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    AI-Assisted formative assessment in social studies education: development of an NLU-integrated smart diagnostic branched tree platform and testing its effect
    (Routledge Journals, Taylor & Francis Ltd, 2026) Yetisensoy, Okan
    Formative assessment practices have been a significant focus in Social Studies education for many years. One commonly used technique in these practices is the diagnostic branched tree, which is designed to assess students' understanding through a branching structure based on their responses. While this technique shows promise for identifying students' learning status and gaps throughout the ongoing process, it presents several structural limitations. This research aims to address these limitations by integrating diagnostic branched trees with natural language understanding, a key AI technique in educational assessment, thereby enhancing their diagnostic capabilities and making them smarter tools in educational settings. Furthermore, this research seeks to evaluate the impact of this revised system in middle schools within a practical setting. Following the development phase, the results of the experimental study demonstrated that the revised system had a significantly positive impact on students' learning performance. Qualitative data, meanwhile, suggested that this success was largely due to the system's ability to provide formative feedback and personalized guidance, which fostered metacognition and self-regulated learning. At this point, considering the positive transformations brought about by technology, it is recommended that innovative technologies be developed to enhance formative assessment across various levels of K-12 education.
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    AI-enhanced disaster education in middle schools: A mixed study from social studies classes
    (Int Forum Educational Technology & Soc, Natl Taiwan Normal Univ, 2026) Yetisensoy, Okan
    This research aims to examine the educational potential of AI-assisted chatbots, the popular AI technology of recent years, in middle school disaster education. In this regard, six chatbot characters in human form, each with fictional stories, were developed for various disasters taught within the scope of 5th-grade Social Studies in T & uuml;rkiye. While the practitioner teacher instructed on disaster-related curricular outcomes in the experimental group through activities that included the use of the relevant chatbots, the same curricular outcomes in the control group were taught using conventional pedagogical methods. The experimental results showed that the disaster knowledge of the experimental group students was significantly higher than that of the control group students. However, no significant difference was observed in students' disaster awareness levels. In the semi-structured interviews, students expressed that chatbots promoted a more effective, interesting, and student-centered disaster education with their distinctive design and pedagogical features. However, they noted that chatbots had certain inadequacies in developing concrete experiences, as well as psychomotor skills necessary to address disaster risks. Findings from the study indicate that the relevant AI technology is effective in promoting disaster education processes, particularly in facilitating more effective learning. Nevertheless, achieving more complex competencies for mitigating risks requires a higher level of technological advancement. At this point, it is suggested that undertaking initiatives to combine chatbots with extended reality environments will be beneficial in making them a more effective tools in disaster education processes.
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    Beyond Algorithms: Unveiling the Intersection of AI Ethics, Citizenship Education, and Social Studies
    (Springer International Publishing Ag, 2025) Yetisensoy, Okan
    The rapid rise of artificial intelligence (AI) in recent years has led to various positive transformations in everyday life. However, this rise has also brought about the emergence of some ethical issues that raise concerns in society. Examples of these ethical issues include the manipulative or harmful use of AI, privacy concerns, bias, discrimination, and lack of transparency and accountability. AI ethics, on the other hand, represent a set of guiding principles that stakeholders should adopt to ensure the responsible development and utilization of AI technologies. Considering that young people are potential users and developers of AI, teaching them these principles from an early age can be seen as a natural part of citizenship education. Thus, incorporating AI ethics-related content into the curriculum of various K-12 courses can be regarded as an effective approach. This chapter argued that social studies, with its focus on fostering effective citizens for the future, will emerge as a crucial application area for this approach. At this point, the relevant study aims to explore the intersections between AI ethics, citizenship education, and social studies. It is believed that the study will make a significant contribution to the literature by revealing the new dynamics created by AI in citizenship education and demonstrating the potential role that the social studies curriculum can play in this regard.
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    The effect of AI-powered chatbots in social studies education
    (Springer, 2024) Yetisensoy, Okan; Karaduman, Hidir
    The aim of this research is to investigate the educational potential of AI-powered chatbots in Social Studies learning-teaching processes. The study was conducted using embedded design, evaluated within the framework of mixed methods research. The study group consists of 78 6th-grade students studying in three different classes, along with one teacher who implemented the practices and guided the process. During the experimental phase of the study, lessons in the experimental group were taught by the practitioner teacher using an AI-powered chatbot named SosyalciBot. Meanwhile, lessons in two control groups were taught following the constructivist approach. At the end of the process, it was determined that the experimental group students' post-test and permanency test scores were significantly higher than the control group students' scores in both post-test and permanency test. In the semi-structured interviews, both students and the practitioner teacher provided positive evaluations of the pedagogical and design features of the chatbot, indicating that these features positively impacted the learning-teaching process. They also identified some shortcomings while offering suggestions for improvement. Based on the study findings, it is evident that chatbots have a high potential to contribute to Social Studies education. However, to fully harness this potential and achieve optimal effectiveness, further advancements and refinements in chatbot technology are required. At this point, it is recommended to conduct theoretical or applied studies focusing on developing chatbots with high communication capacity and explore innovative and constructive ideas regarding the integration of chatbots into educational environments.
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    Exploring the transformative power of GenAI in education: an exploratory journey from social studies classes
    (Springer, 2025) Yetisensoy, Okan
    The potential of GenAI in educational settings has attracted considerable attention, yet practical explorations of its use in K-12 classrooms are still scarce. This research aims to explore the role of GenAI models in social studies, focusing on their influence on learning-teaching processes and understanding of AI. While exploring the influence of GenAI on pedagogical aspects from the perspective of the teacher and students, this qualitative study also embraces AI as a cross-curricular theme and seeks to understand how GenAI-supported teaching activities shape students' AI understanding. The study group of this research, conducted according to the basic qualitative research design, consisted of 21 middle school students studying in one class and a social studies teacher who implemented the practices. The primary data collection tool for the research was semi-structured interview forms. In the relevant research, a teaching framework supported by GenAI, combining Teaching with AI and Teaching about AI was developed. During the four-week process, the practitioner teacher covered the curricular outcomes using lesson plans designed in alignment with this framework. In the semi-structured interviews, students stated that engaging in activities supported by GenAI not only made the learning process more effective and interesting but also enhanced their understanding regarding AI. The teacher highlighted that GenAI models encouraged constructivist learning environments, ensured the active involvement of all students, including those in inclusive education, and contributed to AI learning through their engaging structure. Additionally, the teacher highlighted technology's drawbacks, including encouraging dependence on convenience and potentially providing inaccurate information. The relevant findings of the study, serving as first observations, are expected to provide valuable insights for future research. At this point, it is recommended that further studies be conducted to explore the transformative potential of GenAI in education, especially by employing experimental designs. An additional suggestion, on the other hand, is to disseminate AI throughout the entire curriculum of all subjects within a specific educational level as a cross-curricular theme, as opposed to limiting its teaching to merely a singular, isolated subject area.
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    Investigation of the Relationship between Social Studies Teacher Candidate's Unemployment Worries and Their Attitudes toward Teaching Profession
    (Hacettepe Univ, 2020) Yetisensoy, Okan; Sahin, Ibrahim Fevzi
    This study aims to determine the levels of unemployment worries of social studies teacher candidates and 15.03.2019 their attitudes towards the teaching profession in terms of various variables and to investigate the relationship between their unemployment worries and their attitudes toward teaching profession. This study was Accepted: conducted in accordance with the relational screening model. As a result of the study, it was found that social 24.07.2019 studies teacher candidates' attitudes towards teaching profession differed significantly in favour of female teacher candidates, low-grade teacher candidates and teacher candidates with higher GPA. Also, it was found Online First: that social studies teacher candidates' unemployment worries were significantly higher in 4th grade teacher 26.07.2019 candidates but did not differ significantly by gender and GPA variables. In addition, the findings of the study showed that there were significant negative relationships between the sub-dimensions of unemployment Published: worries and attitudes towards teaching profession and there was a negative, medium level significant 31.07.2020 relationship between teacher candidates' general unemployment worries and their attitudes towards teaching profession. Moreover, it was determined that unemployment worries of the 4th grade social studies teacher candidates were significantly higher than the second-grade teacher candidates, whereas their attitudes towards teaching profession were significantly lower. In conclusion it is thought that the findings and negative relationships determined in this study provide strong clues suggesting that social studies teacher candidates' unemployment worries negatively affect their attitudes towards teaching profession.
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    The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes toward Artificial Intelligence
    (Taylor & Francis Inc, 2024) Kaya, Feridun; Aydin, Fatih; Schepman, Astrid; Rodway, Paul; Yetisensoy, Okan; Kaya, Meva Demir
    The present study adapted the General Attitudes toward Artificial Intelligence Scale (GAAIS) to Turkish and investigated the impact of personality traits, artificial intelligence anxiety, and demographics on attitudes toward artificial intelligence. The sample consisted of 259 female (74%) and 91 male (26%) individuals aged between 18 and 51 (Mean = 24.23). Measures taken were demographics, the Ten-Item Personality Inventory, the Artificial Intelligence Anxiety Scale, and the General Attitudes toward Artificial Intelligence Scale. The Turkish GAAIS had good validity and reliability. Hierarchical Multiple Linear Regression Analyses showed that positive attitudes toward artificial intelligence were significantly predicted by the level of computer use (beta = 0.139, p = 0.013), level of knowledge about artificial intelligence (beta = 0.119, p = 0.029), and AI learning anxiety (beta = -0.172, p = 0.004). Negative attitudes toward artificial intelligence were significantly predicted by agreeableness (beta = 0.120, p = 0.019), AI configuration anxiety (beta = -0.379, p < 0.001), and AI learning anxiety (beta = -0.211, p < 0.001). Personality traits, AI anxiety, and demographics play important roles in attitudes toward AI. Results are discussed in light of the previous research and theoretical explanations.