Validity challenge in GenAI models: Evaluating the validity of content generated by text-to-image models in the context of social studies education

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dc.contributor.authorYeti?şensoy, Okan
dc.date.accessioned2026-02-28T12:09:11Z
dc.date.available2026-02-28T12:09:11Z
dc.date.issued2025
dc.departmentBayburt Üniversitesi
dc.description.abstractGenerative artificial intelligence (GenAI) models have led to many positive changes in educational settings; however, the validity of the content they produce remains a significant topic of academic discussion. This research aims to determine the validity of content produced by text-to-image models within the context of social studies education. For this purpose, different curricular outcomes from the social studies curriculum implemented in Türkiye were identified, and image content related to them was generated using DALL-E 3. The validity of this content was assessed within a panel consisting of social studies teachers. Quantitative analyses revealed inconsistencies, showing that some images are sufficient in terms of scientific accuracy as well as socio-cultural and geographical relevance, while others are not. Intraclass correlation coefficient analyses demonstrated that there was significantly moderate agreement among the panelists regarding these evaluations, indicating that the assessments are reliable. Qualitative analysis, on the other hand, revealed that panelists evaluated some images positively in terms of validity, noting that the content produced by these models has significant potential to enhance the learning of relevant outcomes of social studies. However, it was indicated that certain images exhibit prominent algorithmic biases, provide misleading or incorrect information, include details that could be characterized as hallucinations, and could potentially negatively impact the learning process. Additionally, it was determined that some images, in an attempt to highlight a particular cultural element, displays representations that are either disconnected from reality or overemphasized, which could be termed “Socio-cultural algorithmic exaggerations”. At this point, it is considered essential for all educators, including social studies teachers, to develop a critical perspective on the content created by GenAI models and to use this content only after thorough evaluation. © 2025, Duzce University, Faculty of Education. All rights reserved.
dc.identifier.doi10.33902/JPR.202535435
dc.identifier.endpage101
dc.identifier.issue4
dc.identifier.scopus2-s2.0-105015361372
dc.identifier.scopusqualityQ3
dc.identifier.startpage81
dc.identifier.urihttps://doi.org/10.33902/JPR.202535435
dc.identifier.urihttps://hdl.handle.net/20.500.12403/5852
dc.identifier.volume9
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherDuzce University, Faculty of Education
dc.relation.ispartofJournal of Pedagogical Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_Scopus_20260218
dc.subjectGenAI
dc.subjectSocial studies
dc.subjectText-to-image models
dc.subjectValidity
dc.titleValidity challenge in GenAI models: Evaluating the validity of content generated by text-to-image models in the context of social studies education
dc.typeArticle

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