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

2.1 AI-Enhanced Teacher Collaboration in Educational Research

AI applications in education have mostly focused on enhancing student learning results, optimizing administrative operations, and creating tailored experiences for students (Chen et al., 2020; Zawacki-Richter et al., 2019). However, little emphasis has been placed on how AI may actively assist collaborative research and knowledge development among educators. Teachers frequently collaborate in educational research by co-authoring publications, designing research studies, exchanging data, and communicating findings. These collaborative behaviors are critical for professional growth, innovation, and knowledge construction (Kusumastuti et al., 2023).

In this regard, AI offers exciting possibilities for enhancing teacher collaboration by providing advanced interaction platforms, automatic analysis of content, and intelligent systems for managing information. Tools powered by AI which include natural language processing (NLP) and algorithms based on machine learning are being increasingly incorporated through collaborative platforms to promote joint studies and simplifies knowledge sharing (Luo et al., 2022).

Theoretical frameworks like Socio-Technical Systems (STS) and Community of Practice (CoP) are especially useful for understanding how AI might improve teacher cooperation. STS emphasizes the interaction of technological instruments (AI systems) and social systems (teacher cooperation), stressing how AI can function as a facilitator of collaboration dynamics rather than just a technical tool. From the CoP perspective, AI acts as a mediating tool, allowing multiple groups of educators to contribute knowledge, construct shared collections of research techniques, and maintain collaborative ties across time.

In addition, Collaborative Learning Theory suggests that AI-powered systems might improve real-time knowledge creation and co-authoring by offering continuous feedback, personal recommendations, and dynamic database analysis (Boninger et al., 2020). However, while AI provides tools for improving cooperation, it does not substitute for educators’ ethical, social, and contextual judgments in research and practice.

2.2 Applications of AI in Teacher Collaboration

AI technologies are being used in a number of ways to improve teacher collaboration particularly in areas like:

Despite these advances, the available literature lacks a cohesive framework that explicitly links AI tools to collaborative research techniques among teachers. The research that do address teacher cooperation are primarily concerned with technical capabilities rather than social and organizational aspects. The dearth of complete based on theories research regarding how AI facilitates teacher cooperation poses a key void in the literature.

Research Gap:

The intersection between AI and teacher collaboration remains underexplored, with most studies prioritizing technical advancements over collaborative dynamics. Furthermore, current literature does not adequately address how AI tools influence the social processes of collaboration, particularly within educational research communities. By applying theories such as Socio-Technical Systems, Communities of Practice, and Collaborative Learning Theory, this study seeks to uncover the conceptual foundations and practical implications of AI-enhanced teacher collaboration.

Despite the expanding use of AI in research on education, the relationship of AI and teacher cooperation is still underexplored. Current research focuses mostly on technical elements, discrete educational effects, or unique AI applications. The joint dynamics of AI devices and human teachers, particularly in terms of knowledge and research development, have gotten little attention.

This work attempts to solve this gap by offering a full bibliometric assessment of AI-enhanced collaboration among teachers, finding publication trends, theme structures, and key contributions. While doing so, it intends to offer important insights into future study as well as practical use in educational contexts.

3.1 Data Collection

In order to investigate how teacher cooperation and artificial intelligence (AI) connect in educational research, this study used bibliometric analysis. Scopus, a thorough and trustworthy academic database renowned for its wide coverage of peer-reviewed literature, provided the data. The research was conducted using words associated with “Artificial Intelligence,” “Teacher Collaboration,” “Educational Research,” along with other relevant terms. The inclusion criteria were confined to articles published in English during the previous two decades to ensure a complete assessment of the growing academic scene.

3.2 Data Analysis

The analysis involved two primary techniques: Performance Analysis and Science Mapping.

3.3 Ensuring Reliability of Coding and Analysis

For bibliometric assessment to produce reliable and repeatable results, coding and analysis dependability must be guaranteed. The following actions have been implemented to improve reliability:

Use of Automated Tools:

Well-known programs like VOSviewer and R bibliometrix, which are known for their precision in producing co-authorship, co-citation, and keyword co-occurrence networks, were used in the bibliometric analysis. By processing citation data using automated algorithms, these technologies reduce human bias.

Data Cleaning and Preprocessing:

The data extraction method required thorough filtering to exclude multiple entries, unnecessary articles, and sources which did not fulfil the inclusion criteria. To prevent bias, two researchers worked independently on the cleaning process.

Inter-Coder Reliability Testing:

To improve the reliability of theme code and classification, inter-coder accuracy was investigated. Two separate researchers carried out the coding technique and then examined their findings using Cohen’s Kappa coefficient. A total score of 0.75 or greater was deemed satisfactory, suggesting significant agreement among coders.

Cross-Validation of Findings:

The results generated from automated tools were cross-validated through manual checks to ensure consistency. Discrepancies were resolved through discussions and consensus between the researchers.

Triangulation:

The use of various tools (VOSviewer and R bibliometrix) provided the triangulation technique, which strengthened the findings by ensuring that the findings were consistent across different analytic approaches.

By implementing these reliability measures, the study aimed to ensure that the findings were both accurate and reproducible, thereby enhancing the credibility of the bibliometric review.

4.1 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.

Figure 1. Main information.

Source: BiblioShiny.

4.2 The Patterns of Article Publication Over Time

Figure 2 depicts the distribution of articles on AI and teacher collaboration in education published during the last 20 years (2003-2024). Research activity peaked in 2021 with 15 publications, but rose dramatically to 37 articles in 2023. Notably, the 2024 data only covers the first five months of the year, so the overall number of publications is projected to increase by the conclusion of the year. Even before 2022, the field had inconsistent research effort and changing publication outputs. However, the increased number of publications after 2021 indicates an increasing interest in the junction of AI and teacher cooperation. This growing trend implies that educators are becoming more aware of AI’s potential to improve collaborative research, knowledge dissemination, as well as and teaching practices. In addition, the average yearly growth rate for publication is 6.82 per year, indicating a gradual increase in research interest. The increasing citation numbers, however erratic, indicate that the field is increasingly gaining respect among the academic world. However, the data lacks a thorough examination of the research methodology used and the theoretical structures that underpin these investigations. It is critical to investigate if the increase in publications is accompanied by the development of strong theoretical models, particularly those relating to educational learning communities (TLCs) and professional learning networks (PLNs).

Figure 2. Annual scientific production.

Source: BiblioShiny.

4.3 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.

4.4 Word Cloud, and Treemap

The rise in publications, particularly the prominences of terms like “Artificial Intelligence,” “Teacher,” “Learning,” and “Students,” indicates a growing interest in how AI can enhance educational processes. However, the focus appears heavily centered on technical applications and individual learning outcomes, with less emphasis on collaborative practices among educators.

Figure 4. Word Cloud.

Source: BiblioShiny.

For practice, this trend suggests that while AI tools are increasingly integrated into education, their application is primarily geared toward instructional efficiency and personalization rather than fostering collaborative research and knowledge sharing. For policy, the emphasis on benchmarking, privacy, and computing highlights concerns about standardization, ethical considerations, and technological accessibility.

Research Methodologies:

The word cloud reveals limited emphasis on theoretical frameworks or collaborative methodologies. The frequent appearance of “Publication” and “Article” suggests a descriptive, publication-focused approach, likely involving bibliometric analysis, systematic reviews, or conceptual studies. However, the lack of terms directly related to qualitative or empirical research suggests that methodological diversity is lacking.

Theoretical Frameworks:

The word cloud does not prominently feature established theoretical frameworks like Teacher Learning Communities (TLCs) or Professional Learning Networks (PLNs). This absence implies that most studies have not grounded their analysis in collaboration theories, which could have provided deeper insights into how AI supports or hinders collaborative dynamics among educators.

4.5 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.

4.6 Country-specific Production and Collaborations

The cooperation globe map ( Figure 6) emphasizes international collaborations related to AI and teacher collaboration research in education. The United States and Hong Kong emerge as the most influential states, with the strongest international linkages. This partnership pattern is most likely impacted by their superior research infrastructure, funding availability, and the active participation of academics from prestigious universities such as the Chinese University in Hong Kong and North South University.

Figure 6. Most productive country.

Source: BiblioShiny.

The Growing contributions from these nations indicate a growing organizational interest in AI-enhanced research in education. Still, the dominance of certain nations raise concerns about the worldwide scope of study in this sector. Future research should investigate if research results from underrepresented nations are absorbed into the larger discourse, or whether there is a geographical bias in AI-related teacher cooperation research.

Interpretation of Trends & Implications:

While descriptive patterns provide useful insights into the total number and distribution of publications, further study is required to identify the implications for policy, practice, and research approaches.

Practical Implications:

The rapid increase in papers reflects rising recognition of AI’s potential to improve teacher collaboration. However, do these collaborations improve educational methods or only serve academic interests?

How well are AI tools used in collaborative studies to enhance instructional tactics and learning outcomes?

Policy Implications:

The global importance of the United States and Hong Kong means that AI-related educational initiatives may be heavily influenced by research from these locations.

Policies that focus on encouraging international collaboration may improve the variety and inclusion of research results.

Nature of Studies Analyzed:

The studies examined by BiblioShiny are mostly descriptive and analytical, with an emphasis on charting patterns and trends rather than doing experimental research. The methodology utilized in this bibliometric analysis is helpful in recognizing research gaps and significant contributions, however it may fall short in terms of investigating why AI is important for teacher cooperation.

4.7 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.

The quantitative analysis presented in Figure 7 (Co-occurrence network) and Table 1 (Word frequency per year) highlights the growing emphasis on Artificial Intelligence (AI) in educational research, particularly in 2023. The prominence of terms like “Teacher,” “Teachers’,” “Students,” and “Human” suggests that the integration of AI is being discussed in relation to educational stakeholders (teachers and students) and how AI can potentially transform learning and teaching practices.

However, these findings remain descriptive rather than analytical. While it’s clear that AI is a hot topic, the practical and policy-related implications are not addressed. For instance:

Implications for Practice:

The rise in publications suggests a growing interest in exploring AI’s role in education, but mostly from a technological rather than a collaborative standpoint. For educators and practitioners, understanding how AI tools facilitate co-authoring, knowledge sharing, and collaborative research design could significantly enhance collaborative practices.

Implications for Policy:

Policymakers need to address gaps in the integration of AI with teacher collaboration practices. Clear guidelines and frameworks are needed to promote ethical, effective, and equitable use of AI in educational research collaborations.

Research Methodologies:

The word cloud reveals limited emphasis on theoretical frameworks or collaborative methodologies. The frequent appearance of “Publication” and “Article” suggests a descriptive, publication-focused approach, likely involving bibliometric analysis, systematic reviews, or conceptual studies. However, the lack of terms directly related to qualitative or empirical research suggests that methodological diversity is lacking.

Theoretical Frameworks:

The word cloud does not prominently feature established theoretical frameworks like Teacher Learning Communities (TLCs) or Professional Learning Networks (PLNs). This absence implies that most studies have not grounded their analysis in collaboration theories, which could have provided deeper insights into how AI supports or hinders collaborative dynamics among educators.

4.8 The Co-occurrence Network Analysis

Different Interpretation of Co-occurrence Network Analysis:

The co-occurrence network analysis reveals different thematic streams within AI-teacher collaboration research, represented by color-coded clusters. This clustering helps identify key themes and their relationships based on the frequency and proximity of keywords. However, the interpretation lacks content-based synthesis and thematic analysis, which are essential for deeper insights.

Central Themes & Connections (Red Cluster):

Thematic Relationships (Purple & Green Clusters):

Isolated Themes (Yellow-Orange & Grey Clusters):

Research Gaps & Missing Analysis:

The analysis does not thoroughly examine:

Suggestions for Improvement:

Practical Implications

The results of this bibliometric analysis are useful for various kinds of stakeholders in the fields’ education.

Educators: Understanding AI tools’ collaborative potential can help improve teaching efficacy by reducing administrative duties, personalizing learning experiences, and delivering real-time feedback. Educators may use AI technologies to build more dynamic and welcoming educational environments, which improves overall teaching quality.

Policymakers: The study’s insights may aid in the development of policies that govern the ethical use of AI within educational environments, providing access to AI educational resources while dealing with adoption barriers. Interdisciplinary cooperation between AI specialists and teachers can be facilitated by creating guidelines which policymakers can put into place.

AI Developers: Understanding collaborative teaching challenges, AI developers can create new technologies that include adaptive learning systems, automated evaluation systems, and AI-based tools to help teachers maximize student participation and achievement.

These practical implications underline the need for a synergistic approach including educators, policymakers, and AI developers to create productive AI-teacher collaboration and enhance educational methods.

Ethics and consent

Ethical approval and consent were not required.