Agentic education · Learning science

Actionable companion · Assessment & reasoning

From AI evidence to responsible practice: Perceptions and attitudes of medical students toward AI in anatomy learning

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not findings 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. Health-professions education findings do not establish effectiveness in K–12 classrooms.

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

Background Artificial intelligence (AI) tools are increasingly being integrated into medical education. However, their use in anatomy learning, particularly among medical students in the Middle East, remains underexplored. Objective To assess medical students’ perceptions, attitudes, and patterns toward AI use for anatomy learning, and to examine associations with sex, academic level, and GPA. Methods A cross-sectional study was conducted among medical students at Jordan University of Science and Technology. A validated 36-item questionnaire (Cronbach’s α = 0.785–0.867) was distributed online. Responses were analyzed using descriptive and inferential statistics, including chi-square tests and Spearman’s rank correlation, to identify factors associated with AI use and perceptions of AI in anatomy learning. Results Among 351 respondents, 300 (85.5%) use AI for anatomy, with 95.7% of them using it during exam preparation. ChatGPT was the dominant platform (96.0%). Gross anatomy was the most studied topic. Students strongly endorsed AI platforms for understanding concepts, immediate answers, 24/7 availability, and time-saving. Major concerns included a lack of visual/lab experience, premium costs, and AI misunderstanding questions. Overall, among the AI-using subgroup, 75.3% of students supported the formal integration of AI, but 52.3% believed AI could replace aspects of traditional teaching. Two gender-related patterns were observed in the unadjusted analyses, but neither remained statistically significant after FDR correction. Higher academic performance was associated with lower agreement that AI could replace traditional teaching or function as a standalone learning resource. Conclusions Medical students use AI as an anatomy learning adjunct, valuing its conceptual and accessibility benefits while recognizing its inability to replace hands‑on dissection. Higher GPA was only associated with lower agreement that AI could serve as a complete standalone resource for anatomy learning. These findings support structured AI literacy training, equitable access, and balanced curricula AI integration that preserves hands-on foundations. Supplementary Information The online version contains supplementary material available at https://doi.org/10.1186/s12909-026-10085-1.

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

This was a cross-sectional study conducted among second- and third-year medical students at Jordan University of Science and Technology (JUST). These academic years were specifically selected because students are actively engaged in anatomy courses and are therefore familiar with the subject matter relevant to this study.

Limitations and uncertainty · source passage

To evaluate potential challenges associated with the use of AI platforms in anatomy education, the students assessed five statements using a five-point Likert scale. For analytical purposes, responses were grouped into Agree (Agree and Strongly Agree), Neutral, and Disagree (Disagree and Strongly Disagree). The distribution of responses is summarized in Table 2. Overall, agreement levels ranged from 50.7% to 66.3%, indicating a moderate acknowledgment of limitations alongside the generally favorable perceptions reported earlier.

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.

  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.
  • Co-design accommodations with learners and specialists. Check access and error burden across groups without inferring subgroup effectiveness from small or unreported samples.

A 90-day district pilot

  1. 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.
  2. 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.
  3. 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.
  4. 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

Ibrahim Hoja; Anas J. Mistareehi; Wafaa Mahmoud; Dana Abumadi; Mohammad Isied; Heba Ghozlan; Ayman Mustafa; Mohammed Z. Allouh. Perceptions and attitudes of medical students toward AI in anatomy learning. BMC Medical Education (2026-08-03). https://doi.org/10.1186/s12909-026-10085-1

Companion prepared 2026-11-01. 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.