Evidence map›Paper›PMID 40831685›Full record

ArticleHealth science reports2025

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

Hossein Rezazadeh, Ali Madadi Mahani, Mahla Salajegheh

Abstract read
In one paragraph

Article in Health science reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Hossein RezazadehEndocrinology and Metabolism Research Center Kerman University of Medical Sciences Kerman Iran.ORCID https://orcid.org/0009-0006-2225-0133
Ali Madadi MahaniStudent Committee of Medical Education Development, Education Development Center Kerman University of Medical Sciences Kerman Iran.
Mahla SalajeghehDepartment of Medical Education, Medical Education Development Center Kerman University of Medical Sciences Kerman Iran.ORCID https://orcid.org/0000-0003-0651-3467

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 ( 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.

Indexed as

artificial Intelligencegeneration Zhack in educationmedical education

Identifiers

PMID40831685
PMCPMC12358928

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.