Actionable companion · Feedback & scaffolding

Learning with agency: Adoption of artificial intelligence tools among pharmacy students in Syria: patterns of use, educational perceptions, and institutional barriers

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Background Artificial intelligence tools are reshaping global higher education, yet their deployment and systemic implications within resource-constrained, conflict-affected settings remain poorly characterized. This study examined artificial intelligence adoption prevalence, usage patterns, perceived educational benefits, self-reported usability, attitudes, social norms, and institutional support structures among undergraduate pharmacy students in Syria. Methods A cross-sectional survey was administered between January and February 2026 to 295 pharmacy students across multiple private and public Syrian universities. Data collection utilised a five-construct psychometric instrument. To enhance analytical depth, independent-samples t-tests, one-way Analysis of Variance with post-hoc Tukey’s tests, and multiple linear regression modelling were applied to evaluate variations across student subgroups and determine relational dependencies. Results Overall, 86.8% of participants utilised artificial intelligence tools for academic purposes, with ChatGPT emerging as the dominant platform (96.5%). Core academic use cases included concept explanation (83.2%), drug information retrieval (70.3%), and practice question generation (57.0%). While students reported positive perceived educational benefits (mean = 3.60/5.00) and favourable learning attitudes (mean = 3.66/5.00), institutional support was critically deficient (mean = 1.55/5.00). Formal institutional guidance (93.6%) and training (95.3%) were virtually absent, forcing 95.9% of students to rely entirely on self-directed learning. Inferential analysis revealed significant variations by curricular seniority; fifth-year students demonstrated higher artificial intelligence self-efficacy and usability scores than junior counterparts (P < 0.001). Construct perceptions did not vary significantly by gender, though private university students reported higher institutional support than public university peers (P = 0.036). Conclusions Syrian pharmacy students have autonomously integrated artificial intelligence into their academic routines at rates comparable to high-income settings, yet they do so in the complete absence of institutional scaffolding. The co-occurrence of high adoption and positive attitudes alongside deficient critical verification skills exposes a distinct risk profile: students may develop misplaced confidence in automated pharmacological outputs without possessing the evaluative competencies to intercept factual errors. This pattern demands urgent curricular interventions across low- and middle-income countries. Supplementary Information The online version contains supplementary material available at 10.1186/s12909-026-09708-4.

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

A cross-sectional, descriptive study was conducted using a self-administered online survey distributed between January 27 and February 14, 2026. The study was conducted in full accordance with the ethical principles stipulated in the Declaration of Helsinki (2013 revision). Participant anonymity was maintained throughout all phases of data collection and analysis; no personally identifiable information was retained. Informed electronic consent was obtained from all participants prior to survey access.

Limitations and uncertainty · source passage

Several methodological and structural limitations must be acknowledged when interpreting the current findings. First, the cross-sectional study design captures only a single temporal snapshot within a rapidly evolving AI landscape, preventing the evaluation of long-term behavioural changes or causal trajectories. Sampling was confined to an online convenience sample predominantly drawn from university students, which introduces substantial selection bias. This approach inherently excluded students suffering from severe infrastructural deprivations or total lack of internet access, thereby potentially overestimating general tool adoption rates and underestimating the true magnitude of localised socioeconomic and digital barriers. Furthermore, because the cohort was strictly limited to undergraduate pharmacy students within Syria, the generalisability of the findings to other health professions or alternative regional LMIC educational contexts is constrained. Second, important psychometric and modelling boundaries apply to the survey instrument. The Attitudes Toward AI subscale yielded a Cronbach’s α (α = 0.653) below the standard acceptability threshold due to conceptual heterogeneity introduced by directionally mixed items regarding learning ambivalence. Additionally, the Social Norms and Institutional Support subscales were structurally limited to two-item measures, which masks potential multi-dimensional variances; this is particularly evident given the extreme mean discrepancy observed between the infrastructure and linguistic components of institutional support. Methodologically, while the multi-variable linear regression model (LRM) confirmed robust relational dependencies (R2 = 0.42), the lack of structural equation modelling (SEM) precluded a formal, simultaneous verification of the latent measurement pathways and complex mediational systems hypothesised in the theoretical framework.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
Use plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

A 90-day district pilot

Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Muaaz Alajlani; Afraa Alnokkari; Loai Aljerf. Adoption of artificial intelligence tools among pharmacy students in Syria: patterns of use, educational perceptions, and institutional barriers. BMC Medical Education (2026-06-17). https://doi.org/10.1186/s12909-026-09708-4

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.