instrument, task-level usage records with privacy safeguards, objective assessments of verification skill and longitudinal follow-up. A controlled curriculum evaluation showing better reasoning and fewer unsafe errors would support intervention claims that this preference survey cannot make.[1]
What this means for people
- Students need clear rules and verification practice where AI already overlaps with research and assessed writing.
- Teachers can use the preference pattern to prioritise support without treating self-reported use as misconduct or competence.
- Patients remain protected only if clinical supervisors keep confidential data and consequential decisions inside approved human-led workflows.
Global context
The Iranian setting expands evidence beyond the North American and European institutions that dominate AI-in-education discussion. Medical curricula, connectivity, language, tool access and assessment rules differ across countries, so the ranking of preferred uses should not be assumed universal. The design is readily replicable, but useful comparison requires the same instrument, sampling transparency and task-level context.
What the evidence does not yet show
- The sample came from one Iranian medical university and cannot establish national prevalence.
- All outcomes are self-reported preferences rather than observed use, competence or performance.
- The cross-sectional design cannot explain why demographic or academic groups differed.
- The study did not identify specific tools, prompting practices, privacy exposure or clinical-risk scenarios.
- Multiple subgroup comparisons may produce chance findings, and statistical significance does not establish practical importance.
What to watch next
- Replication across Iranian public and private medical schools, stages of training and regions.
- Direct assessment of citation checking, hallucination detection, patient-data handling and clinical escalation.
- Longitudinal or controlled studies of structured AI literacy teaching and objective learning outcomes.
- Task-level evidence distinguishing language support, research search, drafting, coding and clinical advice.
Living evidence record
Impact record IAI-0HXXAJZ
Evidence stage
Studied
Confidence
Supported
Reporting basis
Source analysis
Independent or research support
Present
Record status
Monitoring
Last checked
11 October 2026
Source trail
1 direct source across 1 source type.
People impact
Documented in this record.
Uncertainty
Limits and next checks are explicit.
Stages describe the evidence available—not whether a technology is good or bad. See the public method.
Single-source reporting disclosure
This record analyses one direct source. It can establish what Scientific Reports published or reported, but it is not independent corroboration of every performance claim or predicted outcome. The confidence label will change only when broader evidence is added.
Evidence trail
Sources used for this report
Links checked 11 October 2026
This report is labelled source analysis. We summarise and analyse source material in our own words; company statements remain attributed claims until independently supported. Translated summaries preserve the meaning of the original source and link back to it. Read our editorial standards.
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