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    A psychometric analysis of the artificial intelligence skills scale developed through chat gpt
    (Springer, 2025) Gorgulu, Deniz; Coskun, Fatma; Demir, Mustafa; Sipahioglu, Mete
    This study aims to examine the psychometric properties of a scale developed to measure teachers' ability to use artificial intelligence tools in education. The scale was originally proposed as having 33 items across 5 sub-dimensions by Chat GPT-4, an AI system. To establish content validity, the scale form was submitted to expert review. An exploratory factor analysis (EFA) was then conducted on a sample of 347 teachers to determine if the 33-item, 5-subdimensional structure proposed by Chat GPT was confirmed by empirical data. The EFA results supported Chat GPT-4's proposed structure. A confirmatory factor analysis (CFA) was applied to a new sample of 603 teachers to assess the scale's validity in an independent sample. The CFA results did not fully support the structure consisting of 33 items and 5 subdimensions proposed by Chat GPT. Reliability was analyzed using Cronbach's Alpha and test-retest methods.
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    Classification of Student Leadership Profiles in Diverse Governance Settings: Insights from Pisa 2022
    (Mdpi, 2024) Gorgulu, Deniz; Coskun, Fatma; Sipahioglu, Mete; Demir, Mustafa
    Student leadership prepares students for responsibilities, such as taking on specific tasks and assuming leadership roles in their future personal and professional lives. Developing students' leadership profiles is among the important goals of educational systems aiming for future generations to take responsibility and advance their countries. With this perspective in mind, the PISA assessment includes items to measure students' leadership behaviors. This study aims to extract student leadership profiles from the leadership-related items in the PISA 2022 application, using data from Cambodia, Peru, Paraguay, and Guatemala, which have different governance systems and cultural characteristics. The second purpose of the research is to determine the distribution of the identified leadership profiles in these countries and explain them in the context of governance and cultural characteristics. Latent class analysis was used to determine student leadership profiles. Accordingly, two-class and three-class latent models were found to be the most suitable models to explain student profiles. While the distinction between student profiles is more pronounced in the two-class model, the three-class model provides more detailed information about student profiles. In this respect, two-class and three-class latent models are reported comparatively. In the two-class latent model, students are labeled as the Shy or Lack of Self-Confidence Group and the Active Leader or Influential Group. In the three-class latent model, students are labeled as the Moderate or Passive Leader Group, the Strong Leader or Influential Group, and the Avoidant or Leadership-Uncomfortable Group. In both models, it is one of the striking findings that Cambodian students are in the low leadership profile, and Peruvian students are in the high leadership profile.