Assessing Artificial Intelligence-Generated Patient Educational Material on Gestational Diabetes Mellitus Content and Quality Evaluation

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dc.contributor.authorAypar Akbag, Nuran Nur
dc.date.accessioned2026-04-25T14:20:10Z
dc.date.available2026-04-25T14:20:10Z
dc.date.issued2025
dc.departmentSinop Üniversitesi
dc.description.abstractPurpose: This study aims to evaluate the content and quality of patient educational materials on gestational diabetes mellitus (GDM) generated by ChatGPT and Gemini. Background: The sources of knowledge are crucial in the effective management of disease. Artificial intelligence (AI) platforms could become a primary source of patient education materials in the near future. Methods: A descriptive research design was employed. Frequently asked questions related to GDM were extracted from patient education sections of existing guidelines. These questions were then submitted to both ChatGPT and Gemini. The responses provided by these platforms were used to create educational material aimed at pregnant women diagnosed with GDM. The content was reviewed by a panel of 11 experts. The Patient Education Materials Assessment Tool for Printed Materials (PEMAT-P) was employed to evaluate the content's effectiveness and clarity, and the readability was assessed through the Ate & scedil;man Readability Formula and the Gunning Fog Index. Results: A total of 32 questions regarding GDM were directed to the AI platforms. The resulting educational materials had a readability score of 77.8 based on the Ate & scedil;man scale and 16.25 according to the Gunning Fog Index. The experts rated the material as highly comprehensible, with an average PEMAT-P understandability score of 91.36% (range: 86.66%-93.75%) and an actionability score of 89.67% (range: 80%-100%). Conclusion: The GDM educational materials generated by ChatGPT and Gemini exhibit a high level of readability, making them easy to understand. Moreover, the material was deemed comprehensible and actionable for pregnant women with GDM. Implications for practice and research: Although AI-generated patient educational materials show great potential, further experimental research is necessary to assess their long-term effectiveness.
dc.identifier.doi10.1097/JPN.0000000000000905
dc.identifier.endpage217
dc.identifier.issn0893-2190
dc.identifier.issn1550-5073
dc.identifier.issue3
dc.identifier.orcid0000-0002-4693-2896
dc.identifier.pmid40730172
dc.identifier.scopus2-s2.0-105012310426
dc.identifier.scopusqualityQ2
dc.identifier.startpage210
dc.identifier.urihttps://doi.org/10.1097/JPN.0000000000000905
dc.identifier.urihttps://hdl.handle.net/11486/8410
dc.identifier.volume39
dc.identifier.wosWOS:001542089600007
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorAypar Akbag, Nuran Nur
dc.language.isoen
dc.publisherLippincott Williams & Wilkins
dc.relation.ispartofJournal of Perinatal & Neonatal Nursing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260420
dc.subjectartificial intelligence
dc.subjectChatGPT
dc.subjectGemini
dc.subjectgestational diabetes mellitus
dc.subjectpatient educational material
dc.subjectreadability
dc.titleAssessing Artificial Intelligence-Generated Patient Educational Material on Gestational Diabetes Mellitus Content and Quality Evaluation
dc.typeArticle

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