Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Article
    A Multidimensional Comparative Analysis of Human Expert vs. AI-Driven Feedback Approaches on Learner-Centered and Collaborative Groups
    (Routledge, 2026) Yıldız Durak, H.; Onan, A.
    The aim of this study is to examine the multidimensional effects of AI-based feedback in learner-centered and collaborative learning environments among university students. The study employed a five-group experimental design: two individual learning groups receiving either AI-based feedback(G1) or human expert feedback(G2), two collaborative learning groups receiving either AI(G3) or human expert feedback(G4), and control group(G5). According to the research results, G4 showed the highest level of development in the areas of creative problem solving, internal-external motivation, and critical thinking. G1 was the group with the highest performance, particularly in terms of system interaction, completed activities, and assignments. In contrast, G2 showed the lowest results in terms of both cognitive development and learning analytics. AI-based feedback in collaborative learning environments provided the highest development in cognitive skills, while AI-based in individual work was more effective in increasing system participation. Factorial MANCOVA indicated significant interactions between learning environment and feedback type on posttest outcomes, with strongest effects on self-efficacy, intrinsic motivation, and flexibility. These results show that AI-based feedback has different effects in both individual and collaborative learning contexts. Qualitative thematic analysis highlighted themes of cognitive facilitation, creativity enhancement, feedback quality perceptions, and environment preferences. © 2026 Informa UK Limited, trading as Taylor & Francis Group.
  • Article
    AI-Powered Detection of Hate Speech Against Refugees in Turkish Social Media
    (Springer Science and Business Media B.V., 2025) Eğin, F.; Bulut, V.; Onan, A.
    Social media platforms can cause hate speech to spread rapidly, so it is important to address such content. The speed at which hate speech spreads on social media makes it impossible to obstruct such content manually. Artificial intelligence support can be a solution for this. Detecting hate speech with artificial intelligence requires determining which expressions are hate speech. In this research, a study was conducted specifically on hate speech against refugees. Considering that Turkiye is the country with the highest migration after the Syrian Civil War, and the hospitality to approximately 3.9 Syrian people, the study focused on the Turkish language. The research first aims to create a Turkiye dataset of social media posts. Posts containing hate speech were labeled using discourse analysis on this dataset. The next stage of the research is to detect Turkish hate speech against refugees with artificial intelligence. According to the research results, the BERTurk model trained with this dataset achieved an accuracy rate of 85% in the automatic detection of Turkish hate speech. In the current climate, hate speech can spread rapidly in society and can easily lead to violent acts. Therefore, taking the necessary measures against hate speech is crucial. This study is crucial for automatically detecting hate speech in Turkish. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.