Actionable companion · Assessment & reasoning

Learning with agency: Effectiveness of a “5E+AI” teaching model on science communication in medical students: a contribution to global health literacy

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. Health-professions learning must be distinguished from K–12 instruction.

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

Background The improvement of global health literacy depends on health professionals having effective public science communication capabilities. However, worldwide, medical education systems still largely lack training in science communication for medical students. How to construct a systematic and scalable model for cultivating scientific communication capabilities is a key issue currently faced by the reform of medical education. Objective Our study aims to evaluate the effect of the “5E+AI” teaching model on the cultivation of medical students' science communication capabilities, and to assess the impact of this model on students' learning participation, acquisition of medical knowledge, and creation of popular science works. Methods A historical controlled quasi-experimental design was employed. Twenty-nine medical students enrolled in the “Fundamentals and Practice of Medical Science Popularization” course at Sun Yat-sen University during the 2025-1 semester served as the historical control group (receiving lecture-based, instructor-centered teaching with PowerPoint presentations and group discussions, but without AI-assisted tools), while 28 students enrolled in the same course during the 2025-2 semester served as the experimental group (receiving the “5E+AI” teaching model). The two groups are the same in terms of teaching content, total class hours, teachers, teaching materials, assessment methods, etc. The evaluation indicators include participation score, medical knowledge test score, final score of the science communication project and student self-report questionnaire. Intergroup comparisons were performed using the Mann–Whitney U-test. Results The experimental group scored significantly higher than the control group on all three primary outcome measures: participation scores (median 85.00 vs. 80.00, U = 220.00, P = 0.002), medical knowledge test scores (median 100.00 vs. 80.00, U = 210.00, P = 0.001), and science communication project scores (median 85.00 vs. 85.00, U = 213.00, P = 0.001). In addition, the experimental group reported significantly higher scores than the control group across all three dimensions of the self-report questionnaire (P < 0.05). Conclusions The “5E+AI” teaching model helps enhance medical students' classroom participation, ability to acquire medical knowledge and science communication skills. In this teaching model, artificial intelligence can help students address the issues of knowledge deficiency and resource constraints. Meanwhile, the 5E teaching framework provides students with structured teaching scenarios. Together, they enhance teaching effectiveness and offer replicable and adaptable local solutions for the reform of medical communication education in the context of global health literacy promotion.

What kind of evidence is this?

Supporting AI learning science · Comparative / pre-post study reported

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 course is offered as an elective and draws limited enrollment each semester. These constraints made a randomized concurrent design unfeasible. We therefore adopted a historical controlled quasi-experimental design.

Limitations and uncertainty · source passage

We also acknowledge several limitations. Because the course was offered only once per year, we could not recruit a concurrent control group. Therefore, a historical controlled quasi-experimental design was adopted, which may introduce potential confounding factors. In addition, students were aware of the instructional approach they received because the two cohorts were recruited from different semesters, and this awareness may have introduced expectation bias. Although the control group did not receive structured AI-assisted teaching activities, we could not completely exclude the possibility that students independently accessed publicly available AI tools outside the course. Furthermore, the sample size was modest, the study was conducted at a single institution, and all assessments were performed immediately after the course, limiting the generalizability and long-term interpretation of our findings. Future studies should include larger multi-center samples, concurrent randomized controlled designs, longer follow-up periods, and systematic monitoring of AI usage behaviors to further validate the effectiveness and broader applicability of the model.

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.

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

Huiru Dai; Minling Liu; Tingwei Li; Jiancheng Wang; Shuo Fang. Effectiveness of a “5E+AI” teaching model on science communication in medical students: a contribution to global health literacy. Frontiers in Public Health (2026-08-07). https://doi.org/10.3389/fpubh.2026.1916714

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.