Actionable companion · Assessment & reasoning

Learning with agency: Preparing Generation Z of Health Professions for Artificial Intelligence Revolution Through Hacking Education: An Interventional Study

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): Age / stage not established. Verify the participant population and task before transferring the findings.

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

ABSTRACT Background and Aims The integration of artificial intelligence into healthcare is rapidly expanding, yet formal AI education for health profession students remains limited. Generation Z students, as digital natives, require innovative instructional methods tailored to their learning preferences. This study aimed to investigate the effectiveness of an innovative course based on hacking education for Z‐generation health profession students about artificial intelligence. Methods This was a single‐group pre‐ and post‐test interventional study conducted with 81 health profession students. Educational needs were identified through an expert panel. A 100‐h flipped classroom course incorporating group discussions, communities of practice, peer teaching, and gamification was delivered. Pre‐ and post‐course questionnaires assessed students' familiarity with key AI concepts. Results Post‐course assessments showed a significant improvement in students' familiarity with AI‐related domains, including programming languages (p < 0.001), machine learning (p < 0.001), AI applications in healthcare (p < 0.001), data science (p < 0.001), image processing (p < 0.001), deep learning (p < 0.001), neural networks (p < 0.001), and statistics (p < 0.001). Conclusion The hacking education‐based AI course effectively enhanced students' AI competencies. Given the increasing role of AI in healthcare, integrating structured AI training into medical curricula is essential to prepare future healthcare professionals for AI‐driven clinical environments.

What kind of evidence is this?

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

Stage: Age / stage not established. 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

The data were collected from 81 health profession students at Kerman University of Medical Sciences who entered the study by the census method. Students were invited via messenger apps to participate in this voluntary, extracurricular course about AI. The inclusion criteria consisted of an interest in AI in the healthcare profession. The exclusion criteria included failure to complete relevant prerequisite courses, failure to complete assignments on time, and dropping out of the course.

Limitations and uncertainty · source passage

One of the limitations was that most participants stated that they took part in the course because of their intrinsic interest in AI. This self‐selection bias may have led to an overestimation of the course's effectiveness. Another limitation is the small sample size and lack of long‐term follow‐up of students. Implementing longitudinal studies would provide deeper insights into the sustained impact of AI education. Also, our results are interpreted without a control group. Future studies should incorporate control groups or comparison groups to better isolate the impact of the intervention. We acknowledge that the use of messenger apps for recruitment may have inadvertently excluded students less engaged with digital communication tools, which is recognized as a limitation of this study. Although census sampling was used, we recognize the potential for selection bias, as participation may have been influenced by self‐motivation or a pre‐existing interest in AI. Given the small sample sizes within certain subgroups (e.g., hematology, dentistry), we acknowledge the challenge of drawing statistically meaningful conclusions about the program's impact within these fields. Future iterations of the program could benefit from conducting more detailed subgroup analyzes by field of study, gender, or other demographic variables to tailor AI education strategies more effectively. One of the other limitations is the rapid change and advancement in AI, and some of the findings may not be generalizable or citable in future years. However, the fundamental principles and novel educational methods utilized in this study can serve as effective approaches for teaching AI to Generation Z students. Also, while self‐reported familiarity provides insight into students' perceived learning, future studies should incorporate performance‐based assessments (e.g., coding assignments, AI model development, or clinical case simulations) to evaluate the actual application of AI knowledge in real‐world scenarios.

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. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. 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. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. 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

Hossein Rezazadeh; Ali Madadi Mahani; Mahla Salajegheh. Preparing Generation Z of Health Professions for Artificial Intelligence Revolution Through Hacking Education: An Interventional Study. Health Science Reports (2025-08-18). https://doi.org/10.1002/hsr2.71169

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.