Mapping the landscape: A bibliometric analysis of AI and teacher collaboration in educational research

2.1 Artificial intelligence

Artificial intelligence is revolutionizing education by automating monotonous tasks, customizing learning experiences, and providing analytical data on student performance (Van der maaten & Hinton, 2008). Educational institutions are increasingly using AI-powered solutions to save costs, improve learning outcomes, and provide each student with a personalized learning experience. This technology also simplifies administrative procedures, allowing teachers to concentrate on teaching. Furthermore, AI brings novel kinds of interaction, which include interactive simulations and learning platforms as well rendering education more available and successful for all kinds of students. Across learning, artificial intelligence is growing to provide innovative instructional and educational solutions that are now being tested in a variety of scenarios. Computer scientists have made significant contributions to the research and development of artificial intelligence for educational applications (Chen et al., 2020; Zawacki-Richteret al., 2019; Williamson & Eynon, 2020).

2.2 Artificial intelligence and Teachers Collaborations

Teachers play a critical role in an artificial intelligence-based system of learning that extends beyond traditional instruction. While AI provides tailored education and administrative efficiency, instructors provide the crucial human components of empathy, comprehension, and moral direction. They analyze and enhance AI-generated insights with expert judgment, personalizing training to each student’s unique requirements. Teachers also serve as facilitators, teaching students through AI tools and developing critical thinking abilities to identify and examine the information offered by AI. Furthermore, they play an important role in developing social and emotional abilities that AI cannot imitate. Therefore, in the present day, instructors transition from solo information producers to guides and mentors in an improved collaborative and participatory educational experience. The practical uses of machine learning in education varied and crucial (Luo et al., 2022). For example, personalized learning systems like as Dream Box Learning employ AI to tailor lessons in mathematics to each student’s learning style and speed, resulting in more effective and tailored training. In language learning, platforms such as Duolingo use AI to create individualized activities that improve language ability more effectively. Powered by AI chatbots, which include Georgia State University’s “Pounce,” assist students with admission and financial aid issues while reducing administrative operations. AI-powered programs, such as Microsoft’s Seeing AI app, convert visual information into audio feedback for visually challenged pupils, increasing access to instructional content. These apps highlight how artificial intelligence may improve education by making it more customized, efficient, and accessible.

Many modern commercial AI systems designed for schooling. like artificial intelligent tutoring systems (ITS), have a focus on offering automated, adaptive, and individualized teaching, an approach that we shall go over in further depth below. Because of the deep historical connection between AI and behavioral science (Gardner, 1985), numerous important artificial intelligence in education have been developed on intellectual architectures, which assume that the human brain operates as a processor of knowledge. According to this approach, learning is primarily concerned with the development of problem-solving skills, which are dependent on the presence of necessary information structures within the human mind (Carvalho et al., 2022a, 2022b). The efficiency of solving the problem depends on experience and knowledge have been discussed by Alves et al. (2022) and Kusumastuti et al. (2023) in their research as well. Given AI’s potential for improving education, educational scholars, policymakers, and practitioners have turned their attention to AIEd. However, the early focus of study was mostly on engineering factors, including the invention of novel algorithms and the improvement of artificial intelligence and deep learning approaches. In contrast to other areas of educational technology, such as videogames and blended learning, study in AI in Education is disorganized and lacks a unified framework. The influence of AI over education is yet not certain (Holmes & Tuomi, 2022), Further research is required to determine the best scenarios and strategies for using these developing technologies into education. Limited awareness of technology innovations creates barriers to their effective integration in educational institutions (Hussain, 2018). As an outcome, additional analyses will be needed to incorporate the material, offer a thorough picture of the opportunities and problems associated with Artificial Intelligence in Education (AIE), and identify areas for future study. As a result, there has been a significant increase in the overall number of journal papers on this subject. To illustrate, Zhai (2021) had done in research synthesis concentrating on developments in artificial intelligence educational technology; hence addressed to mostly engineering elements (King et al., 2022; Bozkurt et al., 2021). Examined prevailing course in AI educational articles, such as subject areas, regional dissemination and textual data sample. Other reviews focused on specialized subjects, such as language learning, mathematical concepts and medical studies (Karaca et al., 2021), regarding some educational tasks, such as evaluation (Jiménez-Hernández et al., 2020) also in specific technology or programs, like assisting computerized robot, machine learning (Muñoz et al., 2022; Wang et al., 2023; Papadopoulos et al., 2022). However, they are review research studies concentrated over a single major area (learning, teaching, evaluation, or management) or a specific educational result. We would need more comprehensive approach which will analyze the function of artificial intelligence in education (Nigam et al., 2021; Chiu et al., 2023).

The COVID-19 epidemic has forced a rapid transition from traditional education to online learning, with artificial intelligence (AI) emerging as a key role (Wang, 2022; Mhlanga, 2022; Stadlman et al., 2022). Online platforms and AI technologies have the ability to provide a more individualized educational experience, which increases efficacy. Smart assistants powered by AI automate administrative duties for instructors, such as attendance tracking and lesson planning. This efficiency gives instructors additional time and energy, allowing teachers to concentrate on individual student attention. AI integration in education reacts to the changing demands of teaching within a digital age, increasing both instructor effectiveness and learning for students experiences (Nemorin et al., 2023). ChatGPT, designed by OPENAI2, has provoked an educational watershed moment, raising ethical, technological, and practical challenges that impact the learning and teaching interaction (Adiguzel et al., 2023).

A. Data synthesis

Starting in 2003, this study looks at the literature on teacher cooperation and artificial intelligence (AI) in education during the previous 20 years. The following important questions are the focus of the study: Which organisations are the main contributors to research on artificial intelligence and teacher cooperation in education, including research institutes, universities, nations, regions, and research communities? Which conceptual, intellectual, and social frameworks are influencing this study? What changes have you seen over the years in the research of AI and teacher collaboration in education? A detailed summary of research trends and advancements in AI and teacher collaboration in education is shown in Figure 1, which presents the results of bibliometric analysis.

B. The patterns of article publication over time

The distribution of papers on AI and teacher collaboration in education for the last 20 years (2003–2024) is shown in Figure 2. According to the data, research activity peaked in 2021 with the publication of 15 documents, and then significantly increased to 37 publications in 2023. It is important to remember that the 2024 numbers only cover the first five months of the year; by the end of the year, the total is anticipated to have increased. Prior to 2022, the field’s publication output fluctuated, showing inconsistent research activity, despite growing interest in AI and teacher collaboration in education. Publications about AI in education are growing at a pace of 6.82 per year. The average annual citations are displayed in Figure 2 and exhibit an increasing tendency over time, but with considerable irregularity

Figure 2. Annual scientific production.

Source: BiblioShiny.

C. Source growth

The number of publications linked to different universities annually in Figure 3 shows journal contributions to research on AI and teacher cooperation in education. In the line chart, each university is represented by a different colour, and the study focusses on the top five because of their noteworthy contributions.

Figure 3. Affiliations production over time.

Source: BiblioShiny.

With publications in this area starting in 2022 and rising yearly after 2023, the Chinese University of Hong Kong and North South University have shown steady and significant development in their outputs. Since 2003, however, the other universities have contributed very little to this field of study.

D. Word Cloud, and Treemap

The number of articles that journals associate with different universities annually illustrates their contributions to research on AI and teacher cooperation in education ( Figure 4). Each university is represented by a different colour code in the line chart, and the top five universities are highlighted in the analysis because of their noteworthy contributions. Beginning in 2022 and growing yearly after 2023, the Chinese University of Hong Kong and North South University have demonstrated consistent and noteworthy growth in this area. Comparatively, since 2003, other universities have contributed very little to this field of study.

Figure 4. Word Cloud.

Source: BiblioShiny.

E. Word growth

Examining word growth provides important information on how literary terminology has changed throughout time. As shown in Figure 5 AI and teacher cooperation in education, where understanding the development of the field is aided by monitoring the introduction and impact of key phrases. learning system, employee, students, and educational computing are some of the first terms in this domain. For professionals, analysts, and researchers, this analysis is an essential tool that helps them spot patterns and gain insightful knowledge. Analysing word frequency over time reveals trends that can help influence strategic planning and decision-making in a variety of situations.

Figure 5. Treemap.

Source: BiblioShiny.

F. Country-specific production and collaborations

The cooperation globe map displays a graphical depiction of author affiliations by nation. The two major nations with the closest international links are the USA and Hong Kong. The number of publications these countries produce is directly correlated with the intensity of these ties. The global collaboration map for AI and teacher collaboration research in education is shown in Figure 6.

Figure 6. Most productive country.

Source: BiblioShiny.

The USA, a leading contributor to this subject, works closely with countries including Australia, Hong Kong, and the United Kingdom. Researchers from the USA show a great propensity to cultivate worldwide relationships, hence increasing cooperative efforts in AI and education research, despite linguistic variations among participating countries.

The USA, a leading contributor to this subject, works closely with countries including Australia, Hong Kong, and the United Kingdom. Researchers from the USA show a great propensity to cultivate worldwide relationships, hence increasing cooperative efforts in AI and education research, despite linguistic variations among participating countries.

G. Trend topics

A quantitative analysis of the influence of scholarly literature is shown in Figure 7, possibly using bibliometric techniques. This assessment takes into account measures like publications and citations, offering a quantifiable indication of the impact and prominence of research results. Important terms like “Artificial Intelligence,” “Teacher,” “Teachers,” “Students,” and “human,” which probably reflect major issues in the literature, are included in the study. According to the results, “Artificial Intelligence” became the most popular topic, especially in 2023, indicating a major emphasis on AI and its cooperation with educators.

Figure 7. Co-occurrence network.

Source: Vosviwer.

This demonstrates how research trends are dynamic and underscores the necessity of continuous observation. It is advised that researchers remain alert to new developments and modify their focus to meet changing goals and problems.

Similarly, Table 1 assesses the influence of scholarly work using a quantitative method that may include bibliometric tools. Similar to Figure 7, it looks at publication data and citations to gauge the scope and importance of research. Additionally, it uses words like “Artificial Intelligence,” “Teacher,” “Teachers,” “Students,” and “human,” which are in line with important literary themes. The findings demonstrate the increasing convergence of artificial intelligence and education, confirming that “Artificial Intelligence” was the most popular topic in 2023. This emphasises how crucial it is to remain aware of changing research environments and match studies with new fields of interest.

H. The co-occurrence network analysis

Different theme streams in the literature on AI-teacher collaboration in education are revealed by the co-occurrence network analysis. Colour-coded clusters are used to graphically represent these streams. Because of its high centrality, the red cluster is considered the central cluster, indicating that certain keywords within it are more important to the network as a whole. Other subjects in the literature are probably influenced and connected by these important terms, which serve as connectors.

Thematic relationships are represented by the interconnecting purple and green clusters in Figure 7, which show that keywords commonly occur together in these clusters. This suggests that the concepts these keywords reflect have a common theme or conceptual overlap. On the other hand, because they are not connected by the main linking streams, the yellow-orange and grey clusters appear to reflect more discrete subjects. These clusters’ keywords have a weaker connection to the main ideas.

Scholars and researchers can identify important subjects and areas of interest by using information from the co-occurrence network. The choice of study themes, the development of research questions, and an emphasis on subjects that are crucial or closely related in the literature are all supported by this analysis. Furthermore, as similar topics inside the network give academics from many domains a starting point for collaboration, the relationships between clusters may also point to prospects for interdisciplinary cooperation.

Ethics and consent

Ethical approval and consent were not required.