Browsing by Author "Türkmen G."

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    Exploring the artificial intelligence anxiety and machine learning attitudes of teacher candidates
    (Springer, 2024) Hopcan S.; Türkmen G.; Polat E.
    With the advancement of artificial intelligence (AI) and machine learning (ML) techniques, attitudes towards these two fields have begun to gain importance in different professions. One of the affected professions is undoubtedly the teaching profession. Increasing the levels of concern for artificial intelligence and attitudes towards machine learning has become important in order to adapt to potential technologies that will be used. The purpose of this study is to examine the anxiety related to AI and the attitudes towards ML among teacher candidates of different ages, genders, and fields. This study investigates the relationships between sub-dimensions of anxiety towards artificial intelligence and attitudes towards machine learning, as well as to identify differences in these sub-dimensions among gender, age, and department. The findings suggest that although teacher candidates from different disciplines, ages, and genders do not have any concerns regarding learning about artificial intelligence, they do express anxiety about the impact of artificial intelligence on employment rates and social life. The results of this study can be beneficial for developing instructional programs that focus on AI in the long run, considering factors such as age, personal experience, gender, and field-specific elements. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. corrected publication 2023.
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    The Review of Studies on Explainable Artificial Intelligence in Educational Research
    (SAGE Publications Inc., 2024) Türkmen G.
    Explainable Artificial Intelligence (XAI) refers to systems that make AI models more transparent, helping users understand how outputs are generated. XAI algorithms are considered valuable in educational research, supporting outcomes like student success, trust, and motivation. Their potential to enhance transparency and reliability in online education systems is particularly emphasized. This study systematically analyzed educational research using XAI systems from 2019 to 2024, following the PICOS framework, and reviewed 35 studies. Methods like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), used in these studies, explain model decisions, enabling users to better understand AI models. This transparency is believed to increase trust in AI-based tools, facilitating their adoption by teachers and students. © The Author(s) 2024.
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    A basic toolkit for instructional design by AI in elementary level teaching
    (IGI Global, 2025) Türkmen G.; Özdoğan S.S.
    Reporting pathways between instructional design and specific AI models for elementary education is considered highly valuable for the educational field, based on recent developments. Therefore, the aim of this study is to provide an instructional design practice guide for elementary school teachers. Drawing on the capabilities of current educational AI models and the most frequently requested areas of support in instructional design, sample practices were provided as part of in-service teacher training. Four AI tools were selected based on their availability and usability for anyone with internet access. These tools were integrated into the teachers' instructional practices. Ultimately, teachers can utilize educational AI models in every aspect of their instructional design at the elementary level. © 2025, IGI Global Scientific Publishing. All rights reserved. All rights reserved.