Concerns and criticisms regarding the integration of generative artificial intelligence into education must be explained from various perspectives. Some educators have expressed concerns that the ability to generate reports so quickly using artificial intelligence could encourage plagiarism or hinder the development of critical thinking skills in students (Hua Hsiao & Yu Tang, 2025). GenAI tools, especially ChatGPT, can generate human-like responses that are difficult to distinguish from student-written work. This creates a gray area where students may submit content generated by GenAI without realizing or accepting that they are violating academic norms (Yi Tan et al., 2025). In this context, one of the fundamental concerns regarding the use of generative AI in education is its impact on academic integrity. As generative AI tools gain popularity, concerns arise about students’ autonomy in completing assignments (such as homework, essays, and exams) and the potential for AI to circumvent traditional methods of knowledge assessment. Another fundamental concern is the impact of AI on critical thinking and creativity. This is because ready-made answers generated by language models can lead to superficial knowledge acquisition and limit student’ ability to independently analyze and interpret information (Jukiewicz, 2025). In addition, ethical and privacy concerns regarding generative artificial intelligence continue to be of critical importance (Ayyoub et al., 2025). One concern related to generative artificial intelligence literacy concerns educators. Today, many teachers do not have sufficient training in artificial intelligence literacy. This problem also highlights the importance of professional development for teachers (Khlaif et al., 2025). Similarly, it is necessary to note that teachers need classroom-level ethical guidelines that align GenAI use with pedagogical goals and institutional requirements (Vetter et al., 2024). Another concern regarding generative AI is GenAI hallucinations. GenAI hallucinations are defined as GenAI occasionally producing content that contains biases or inaccuracies. GenAI hallucinations may stem from the data used to train the GenAI model. Similarly, these hallucinations may also stem from flaws in probabilistic techniques that enable the model to generate the most probable answer, which may not always be correct (Ringman, 2023).

As a groundbreaking tool in education, GenAI signals significant transformations in how citizens, educators, and policymakers approach educational practices and ideas (Costa & Murphy, 2025). GenAI technologies are being evaluated to expand learning and creativity and to alleviate educators’ administrative workloads through automated solutions (Sok & Heng, 2023). In this context, the integration of generative artificial intelligence technology into education is poised to transform traditional teaching and learning models. The era of teacher-led pedagogy, where teachers were solely providers of information and students were merely recipients, is long gone. Teachers are faced with the choice of either embracing new artificial technologies or risking obsolescence by falling behind educators with next-generation artificial intelligence skills. To maximize the benefits, teachers and new-generation artificial intelligence tools must coexist in a collaborative relationship, underlining a new pedagogy model for the new era (Memon & Kwan, 2025). In particular, the feedback mechanism of ChatGPT, a generative artificial intelligence tool, is advancing to a level that rivals the traditional role of teacher feedback. With ChatGPT’s feedback mechanism, students can structure their knowledge and correct their mistakes. Perhaps one of the most important benefits of generative artificial intelligence is its ability to provide customized feedback and support by adapting to individual learning needs. This allows learning to be personalized (Khlaif et al. 2024). To put it more clearly, GenAI significantly supports personalized learning and assessment by transforming traditional, standardized classrooms into dynamic, student-centered environments. Similarly, GenAI enables continuous virtual teaching across multiple devices, languages, and formats by processing large-scale data and integrating various methods (Z. Zhu et al., 2024). It is noted that ChatGPT can significantly improve the English writing skills of language learners. Furthermore, thanks to ChatGPT’s influence, students feel more comfortable, happy, and at ease with their writing during this process (Boudouaia et al., 2024). Furthermore, it emphasizes that ChatGPT, as a generative AI tool, supports student engagement by answering and explaining questions and generating learning materials for students (Bhatt & Muduli, 2024). In this context, providing students with opportunities to learn ethical rules, access licensed AI tools, and learn how to use these technologies thoughtfully and without unnecessary use can alleviate many of the concerns raised without compromising the educational benefits that AI can provide (Fine Licht, 2025). Ultimately, educators need to develop the appropriate understanding and skills to teach effectively in the AI-focused era (Ayyoub et al., 2025).

Generative artificial intelligence can now be used in almost every level of education. Today’s schools have come to fully realize the importance of integrating GenAI into education. Therefore, they have eagerly begun offering artificial intelligence courses (Y. Zhu & Yang, 2023). Schools can revolutionize traditional teaching and learning scenarios by using generative AI tools such as ChatGPT to expand teacher-student interaction methods, diversify approaches to knowledge acquisition, and implement personalized learning through deep learning and machine learning technologies. Furthermore, machine learning-based technologies, such as automated composition scoring, enhance the accuracy and validity of school education by enabling dynamic and accurate assessment and diagnosis of education and teaching. In addition, ChatGPT promotes interactive education, improving management methodologies and increasing the efficiency of school administration (Liu et al., 2023). For example, within this scope, the literature reveals a consensus on the importance of incorporating GenAI into higher education (Tillmanns et al., 2025). Some researchers have suggested that the use of GenAI in higher education can increase the learning and productivity of university students, teachers, and researchers (Hua Hsiao & Yu Tang, 2025). Although still in its early stages, GenAI’s potential to increase knowledge production and dissemination suggests that it will play an important role in the development of higher education. GenAI has been widely adopted by faculty and students as a vital learning and research tool. Higher education must embrace this technological change and adapt to it to remain relevant and keep pace with changing global demands (Li et al., 2025).

The use of generative artificial intelligence is also becoming widespread in elementary, middle, and high schools. According to Riser (2025), while artificial intelligence systems were once primarily associated with tertiary education, they are now becoming increasingly common in elementary and middle schools. For example, AI-based reading platforms in elementary schools provide instant feedback on pronunciation and fluency, while essay analyzers in middle schools help students improve their writing structure and consistency. Tate and Warschauer (2025) and Graham et al. (2025) highlight the importance of using GenAI in middle schools. Tate and Warschauer highlight important points in this context. They offer recommendations regarding the use of GenAI in middle schools. In this context, some of these points include generating possible writing assignments, producing model texts that students can imitate, preparing a writing lesson to be reviewed and presented by the teacher, improving lessons, providing individualized writing instruction to students, grading written assessments or assignments, and making recommendations on which writing skills and processes should be taught. Du and Lv (2024) point out the need to gain a deeper understanding of elementary school students’ attitudes toward GenAI, their perceptions of its usefulness, and the obstacles they encounter when using it for learning support. Du and Lv also provide practical guidance for effectively integrating GenAI by supporting its adoption and use in elementary schools. Kopecký and Voráč (2025) also emphasize the importance of using generative artificial intelligence in elementary and middle schools. They particularly note that in elementary schools, generative AI can be used to create personalized learning materials and interactive tasks that meet students’ age and individual needs. It should also be noted that generative AI can be integrated into preschool education. It is important that Qiu and Kim (2025) and Nacimiento-García et al. (2024) develop models that will be integrated into the teaching process for preschool-level children with the support of generative AI tools. In conclusion, generative AI tools can be used at many levels of education to achieve various objectives. It should also be noted that studies on the integration of generative AI into educational environments and classrooms are increasing.

With the widespread adoption of generative artificial intelligence, its use in education is also becoming more prevalent. The current study focuses on the bibliometric analysis of studies examining the use of generative artificial intelligence at different levels of education. The diversity of research examining the use of generative artificial intelligence in education is noteworthy. On the other hand, studies examining the bibliometric analysis of research investigating the integration of generative artificial intelligence into classrooms and education remain limited. However, bibliometric analyses offer researchers the opportunity to identify trends in specific areas, dominant keywords in related studies, and gaps in certain fields. In other words, bibliometric analyses have the potential to provide researchers with new areas of focus and opportunities for further work. In line with the nature of bibliometric analysis, the present study examines research on the use of generative AI in education, thereby attempting to open new doors for both the literature and researchers. The dimensions that distinguish the current research from studies in the literature and make it unique should be interpreted well from this perspective. It is noteworthy that, unlike the current research, studies on bibliometric analysis in the literature focus on research related to artificial intelligence rather than generative artificial intelligence. We know that generative artificial intelligence entered our lives in 2022 with the introduction of ChatGPT by OpenAI. However, before that date, research was still being conducted on artificial intelligence, not generative artificial intelligence. Although bibliometric analyses in the literature include the dimension of artificial intelligence, research related to generative artificial intelligence, which entered our lives in 2022, is lacking and limited. For example, although Song and Wang’s (2020) study analyzed the development of educational AI research using bibliometric analysis, it is noteworthy that the studies included in the analysis were conducted between 2000 and 2019. Similarly, in Guo et al.’s (2024) study, generative AI research remained in the background. Relevant studies have examined research conducted before the release of generative AI at a more intensive level. The studies by Song and Wang and Guo et al. are far removed from the current study in that they do not include generative AI research and examine studies conducted before 2023. The study by Kartal and Yeşilyurt (2024), which conducted a bibliometric analysis of studies examining the effects of artificial intelligence in applied linguistics between 1995 and 2022, is also quite distant from the focus of the current study. Huiling et al. (2024) also examined studies conducted between 1990 and 2023. The studies by Kartal and Yeilyurt and Ma and Ismail (2025) are relatively more limited and insufficient compared to the current study, as they only examine studies focusing on second language teaching. On the other hand, the studies by Zhang and Umeanowai (2025), Dissanayake et al. (2025), and Avello and Aranguren Zurita (2025) have also conducted bibliometric analyses of studies focusing on specific concepts of education. Furthermore, the productive artificial intelligence dimension is not at the forefront of the relevant studies. Therefore, the relevant studies are also far from the focus of the current studies.

Bibliometric studies examining the use of artificial intelligence, and particularly generative artificial intelligence, in education are very limited. In this context, the study by Tao et al. (2025) is noteworthy. The study by Tao et al. examined articles and papers in almost all indices. However, the current study focuses only on scientific articles indexed in WoS that examine the use of generative artificial intelligence in education. In addition, the Tao et al.’s study differs from the current study in that it analyzes conferences and covers studies from 2010 to 2024. In other words, AI research dominates the relevant study, but generative AI research is limited. On the other hand, Bozkurt’s (2023) study is limited to the bibliometric analysis of studies in the Scopus database. Furthermore, the relevant study differs from the current study in that it is designed as both a systematic review and a bibliometric analysis. When looking at bibliometric analyses in the literature, the current research has a unique dimension. The current research is important in that it focuses on research conducted after November 2022, when not only artificial intelligence but also generative artificial intelligence (e.g., ChatGPT, DeepSeek, GEMinAI) entered our world. Therefore, the current study has included research conducted between 2023 and 2025 that examines generative artificial intelligence and the use of artificial intelligence in education. Consequently, all studies subjected to bibliometric analysis in the current study belong to the years following the introduction of generative artificial intelligence into our world. In addition, the current study is not limited to research related to a specific dimension of education, as is the case in various bibliometric analysis studies in the literature. On the contrary, by examining articles in the field of education and at all levels of education, the current study is a candidate for achieving a strong research feature. Identifying research gaps, research trends, and key concepts at all levels of education may make the current research significant in this regard. Furthermore, thanks to these aspects, the current research may offer researchers the opportunity to discover new focal points in the subjects they are interested in and the levels of education they wish to work on.

The primary objective of the current research is to map the trends and conduct a bibliometric analysis of articles examining the use of generative AI in education. For this purpose, articles published in Web of Science (WoS)-indexed journals between 2023 and 2025 were included in the analysis. Furthermore, the current study aims to shed light on specific areas related to the use of generative AI in education. In line with these objectives, the current research also aims to provide insights that can contribute to the bibliometric discourse and evolving discussions on the use of generative AI in education by answering the following research questions.

The current study uses a bibliometric analysis to determine the scope and quality of scientific articles examining the use of generative AI in education. This bibliometric analysis covers research across all educational levels. The PRISMA diagram (Page et al., 2021) was used for the analysis. The search for studies to be subjected to bibliometric analysis was conducted based on PRISMA’s conceptual framework.

The scientific articles included in the bibliometric analysis were retrieved from the Web of Science (WoS) platform as the primary data source. WoS was selected as the reference because it includes high-impact research and peer-reviewed journals. To avoid potential bias caused by daily database updates in the current study, all articles published between 2023 and 2025 were downloaded from the WoS database on September 20, 2025. Because generative AI entered our world in November 2022 and because studies in this field are expected to become widespread by 2023, the beginning of 2023 was taken as the reference. A targeted search query was used to find publications related to generative AI applications on the Web of Science (WoS) platform during the search process. Various search combinations were used during this search. The search terms are as follows: ‘generative artificial intelligence, education’ or ‘artificial intelligence, education’ or ‘GenAI, education’ or ‘AI, education’ or ‘ChatGPT, education’ or ‘ChatGPT, school’ or ‘generative artificial intelligence, school’ or ‘artificial intelligence, school’ or ‘GenAI, school’ or ‘AI, school’

In the current study, various inclusion and exclusion criteria were applied to select articles for bibliometric analysis. First, studies outside the field of education included in the WoS were excluded. The exclusion process then continued with studies published before 2023. Furthermore, reports, book chapters, and proceedings were also excluded. Only scientific articles were included as document types in the analysis. Furthermore, articles written in languages other than English were also excluded. This ensured that the data were relevant, specific, consistent, and reliable.

The scientific articles included in the bibliometric analysis were determined according to the inclusion and exclusion criteria and coded as C1, C2, C3, C4, and C5, as seen in Table 1.

An initial search of Web of Science (WoS) using predetermined key terms and combinations yielded 5,554 studies. Exclusion criteria were then applied sequentially. Excluding studies other than those in the field of education, this left 2,039 studies. The final number was then increased to 1,388 by applying the criterion that relevant studies must be articles. When the inclusion and exclusion process was applied based on the year of publication, the number of studies decreased to 1,257.

The current research provides an overview of scholarly articles examining the use of generative AI in education. It identifies trends in articles examining the use of generative AI in education, the distribution of publications by country, collaborations, frequently used research words, and conceptual gaps. It also presents potential collaboration opportunities through visuals.

All ethical guidelines were adhered to at every stage of the study. Because the current research is a bibliometric analysis, no separate ethical approval document was obtained.

As shown in Figure 1, English was selected as the publication language for the studies, and the number of studies subject to bibliometric analysis was determined to be 1,184. The data were then exported as plain text files (.txt) in two separate processes, first as studies 0-1000 and then as studies 1001-1184. Finally, the data were imported into VOSviewer software for statistical analysis.

The VOSviewer software tool was used to ensure the accuracy and efficiency of the bibliometric analyses in the current study. The VOSviewer program generated visual maps by analyzing the distribution of publications by year, the most published authors and countries, the most frequently used keywords, and citation analysis. This created conceptual maps and collaboration networks. The selection of studies to be included in the bibliometric analysis was also reviewed by an independent reviewer. A consensus was reached following the review of the independent reviewer.

This section contains the findings, presented under separate headings, aligned with the six research questions.

According to the results of an examination of articles published in journals indexed in the Web of Science between 2023 and 2025 examining the use of generative AI in education, 2025 is considered a golden age. This finding can be attributed to the rapid adoption of generative AI in education and the growing interest in its applications in the educational context. China and the United States are among the top countries in both publication production and citation impact. Regions such as Latin America and Africa are significantly underrepresented in research on the use of generative AI in education. The most frequently used terms in the research were “artificial intelligence, ChatGPT, higher education, generative AI, education, generative AI, artificial intelligence, AI, AI literacy.” It was concluded that the research agenda is largely shaped around these concepts. The journal Education and Information Technologies stands out as the scientific journal with the highest publication production. Jiahong Su from the University of Hong Kong was found to be the most influential author.

While the current research provides some valuable insights, the authors acknowledge that it has some limitations. The articles subjected to bibliometric analysis in the current study were limited to studies conducted between 2023 and 2025. Furthermore, during the literature review of the articles subjected to bibliometric analysis, the data source included only articles from Web of Science. Similarly, the fact that the analyzed articles were published only in English is a limitation. Furthermore, because fields such as healthcare, engineering, and business require different experience and knowledge, the author(s) in this study limited research examining the use of generative AI to the field of education. Furthermore, the studies subjected to bibliometric analysis included only articles as a document type. Therefore, the exclusion of different document types can be interpreted as a limitation. Furthermore, as the number of studies in the field of GenAI is rapidly increasing, our findings may not be resilient to outdatedness.

For future research, we recommend that researchers include databases other than Web of Science in their searches. Additionally, we recommend that researchers broaden the search scope of the studies they will analyze. Furthermore, researchers can broaden their scope by including studies examining the use of generative AI in fields other than education. Researchers can conduct analyses that include a variety of document types. Researchers can conduct systematic reviews of studies examining the use of generative AI in education, focusing on different identified research categories.