Mustafa Uğraş

Artificial intelligence (AI) is increasingly embedded in everyday teaching, yet its environmental costs remain largely absent from how educators reason about its use. This study examines how 22 teachers working in public schools in Türkiye, each with at least one academic year of experience integrating artificial intelligence into the classroom, interpret and navigate these technologies at the intersection of pedagogical effectiveness, professional responsibility, and ecological sustainability. Semi-structured interviews were analyzed through Braun and Clarke’s reflexive thematic analysis within a generic interpretive qualitative design. Five themes were developed. The first three show teachers actively mediating AI through pedagogical judgment, a restructured professional agency, and a renewed relational and epistemic role. The study’s central contribution lies in the final two: for almost all participants, AI’s ecological cost was not unknown but rendered invisible in everyday practice—a professionally produced ordering of attention enacted through the very pedagogical agency that makes teachers effective. When this cost surfaced, teachers managed rather than resolved the tension through pedagogical rationalization and self-reported intentions toward ecologically motivated restraint, while extending their moral concern to students’ cognitive dependence. Ecological invisibility thus emerges not as a knowledge deficit but as a by-product of professional attention—one that teacher education and policy should address through a critical AI literacy integrating ethical, ecological, and pedagogical dimensions.

This study maps the development, intellectual roots, and conceptual transformation of early childhood science education between 1990 and July of 2026 through performance analysis and science mapping of 1147 English-language research articles indexed in the Scopus Social Sciences subject area. Analyses conducted in R with bibliometrix/Biblioshiny examined performance indicators, collaboration, direct citation paths, co-citation patterns, and author-keyword structure. Publication output increased sharply after 2019: 635 articles (55.4%) appeared between 2019 and July 2026, including 537 (46.8%) in the completed years 2019–2025 and 98 in the partial 2026 period. Research in Science Education and the International Journal of Science Education were the most productive sources, while Fleer ranked first in both publication count and local citations. Internationally co-authored articles represented 12.57% of the corpus according to bibliometrix’s Multiple Country Publications ratio. The historiograph retained a connected sequence of influential works. Under the baseline settings, the displayed co-citation network was partitioned into four Louvain groups concerned with cultural-historical theory, empirical science pedagogy, nature of science and standards, and cognitive development. Modularity was low (Q = 0.13), and the number of groups was parameter-sensitive, while author keywords indicate that inquiry, play, and teacher pedagogy remain prominent alongside the more recent visibility of STEM, computational thinking, and computer science education. Together, these patterns suggest continuity in established research traditions alongside an expanding thematic vocabulary. They also motivate a hypothesis about core–periphery organization, although no core–periphery model was fitted. An expanded query (n = 2739) reproduced the post-2019 rise in publication counts and was not used for the primary maps. The findings are limited to the retrieved corpus and do not estimate the prevalence of these themes across the wider STEM, robotics, coding, computational thinking, or artificial intelligence studies.

The aim of this study is to determine teachers’ views on integrating sustainable artificial intelligence use into classroom teaching processes. The study was conducted using a qualitative research approach and adopted a case study design. The study group consisted of 38 teachers who were selected using maximum diversity sampling, who currently use AI, and who participated in a 4-week structured “Sustainable AI Training Program.” To ensure methodological triangulation, data were collected through semi-structured interviews, researcher diaries, and participant diaries and analyzed using inductive thematic content analysis. According to the analysis results, some findings reveal that teachers considered filtering AI tools through a pedagogical filter centered around the question “Is it really necessary?” rather than using them directly and intensively. Furthermore, digital minimalism was adopted in classroom practices, along with the use of a single, optimized prompt instead of trial-and-error queries, the practice of archiving and reusing generated content, and a shift toward low-tech alternatives. It was determined that teachers would adopt digital minimalism in classroom practices, aiming to serve as role models for sustainable use by bringing the hidden environmental costs of technology into the learning process and fostering eco-digital citizenship awareness among students. Consequently, AI integration has evolved from a technical decision into a pedagogical redesign process encompassing ethical and ecological dimensions.

This study aims to analyze scientific publications on the use of productive artificial intelligence tools in STEM education during early childhood between 2021 and 2025 using bibliometric methods. In this context, the international literature published during the relevant period was examined to identify scientific production trends, prominent authors, influential studies, institutional and international collaboration networks, and thematic focuses. A total of 1,224 documents were included in the analysis. During the data analysis process, the Bibliometrix (Biblioshiny) software running on the R program was used; publication trends, keyword frequencies, citation analyses, author and institution collaborations, and scientific mapping techniques were utilized. The analysis findings show that academic interest in productive artificial intelligence tools has increased significantly between 2021 and 2025. The study also systematically identifies key trends, application areas, and research gaps related to the integration of these technologies into educational processes.

Bu çalışmanın amacı, Deng ve arkadaşları (2025) tarafından geliştirilen Yapay Zekâya Yönelik Gelişmeleri Kaçırma Korkusu (Fear of Missing Out on AI; FoMO-AI) ölçeğinin Türk kültürüne uyarlanması ve öğretmen örnekleminde geçerlik ile güvenirlik kanıtlarının elde edilmesidir. Yöntem: Araştırma, betimsel tarama deseninde yürütülmüş bir ölçek uyarlama çalışmasıdır. Süreç; çeviri-geri çeviri, yedi alan uzmanından kapsam geçerliği için görüş alma, 35 öğretmenle pilot uygulama ve iki bağımsız öğretmen örnekleminde (AFA için n = 320; DFA için n = 315) ana uygulama aşamalarını kapsamaktadır. Veriler Doğu Anadolu’daki bir ilde uygun örnekleme yöntemiyle toplanmış; analizler SPSS 20.0 ve AMOS yazılımları kullanılarak gerçekleştirilmiştir. Yapı geçerliği için Açımlayıcı Faktör Analizi (AFA) ve Doğrulayıcı Faktör Analizi (DFA); güvenirlik için Cronbach Alfa, bileşik güvenirlik (CR) ve ortalama açıklanan varyans (AVE) hesaplanmıştır. Bulgular: AFA sonucunda KMO değeri .964, Bartlett küresellik testi anlamlı (χ² = 8012.989, sd = 325, p < .001) bulunmuş; özdeğeri 1’in üzerinde olan üç faktörlü yapı toplam varyansın %76.006’sını açıklamıştır. DFA bulguları üç faktörlü yapının model veriyle yüksek düzeyde uyum göstermektedir (χ²/df = 1.105; CFI = .995; TLI = .994; GFI = .942; RMSEA = .016). Faktörlere ilişkin Cronbach Alfa değerleri .89 ile .92, CR değerleri .91 ile .93 ve AVE değerleri .53 ile .59 arasında değişmiştir. Fornell-Larcker kriteri, ayrışma geçerliğinin sağlandığını göstermiştir. Sonuç: FoMO-AI ölçeğinin 26 maddelik Türkçe formu, eğitim paydaşlarının yapay zekâya yönelik gelişmeleri kaçırma korkusunu ölçmede kullanılabilecek geçerli ve güvenilir bir araçtır.

This study explores the potential of ChatGPT to address challenges in Early Childhood Science Education (ECSE) from the perspective of educators. A qualitative case study was conducted with 33 Early Childhood Education (ECE) teachers in Türkiye, using semi-structured interviews. Data were analyzed through content analysis with MAXQDA 24 software. The results indicate that ECE teachers perceive ChatGPT as a partial solution to the scarcity of educational resources, appreciating its ability to propose alternative material uses and creative activity ideas. Participants also recognized its potential to support differentiated instruction by suggesting activities tailored to children’s developmental needs. Furthermore, ChatGPT was seen as a useful tool for generating lesson plans and activity options, although concerns were expressed that overreliance on the tool might undermine teachers’ pedagogical skills. Additional limitations highlighted include dependence on technology, restricted access to digital tools, diminished interpersonal interactions, risks of misinformation, and ethical concerns. Overall, while educators acknowledged ChatGPT’s usefulness in supporting ECSE, they emphasized that its integration into teaching practice should be cautious and balanced, considering both its educational benefits and its limitations.

This study aims to determine teachers' views on the use of ChatGPT in preschool education. The study was conducted using a case study, which is one of the qualitative research methods. In the 2023–2024 academic year, the study group consisted of 16 preschool teachers working in a province in the Eastern Anatolia Region of Turkey. The researcher developed a semi-structured interview form and used researcher diaries as data collection tools. An inductive content analysis approach described the data from the interviews with the participating teachers and the research diaries. The findings revealed that most of the teachers thought that ChatGPT was suitable for preschool education due to its potential, such as creating personalized and creative activities and suggesting games and stories. However, negative opinions about potential problems such as obtaining misinformation, technology addiction, decreased social interaction, and deriving age-inappropriate content were also identified. Teachers agreed that ChatGPT has potential in terms of language development, individual learning support, development of creativity, fast access to information, and story creation, but they also had a common opinion that it can be harmful, such as increasing screen time, reducing social interaction, and not fully supporting emotional and social aspects. For successful integration at the pre-school level, technical requirements such as tablets and computers may be needed, as well as teacher trainings, a guide on how to use ChatGPT effectively, and information for parents. School administrators, teachers, and parents should receive training about ChatGPT, as suggested.

Attitude for the teaching profession has an important place in the professional resilience of teacher candidates. Examining the moderating role of pre-service teacher (PST) attitudes for technology will contribute to the enrichment of the literature by looking at the relationship between their attitudes for the profession and their professional resilience from a different perspective. The aim of this study is to examine the moderating role of PST' attitudes for technology in the relationship between their attitudes for the profession and their professional resilience. Relational survey model was used in the study. The research group consists of 407 PST in Gaziantep, which can be reached with an easy sample. As data collection tools; 'Personal Information Form', 'Attitude Scale For the Teaching Profession', 'Professional Resilience Scale for Teaching Candidates', 'Attitude Scale For Technology' were used. The scales used were filled in with a paper-pencil form by the PST. The data gathered throughout the investigation were assessed utilizing quantitative analysis techniques with the use of SPSS software. Descriptive statistics, Pearson correlation analysis, and Hayes (Process) analysis 1 were employed in the examination of the data. The model is used. As a result of the research, it was seen that the attitudes of PST for the profession predicted their professional resilience. However, the relationship between these PST' attitudes for the profession and their professional resilience differs according to their attitudes for technology. It has been determined that PST' attitudes for the profession and their attitudes for technology have a moderating role in the relationship between their professional resilience.

This study aims to adapt the Artificial Intelligence Literacy Scale translated by Wang et al. (2023) into Turkish and create a scale suitable for assessing the artificial intelligence literacy of pre-service teachers. The study used the survey method within the scope of the quantitative method. The sample of the study consisted of 440 pre-service teachers from a state university in the Eastern Anatolia Region of Turkey. The original scale consists of 12 items, 4 factors, and a 5-point Likert-type structure. In the first stage, we conducted translation studies to assess the language validity of the adapted scale. Then, the data collected from the part of the sample determined for EFA (Exploratory Factor Analysis) were analyzed. The results show that the adapted scale preserves the original scale structure. The data collected from the part of the sample designated for CFA (confirmatory factor analysis) was also analyzed. The results of the analysis show that the scale has acceptable and good-fit indices. In terms of reliability, Cronbach Alpha reliability coefficients show that the scale has a reliable structure. The results of the analysis indicate that the scale adapted into Turkish has a valid and reliable structure.