ArticleHealth science reports2025
Academic Integrity Within the Medical Curriculum in the Age of Generative Artificial Intelligence.
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 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- From hype to reality: the feasibility, dilemmas, and solutions of Gen AI in medical education from students' perspectives.BMC medical education · 2026Article
- Role reconstruction among double-qualified nursing educators in the generative AI era: a qualitative study.Frontiers in medicine · 2026Article
- Ethical Use of Artificial Intelligence for Clinical Vignettes.HCA healthcare journal of medicine · 2026Article
- Article
- AI usage among medical students in Palestine: a cross-sectional study and demonstration of AI-assisted research workflows.BMC medical education · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The integration of generative artificial intelligence (AI) technologies, such as GPT-3, Wordtune, and Jenni, into academic settings has revolutionized content creation, raising significant questions about authorship and originality. While AI offers benefits in efficiency and productivity, it presents substantial challenges to academic integrity. This paper examines these challenges and the need for new frameworks and policies to ensure ethical AI use. Materials and Methods: Our study aims to develop frameworks promoting ethical AI use and safeguarding academic work authenticity. We employed project-based learning (PBL) methodology to enhance student engagement and academic performance. A total of 179 students participated. PBL involves students in real-world projects, fostering critical thinking and research skills. Turnitin was used to evaluate the similarity percentage of submitted research papers, with a maximum allowable similarity of 20%. Results: Results showed that PBL students had higher academic performance (82.5 vs. 66.5) and lower similarity percentages (4.5% vs. 13%) compared to traditional literature review assessments. The use of Turnitin effectively identified AI-generated content, although it struggled with more sophisticated texts. Conclusion: Our findings highlight the effectiveness of PBL in promoting originality and reducing AI-generated plagiarism. Integrating these methodologies, along with ethical AI education, can help institutions maintain academic integrity while leveraging AI benefits. Future research should refine these strategies to adapt to the evolving educational landscape.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.