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- MakaleAçık Erişim2025Scopus SJRQ2Validity challenge in GenAI models: Evaluating the validity of content generated by text-to-image models in the context of social studies education
Yetişensoy, Okan
Generative 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.
AtıfScopus3OpenAlex3
İncele - MakaleAçık Erişim2024Scopus SJRQ2Behind the Scenes: A Critical Perspective on GenAI and Open Educational Practices
Aksoy, Dilara Arzugül; Kursun, Engin
Artificial Intelligence (AI) is a rapidly evolving field that is influencing every aspect of life. Generative AI (GenAI) as a sub-branch of AI is used to create content in various formats such as text, images, video, and audio. This paper discusses the implications of GenAI for Open Educational Practices (OEP), highlighting the potential affordances and challenges. GenAI can address the challenges within the OEP by leveraging openness and ethical use. GenAI’s “generative” nature and human-like language capability can provide resources such as course material, activities, examples, questions, assessment, and learning outcomes in the context of OEP. With machine learning and deep learning infrastructure, it can support the discoverability and accessibility of open resources by increasing the metadata quality. GenAI can automatically score student assignments, answer their questions, and provide instant feedback to address the lack of interaction and feedback that arises due to the large number of students, especially in massive open online courses (MOOC). On the other hand, GenAI brings challenges such as data privacy and security, copyright, biased outputs, and the generation of false information. The conclusions emphasize the importance of a nuanced approach that considers not only the advantages but also the risks associated with adopting GenAI in the OEP world. Researching and developing how to apply these technologies to education is important to shape the future of education.
AtıfScopus27WoS18OpenAlex23
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