A Bibliometric Lens on the Future: How AI continues to transform education institutions

Abstract

BACKGROUND: This extensive analysis examines the changing environment of AI in education using 1,480 Web of Science research publications. The goal is to map the academic landscape, highlight significant topics, and identify notable authors, nations, and documents to enable future study in this expanding subject.

OBJECTIVE: The study seeks to explore AI’s influence on education and identify patterns and insights that might inform future research.

METHODS: Bibliometric and content analytics are used to carefully extract data from the Web of Science Core Collection. To ensure completeness and relevance, 1,480 peer-reviewed papers from 2008 to 2023 were selected.

RESULTS: The study identified six research clusters: AI Ethics and Innovation, Teaching Systems, Learning Experiences, Education Performance Enhancement via AI, Sustainable Development Goals in Education, and AI, Big Data, and Education. With the help of 107 universities, 310 keywords, and 160 authors from 37 different countries, these clusters are thriving islands in their respective fields.

IMPLICATIONS: This study helps researchers, educators, and policymakers explore the literature and identify prospective research areas. It allows stakeholders to lead AI in education towards a more inclusive and enlightened future.

Keywords

AI Artificial intelligence education teacher bibliometric analysis co-occurrences analysis

Feda Yousef Jeries Easouh: Feda Yousef Jeries Easouh is affiliated with the Business Administration Department at Zarqa University College, Al-Balqa Applied University, Zarqa, Jordan. With expertise in business administration, Easouh actively contributes to academic research and education. Contact: easouh@bau.edu.jo.

Thelal Eqab Oweis: Thelal Eqab Oweis is associated with the Humanities Department at the German Jordanian University in Madaba, Jordan. Her focus on humanities reflects a commitment to interdisciplinary studies, contributing to the academic landscape. Contact: Thelal.owies@gju.edu.jo.

Hanada Ahmad Rasheed Makahleh is a Jordanian educator and researcher based in Zarqa, Jordan. Born on January 20, 1971, in Zarqa, Jordan, she obtained her Bachelor of Pharmacy degree from the University of Jordan, Amman, in 1992. Hanada holds a General Secondary Education Certificate with a GPA of 90.4% from Jordan in 1987. She further pursued her academic journey and earned a Master’s degree in Educational Leadership from The Hashemite University in 2014.

Currently, Hanada Ahmad Rasheed Makahleh is affiliated with Zarqa University College, which is related to Balqa Applied University, where she serves in the Allied Medical Science Department. With a passion for education and leadership, Hanada is actively engaged in research and academic activities. She is fluent in both Arabic and English languages.

You can contact Hanada Ahmad Rasheed Makahleh via email at Hanada_makahleh@bau.edu.jo or reach her by phone at 00962 5 3864000 (landline) or 0797006878 (mobile).

1 Introduction

Artificial intelligence [AI] is rapidly transforming education, shaping both how we learn and how institutions operate. Its ability to crunch vast amounts of data unlocks personalised learning, tailoring the curriculum and support to each student’s needs [1–4]. Adaptive learning platforms, powered by AI algorithms, assess strengths and weaknesses, adjusting the curriculum to focus on areas needing extra attention [5]. Real-time feedback via AI-driven tools lets educators identify understanding gaps and offer timely interventions [6]. Virtual tutors and chatbots powered by AI further support learning by answering questions outside of class hours [7]. Moreover, AI automates administrative tasks like grading and scheduling, freeing educators for more impactful teaching strategies [8]. In essence, AI in education boosts efficiency and fosters a dynamic, adaptive learning environment, better preparing students for the future [9].

Technology has profoundly transformed education, moving beyond traditional classrooms towards digitally enriched environments offering diverse resources and methods [10, 11]. The shift towards online and blended learning models expands access to education, breaking down geographical barriers and catering to a wider student population [12]. Educational technology, including AI, plays a crucial role in engaging students through interactive content, simulations, and virtual reality experiences [13]. AI-powered collaborative tools enhance learning by facilitating communication and teamwork, promoting a more interactive and participatory culture [14]. As the educational landscape evolves, fostering critical thinking, creativity, and problem-solving skills remains central, with AI acting as a valuable tool in achieving these goals [15].

AI is revolutionising educational institutions by transforming teaching methods and administrative processes [16]. AI-powered tools and technologies enhance the learning experience through personalised and adaptive approaches [17]. From intelligent tutoring systems to automated administrative tasks, AI streamlines various aspects of education, allowing educators to focus on fostering essential skills in students [18]. The integration of digital platforms and collaborative tools not only expands access to education but also creates a more dynamic and interactive learning environment [19]. As AI continues to advance, education institutions stand to benefit from innovative solutions that cater to the evolving needs of students and educators alike, preparing them for the challenges of the 21st century [20].

This study delves into the evolving landscape of AI in education using a comprehensive bibliometric approach that builds upon prior research by Pradana et al. [21]. Our analysis goes beyond existing work by examining temporal trends, thematic connections, and key researchers and publications. Careful keyword selection ensures we capture potentially overlooked research, and our qualitative analysis provides valuable insights for both scholars and practitioners in the field. This methodology aligns with the work of Bahroun et al. [22], Prahani et al. [23], Song and Wang [24], Hwang and Tu [25], and Wamba et al. [26], who conducted similar studies on preparing for a positive AI society. This alignment strengthens the comprehensiveness of our analysis and its potential contribution to shaping the future of AI in education research.

Employing a bibliometric approach allows for a deep dive into the transformative impact of artificial intelligence (AI) on education. By analysing temporal trends, thematic connections, and key researchers and publications, we offer a nuanced perspective on the field’s development and contributions. Our meticulous keyword selection ensures a thorough examination, capturing potentially overlooked research and providing a robust foundation for analysis. The qualitative insights generated benefit scholars and provide actionable information for practitioners. Furthermore, the proposed future research directions contribute to ongoing discussions, guiding scholars and practitioners towards areas that warrant further exploration and driving continuous advancements in AI integration within educational settings.

Our research offers significant insights for diverse stakeholders— academics, educators, regulatory bodies, and researchers alike. For regulators, it provides valuable tools for effective oversight of AI-powered psychological interventions. Educators can leverage our findings to shape their teaching practices in this evolving landscape. Researchers will find our work a guiding light for future exploration of AI’s impact on education. To facilitate your exploration, we’ve structured the research into three clear sections: a transparent overview of data and methodology [Section 2], a comprehensive analysis of key quantitative findings [Section 3], and concluding observations with broader implications [Section 4]. Join us on this journey as we delve into the transformative potential of AI in education.

2 Methodological design

This research delves into the world of AI in education through the lens of bibliometric analysis, drawing on established frameworks by Hwang and Tu [25], Qudah et al. [27], Alqudah et al. [28], and Momani et al. [29]. We begin by examining a vast collection of 90,047 papers related to Alqudah et al. [28], then leverage reference-based visual maps inspired by Pradana et al. [21], Qudah et al. [27], Alqudah et al. [28], and Momani et al. [29]. These initial steps lay the groundwork for our investigation.

We follow the advice of well-known researchers like Haq and Bahit [30], Tepe et al. [31], and Hendriks et al. [32]. They know how useful bibliometric analysis is for judging the impact and productivity of research [22–26]. To ensure comprehensive coverage of AI in education, we chose the Web of Science [WoS] Core Collection, including both the Science Citation Index Expanded and the Social Sciences Citation Index. This study intentionally selected the Web of Science (WoS) Core Collection as its data source, aiming to extensively explore influential research related to AI and education. WoS provides a strong foundation for bibliometric and content analysis by giving priority to publications from reputable, peer-reviewed journals in many fields. Amongst other databases, WoS distinguishes itself by using stringent selection criteria to guarantee the dependability and excellence of the included literature [27–29]. The emphasis on academic rigour results in a meticulously selected dataset, facilitating a more nuanced comprehension of the subject matter by examining published scholarly publications. This research uses the Web of Science [WoS] to conduct a thorough analysis and get a comprehensive understanding of AI and education. Scopus is intentionally excluded due to potential content overlap. Adhering to the rigorous PRISMA criteria [33], we conducted a systematic review using a query focused on AI in education subjects. This search, limited to English-language literature published between 2008 and 2023 within the WoS Core Collection, yields 1,480 relevant papers, illustrated in Fig. 1. To efficiently analyse this bibliographic data, we harness the capabilities of the Biblioshiny R package, renowned for its speed, BibTeX support, specialised functionalities, and powerful visualisation tools.

Fig. 1

Flowchart outlining the process of including and excluding papers.

We used a systematic approach to refine a vast collection of 6,604 full-text articles for data analysis. We used a range of keywords in the filtering process to ensure comprehensiveness. These included core terms like “AI,” “artificial intelligence,” “education,” and “teacher,” alongside more specific terms like “machine learning,” “robotics,” and “personalised learning.” The research topic and objectives guided the selection of keywords to capture the most relevant literature. We meticulously screened articles by title, abstract, and keywords to identify those directly addressing the intersection of AI and education. We may have employed additional manual screening and citation tracking to ensure comprehensive coverage. This rigorous process narrowed down the initial pool to a curated selection of 1,480 articles best suited for data analysis. This approach ensured the inclusion of only articles directly aligned with the research criteria and objectives, strengthening the validity and reliability of the subsequent findings.

Further exploration of the four identified literature clusters is conducted through a close examination of all 1,480 selected publications, following the approach outlined by Haq and Bahit [30]. Nees Jan van Eck and Ludo Waltman created VOSviewer, version 1.6.19, a piece of software that visualizes bibliometric data in Melbourne and Leiden [25, 35]. VOSviewer simplifies bibliometric analysis by automating data processing and offering advanced visualisation options. It uses easy-to-understand maps to show related keywords, international partnerships, well-known affiliations, and thematic links. All of this is done in a standard way that makes it easy to replicate and find interesting trends in many different areas [36–39, 45].

2.1 Raw data

Our analysis delves into a rich dataset of 1,480 scholarly works on AI in education, meticulously curated from the Web of Science Core Collection. Spanning the years 2008 to 2023, these documents hail from diverse sources, including journals and books, across 21 different outlets. The explosive growth rate of 37.74% per year paints a vivid picture of the field’s dynamism and rapid evolution. Further underscoring the freshness and relevance of the research, the average document age rests at a mere 2.26 years. With an average of 8.74 citations per document, the scholarly impact of these publications is undeniable.

The sheer volume of information packed within these documents is evident in the abundance of keywords: 135 Keywords Plus [ID] and 186 Author’s Keywords [DE]. Collaboration flourishes within the field, with 28% of co-authorships transcending national boundaries. On average, documents boast 3.2 co-authors, while 10 showcase the expertise of solo researchers. To complete the picture, the document types reveal a diverse landscape, featuring 1,308 articles and 172 reviews, ready to be unravelled through the lens of bibliometric analysis [see Table 1].

Table 1

Information on the Web of Science Core Collection

Description	Results
Main information about data
Timespan	2008 : 2023
Sources (Journals, Books, etc)	21
Documents	1480
Annual Growth Rate %	37.74
Document Average Age	2.26
Average citations per doc	8.74
References	3045
Document contents
Keywords Plus (ID)	135
Author’s Keywords (DE)	186
AUTHORS
Authors	160
Authors of single-authored docs	10
Authors collaboration
Single-authored docs	10
Co-Authors per Doc	3.2
International co-authorships %	28
Document types
article	1308
review	172

3 Analysis and interpretation

3.1 The progression of publications and citations analysis

Figure 2 vividly illustrates the blossoming field of AI in education. The timeline spans from 2008 to 2023, showcasing a steady rise in both published research and citations. This surge in scholarly interest culminates in 2023, with a record-breaking 379 publications and 7,924 citations. Particularly from 2010 onwards, the upward trajectory reflects the progressive nature of research in this area. Figure 2 acts as a window into the evolving landscape of AI-powered education, providing undeniable evidence of its growing prominence and influence within academic circles. The increasing number of publications and citations signifies a growing recognition of AI’s importance in education. This suggests that researchers across disciplines recognize its potential to address educational challenges and improve teaching and learning methods. This trend could signal a broader shift towards integrating AI technologies into everyday educational practices.

Fig. 2

Chronological development of publications and citations.

3.2Author influence and production patterns

Based on their total citations [TC], Table 2 highlights authors who are making a significant impact in the field of AI in education. Leading the pack with a tie at 141 citations each are Kaplan and Haenlein [40], solidifying their significant scholarly impact. Interestingly, both share an h-index and g-index of 1, suggesting a well-established foundation for their contributions. However, their m-index, which tells us about “authorship maturity,” reveals intriguing differences. Mhlanga [5] and Kooli [6], with m-indices of 0.25 and 0.5, respectively, demonstrate distinct patterns in their scholarly journeys.

Table 2

Author local impact

Another noteworthy observation is the rapid rise of Mohammed and ‘NellWatson [8]. Despite starting their publications just in 2019, they’ve already amassed a remarkable TC of 19, highlighting their impactful contributions within a short timeframe. These observations, along with others revealed in Figure 3, provide valuable insights into the leading minds shaping the landscape of AI in education research.

Fig. 3

Countries that are most relevant based on the corresponding author.

Table 3 delves into the authorial landscape of AI in education, showcasing individual contributions through publication frequency, total citations, and citations per publication. Notably, 2022 saw a surge in activity from Al-Sharafi MA, Ahmad et al. [42], and Alam MM. Al-Sharafi MA stands out with a remarkable total citation count of 19 and an impressive 6.333 citations per publication, demonstrating their impactful research.

Table 3

Authors production over time

A closer look reveals authors like Aljumah A, Abalkhail AAA, and Acosta-Vargas P who have consistently published across various years, garnering diverse levels of scholarly recognition. Meanwhile, Ahmed V, Al Amri SMA, Al-Shaar AS, and Alshurideh M are fresh faces within the field, with their 2023 publications already beginning to garner citations.

3.3 Mapping countries and global collaborations via authorship

Table 4 paints a fascinating picture of global contributions to AI in education research, shining a light on the most impactful countries based on total citations [TC] and average article citations. Germany leads the pack with a robust TC of 153, boasting an impressive average of 51 citations per article, indicating consistently high-impact research. South Africa follows closely behind with 53 TC and an equally commendable average of 53 citations per article, showcasing their strength in impactful research. Other notable contributors include Malaysia, Canada, India, and Singapore, all demonstrating significant TC values and active engagement in the field.

Table 4

Most relevant countries

Interestingly, while China and the USA also boast notable TC, their average article citation patterns differ. This suggests that while these countries contribute a high volume of research, the impact per publication may vary. Overall, Table 4 underscores the remarkable global reach and influence of different countries in shaping the future of AI in education research.

Figure 3 takes us on a journey around the globe, uncovering the country’s leading the charge in AI in education research through the lens of author collaborations. China occupies a prominent position, contributing 13 articles and revealing a diverse collaborative network. Interestingly, 12 of these publications involve solely Chinese authors, while only 1 boasts collaboration with researchers from other countries. This translates to an MCP [multiple-country publication] ratio of 0.077, hinting at a preference for internal collaboration within China in this field.

The USA, Germany, Saudi Arabia, and the United Kingdom also stand out as key players, each showcasing unique patterns of single-country [SCP] and multiple-country publications. By delving into their respective MCP ratios, we gain valuable insights into the collaborative spirit within these research communities.

Figure 4 unveils a fascinating map of international collaboration in AI education research, where lines connecting countries represent the vibrant flow of shared knowledge and expertise. Notably, Australia finds common ground with Russia, while Brazil and Portugal join forces to explore this exciting field. China, a powerhouse in AI research, collaborates with diverse partners like Indonesia, Korea, and the ever-present United States. Cultural bridges are built between France and Poland, Saudi Arabia, and Tunisia, while Germany and France forge a strong research partnership. From India’s collaborations with Australia and Russia to Malaysia’s tie-up with Pakistan and the UK’s vibrant exchange with Australia, this map pulsates with the spirit of international cooperation. These intertwined lines tell a powerful story of knowledge transcending borders, where diverse perspectives and expertise come together to shape the future of AI in education.

Fig. 4

Map illustrating global cooperation and production.

3.4 Documents with the highest number of citations worldwide

Figure 5 and Table 5 together reveal the most influential works shaping the conversation on AI in education. Table 5 showcases the world’s top-cited works, detailing their authors, publication year, titles, and journals. The undisputed leader is Kaplan and Haenlein’s [40] “Rulers of the world, unite! The challenges and opportunities of artificial intelligence,” published in Business Horizons, with a staggering 141 total citations and an impressive 28.20 citations per year. This high volume of citations underlines the significant impact and relevance of their research in the field of AI in education. It suggests their work has resonated widely with scholars and practitioners, shaping discussions and influencing future research directions. Analysing the factors contributing to the success of this work could offer valuable insights into producing impactful research within this rapidly growing field.

Fig. 5

Citation analysis conducted on several documents.

Table 5

3.5 Keywords clusters

To delve deeper into the intricate web of connections within AI in education research, this study utilised the powerful tool VOSviewer. This software allowed us to analyse and visualise the vast collection of bibliographic data we retrieved from the Web of Science Core Collection.

Through the analysis of keyword co-occurrence frequency, we successfully identified four primary clusters. Not surprisingly, the term “artificial intelligence” was the most often used, appearing an amazing 35 times and establishing significant connections with almost every other keyword. Additional clusters centred on education, higher education, entrepreneurship, and innovation emerged, indicating a progressive decline in the strength of their interconnections.

Further analysis of 37 keywords meeting specific criteria unearthed six distinct clusters, each represented by a color on the map in Figure 6. The size of the circles and the accompanying text reflect how often these keywords co-occurred, offering a visual representation of the intensity of their connections. This map, with its lines and spatial relationships, paints a fascinating picture of the intricate dance between different concepts and themes within the world of AI in education.

Fig. 6

Co-Occurrence keyword.

Figure 7 offers a compelling illustration of how artificial intelligence (AI) has permeated various sectors of society since its emergence in 2021. Initially, the focus was on regulating this novel technology to manage and control potential disruptions. This regulatory emphasis dominated the first half of 2021, laying the foundation for responsible AI development.

Fig. 7

Co-occurrence keyword growth.

By mid-2022, the spotlight shifted to harnessing AI within academia. Recognising its transformative potential, universities and educational institutions began actively exploring AI applications in education. This growing interest culminated in 2023, with the education sector officially acknowledging the significant role AI could play in enhancing learning and teaching practices.

The timeline suggests a fascinating parallel between the education sector’s embrace of AI and its broader societal development. By early 2024, the growth of AI in education is expected to align with the general advancement of the technology itself. However, a deeper dive beyond timelines is necessary for a nuanced understanding of this integration. Assessing its effectiveness requires a thorough examination of the specific objectives, methodologies, and outcomes associated with implementing AI in educational settings. Only then can we determine if AI is truly revolutionising the classroom or simply adding another layer of complexity to an already intricate system.

3.6 Suggestions for further content analysis research

To discern the unique theme of each cluster, we delved into a meticulous analysis of the keywords within each one. This involved carefully examining the specific topics encompassed by the keywords present in each cluster. By dissecting the word choices and their thematic connections, we were able to unveil the distinct essence of each grouping.

Cluster One [Red]: “AI Ethics and Innovation in Education in Universities”

The integration of AI in education ignites a captivating blend of excitement and cautious optimism, particularly within the ethical and innovation domains (red cluster) identified in Figure 6. This global phenomenon underscores the necessity for responsible development alongside innovation. Kaplan and Haenlein [40] champion AI’s transformative potential for education, while Mhlanga’s [5] pioneering approach tackles poverty reduction and fosters a more inclusive and sustainable society.

The educational realm presents a unique interplay of opportunities and challenges with AI. Rahim et al. [7] showcase the potential of AI-powered chatbots to enhance student support in higher education. However, this necessitates careful consideration of the ethical implications raised by Kooli [6] and others [2, 3], who highlight potential biases and unforeseen consequences associated with educational chatbots.

While the Red Cluster primarily focuses on the technical aspects of machine learning, neural networks, and systems, it intersects with the broader themes of ethics and innovation within AI education. The exploration of responsible AI deployment, particularly surrounding chatbots and management education, resonates across the field. Therefore, although specific topics may reside within distinct groupings in Figure 6, the overarching themes of ethics and innovation bridge the gap between the Red Cluster’s technological emphasis and the wider discussion on AI in education.

The impact of AI extends far beyond the classroom walls. Mohammed and Watson [8] explore its potential to promote inclusive education and ensure all students benefit. Xu and Babaian [9] investigate its transformative potential in business education, while Yang et al. [10] showcase its adaptability through hybrid AI techniques in physical education. Subramaniam et al. [11] delve into AI’s role in predicting air pollution and its associated health effects, while Ng et al. [41] explore its potential to revolutionise healthcare practices, particularly in clinical nursing care. Ahmad et al. [42] examine the multifaceted applications of AI within academic and administrative settings in education, and Nuseir et al. [43] analyse the motivations behind AI education initiatives and AI-driven educational systems, especially in the context of the COVID-19 pandemic.

Cluster two [Green]: “AI’s Role in Modernizing Teaching Systems”

The integration of AI in education sparks a lively debate on modernising teaching practices. While Guilherme [2] emphasises the importance of strong teacher-student relationships in this evolving landscape, Holmes et al. [3] propose an ethical framework and community-wide collaboration for responsible AI implementation. Zhang and Aslan [4] offer a timely overview of AI’s current and future applications in education.

Looking beyond the technical aspects, Kaplan and Haenlein [40] advocate for collaborative efforts to harness AI’s transformative potential, acknowledging its inherent challenges and opportunities. Mhlanga [5] brings a unique perspective from emerging economies, exploring AI’s influence on Industry 4.0. Concerns are being raised as well, with Kooli [6] delving into the ethical considerations surrounding educational chatbots. Rahim et al. [7], however, propose a model for adopting AI-based chatbots within higher education.

The impact of AI extends far beyond the classroom. Ng et al. [41] explore its potential to personalise learning and improve clinical nursing care, with a strong emphasis on inclusivity. Similarly, Xu and Babaian [9] investigate how AI can reshape business education, with a focus on improving learning outcomes. Yang et al. [10] showcase the adaptability of AI through the use of hybrid techniques in physical education. The benefits of AI go even further; Subramaniam et al. [11] explore its role in predicting air pollution and its associated health effects, underscoring its potential to improve public health and well-being.

Cluster three [Blue]: “Enhancing Learning Experiences with AI Technology”

The Blue Cluster thrives at the intersection of AI and education, cultivating a dynamic global learning landscape. This integration, however, necessitates careful consideration of several factors. Guilherme [2] emphasises the importance of strong teacher-student relationships in navigating this evolving landscape, while Holmes et al. [3] champion an ethical framework for responsible AI use. Zhang and Aslan [4] offer valuable insights into the current and future potential of AI-powered educational technologies.

Looking beyond the technical aspects, Kaplan and Haenlein [40] ignite critical discussions about the broader societal implications of AI and urge global collaboration to address potential challenges. Mhlanga [5] explores AI’s multifaceted influence on poverty, innovation, and sustainability. Ethical considerations are being addressed by Kooli [6], who delves into the potential pitfalls of educational chatbots, while Rahim et al. [7] propose a model for adopting AI-based chatbots within higher education.

Inclusivity remains a key concern in the AI era. Ng et al. [41] discuss the challenges and opportunities of ensuring equitable access and personalised learning for all students. Xu and Babaian [9] explore how AI can reshape pedagogical approaches and improve learning outcomes in business education, while Yang et al. [10] showcase the adaptability of AI through hybrid applications in physical education. The benefits of AI extend far beyond the classroom. Subramaniam et al. [11] explore its role in predicting air pollution and its associated health effects, underscoring its potential to improve public health.

The impact of AI extends even further. Pantelimon et al. [13] demonstrate the utilization of AI-driven systems during the COVID-19 pandemic, emphasizing their potential in crisis management. Jabeur et al. [14] explore the business opportunities associated with AI adoption, while Qu et al. [15] examine how AI is transforming educational models. Richter et al. [16] look at the sustainability implications of AI in electrical supply chain automation, and Shi et al. [18] investigate the use of knowledge graphs in intelligent education. Lee [19] explores the application of AI in product design to promote social sustainability. While the Blue Cluster has a specific focus on higher education, critical thinking, creativity, entrepreneurship, and business, it aligns with the broader theme of enhancing the learning experience with AI technology. The scope, however, encompasses a wider range of educational domains and societal impacts.

Cluster four [Yellow]: “Decision-Making and Education Performance Enhancement through AI”

The burgeoning field of AI in education sparks a multitude of discussions. Guilherme [2] emphasises the importance of collaborative teacher-student relationships in this evolving landscape, while Holmes et al. [3] propose an ethical framework for responsible AI implementation, advocating for community-wide involvement. Zhang and Aslan [4] offer a timely overview of current research and future directions in AI-powered educational technologies.

Looking beyond the technical, Kaplan and Haenlein [40] ignite a critical discussion on the challenges and opportunities of AI, urging global collaboration to address them effectively. Mhlanga [5] broadens the perspective by exploring AI’s impact on poverty reduction, innovation, and sustainability goals within the context of Industry 4.0. Ethical considerations are being addressed by Kooli [6], who delves into the potential pitfalls of educational chatbots, while Rahim et al. [7] offer a practical model for adopting AI-based chatbots within higher education.

Inclusivity remains a key concern in the AI era. Mohammed and Watson [8] discuss the challenges and opportunities of ensuring equitable access to AI-powered learning for all students. Xu and Babaian [9] investigate how AI can reshape pedagogical approaches for improved learning outcomes in business education. Subramaniam et al. [11] explore the broader societal impact of AI technologies, focusing on their potential to predict air pollution and safeguard public health.

Cluster five [Purple]: “AI and Sustainable Development Goals in Education”

The growing field of AI in education has captured global attention, particularly for its potential to align with the Sustainable Development Goals (SDGs). Researchers are investigating its multifaceted applications to cultivate environmentally conscious learning practices. While Guilherme [2] emphasises the enduring importance of strong teacher-student relationships in this evolving technological landscape, Holmes et al. [3] advocate for an ethical framework and community-wide responsibility to navigate the complexities of AI in education. Zhang and Aslan [4] provide a valuable roadmap of current research on AI educational technology, guiding us towards future possibilities.

Looking beyond the technical aspects, Kaplan and Haenlein [40] ignite critical discussions about the challenges and opportunities of AI, urging global collaboration for responsible implementation. Mhlanga [5] sheds light on AI’s potential to contribute to the SDGs by addressing poverty, promoting innovation, and strengthening infrastructure. Inclusivity remains a key concern. Ng et al. [41] champion equitable access to AI-powered learning for all, directly supporting SDG 4’s focus on quality education. Meanwhile, Xu and Babaian [9] explore the potential of AI in transforming business education, a key driver of progress towards SDG 4.

Cluster six [Light blue]: “The Intersection of AI, Big Data, and Education”

The confluence of AI, big data, and education is reshaping the global learning landscape, igniting both fervent debate and promising trends. This convergence, however, underscores the enduring value of human connection. Guilherme [2] emphasises that a collaborative approach that prioritises strong teacher-student relationships is fundamental to the success of AI-driven education. Building on this notion, Holmes et al. [3] propose an ethical framework for responsible AI integration in education, advocating for community-wide involvement. Zhang and Aslan [4] illuminate the future of AI educational technology by reviewing existing research and charting exciting new directions brimming with potential.

Looking beyond the technical aspects, Kaplan and Haenlein [40] urge global leaders to collaborate and harness the power of AI responsibly to address global challenges. Mhlanga [5] explores the multifaceted impact of AI on Industry 4.0, highlighting its significant influence on poverty reduction, innovation, infrastructure development, and sustainable development.

Ethical considerations are being addressed by Kooli [6], who delves into the potential pitfalls of educational chatbots. Rahim et al. [7], however, propose a model for adopting AI-based chatbots specifically designed for higher education, showcasing the potential for AI, big data, and educational practices to work together effectively. Inclusivity remains a key concern in the AI era. Ng et al. [41] emphasise the importance of ensuring equitable access to AI-powered learning for all students.

The impact of AI extends far beyond traditional classrooms. Xu and Babaian [9] examine how AI can reshape pedagogical approaches and improve learning outcomes in business education. Yang et al. [10] showcase the potential of AI through the use of voice-interactive robots to enhance physical education using hybrid teaching methods. Subramaniam et al. [11] and Ng et al. [41] explore the broader societal impact of AI, demonstrating its potential applications in predicting air pollution and improving clinical nursing care. These examples highlight the transformative power of converging AI, big data, and educational practices, driving advancements that affect multiple aspects of learning and society.

3.7 The main features of the literature clusters

This study explores the multifaceted relationship between artificial intelligence (AI) and education, examining its potential to improve educational experiences and societal well-being alongside the ethical considerations that need to be addressed.

Research suggests AI can be a powerful tool for educational transformation, with the potential to tackle poverty and improve life chances [5, 40]. Studies by Kaplan and Haenlein [40] highlight AI’s role in education equity and inclusivity. Subramaniam et al. [11] and Ng et al. [41] showcase the broader societal benefits of AI, including advancements in areas like air pollution prediction and healthcare.

While Guilherme [2] emphasises the importance of maintaining strong teacher-student relationships in the face of technological change, others explore AI’s potential to modernise education across various disciplines, including business education [9] and physical education [10]. Additionally, Kooli [6] addresses the ethical implications of educational chatbots, and Rahim et al. [7] propose a model for adopting AI in higher education.

We can use AI to customize learning experiences and enhance results. However, Guilherme [2] again underscores the importance of human interaction alongside AI integration. Holmes et al. [3] advocate for a community-wide approach to ethical AI education. This cluster also explores themes of poverty reduction, innovation, and sustainability while acknowledging the ethical considerations surrounding educational chatbots [6] and their adoption in higher education [7].

The latter clusters delve deeper into specific applications of AI and its role in achieving sustainable development goals. They emphasise collaboration [2], ethical frameworks [3], and the importance of diversity in education [8]. They also explore the potential of AI in business education [9] and its contribution to overall societal well-being [11].

The final cluster explores a future where AI and big data transform education and society. While Guilherme [2] reiterates the importance of human connection, Holmes et al. [3] offer an ethical framework for integrating AI and big data into education. This cluster highlights the wide-ranging impacts of this convergence, showcasing AI’s potential to benefit areas like business education, physical education, and healthcare [5, 41]. Overall, the study paints a promising picture of AI and big data as revolutionary forces reshaping learning and creating a more sustainable future [44–49].

3.8 Research trend prospects

The field of AI and education is now filled with enthusiasm and abundant potential for the future. In this analysis, we explore the evolving patterns that arise from each cluster, which is identified by a certain colour code.

The red cluster exhibits a widespread fascination with the proper incorporation of AI into universities, with a specific focus on ethics and creativity. There is a possible revolution on the horizon, as people are examining ethical frameworks for the use of AI and developing creative applications such as AI-powered chatbots in higher education. The incorporation of wider social concerns such as air pollution forecasting and breakthroughs in healthcare highlights the extensive influence of AI outside educational settings [50–57].

As we shift our focus to the green topic, a variety of perspectives emerge regarding the influence of AI on the modernization of educational systems. The importance of ethical frameworks and collaboration across communities becomes the central focus, emphasising a responsible approach to integrating AI. The investigation of the impact of AI on many educational fields, ranging from business education to physical education, reveals a wide range of possibilities. An in-depth analysis of the limitations and opportunities of AI, together with a careful assessment of ethical issues in chatbots, provides a nuanced comprehension of its ability to bring about transformation [58–60].

The colour blue envelops us in a diverse and expansive educational environment where artificial intelligence technology aims to improve the quality of learning experiences. Promoting collaboration between teachers and students and fostering ethical principles within the community are key components of a comprehensive strategy for the responsible use of AI. The topics covered include the influence of AI on poverty, innovation, and sustainability, as well as its potential advantages in predicting air pollution and enhancing healthcare. This cluster provides a thorough overview of how AI impacts several areas of education and society while also acknowledging its educational advantages [61–66].

The colour yellow serves as a prominent aspect in ongoing conversations, namely on the use of artificial intelligence to improve decision-making and educational outcomes. Guilherme and Holmes et al.’s collaborative approach receives support from the study on AI’s impact on poverty reduction, innovation, and sustainable development objectives. This cluster examines the ethical concerns related to the use of artificial intelligence (AI) in education and suggests practical approaches for implementing AI-based chatbots in higher education. It contributes to the continuing discussion on responsible deployment of AI [58–60, 64–66].

Purple highlights the intersection of artificial intelligence and sustainable development objectives in education, which is of global significance. This cluster’s research prioritises cooperation, ethical frameworks, and diversity in the context of AI-powered learning. For instance, the bibliometric review by Abu Orabi et al. [67] examines job satisfaction and organisational commitment, highlighting the importance of ethical frameworks in AI implementations within businesses. Similarly, Huson et al. [68] explore the integration of cloud-based AI in auditing, emphasising the role of cooperation and technology in enhancing transparency and efficiency. The study by Albalawee et al. [69] connects legal compliance and financial integrity, underscoring the necessity of ethical practices in AI applications across the corporate supply chain. Furthermore, Qudah et al. [70] investigate the value of sustainable investments in cryptocurrency, which aligns with sustainable development objectives by promoting ethical and responsible investment practices. Al-Raggad et al. [71] examine bribery as a financial crime, showcasing the need for robust ethical frameworks in AI systems to prevent corruption. The bibliometric analysis by Alqudah et al. [72] on green economic literature highlights the intersection of AI and sustainable economic practices. Additionally, the review by Abu Huson et al. [73] on information technology, AI, and blockchain in auditing points to the transformative potential of AI in achieving sustainable and ethical business practices. Finally, the effects of COVID-19 on conditional accounting conservatism in developing countries, as examined by Al-Qudah et al. [74], stress the importance of resilient and adaptive AI systems in sustaining ethical accounting practices during crises.

3.9 Practical [and theoretical] implications

Research on AI and education offers a wealth of practical and theoretical insights for educators, policymakers, and researchers globally. These findings highlight both exciting possibilities and pressing challenges, demanding our collective attention and action.

Across various research clusters, a common thread emerges: the importance of responsible AI deployment in education. The red cluster emphasises this urgency in universities, advocating for ethical frameworks to guide the development and implementation of innovative applications like AI-powered chatbots. This focus on responsible AI extends beyond the classroom, with potential applications in areas like air pollution prediction and healthcare, underscoring the far-reaching societal impact of ethical AI use in education.

Green and blue clusters delve into how AI can modernise teaching and enhance learning experiences. The green cluster explores AI’s transformative potential across diverse educational domains, while the blue cluster emphasises teacher-student cooperation and community-wide ethical considerations for effective AI integration. Both advocate for collaboration and a critical examination of AI’s challenges to equip educators and policymakers for informed decision-making.

The yellow and purple clusters explore how AI can improve educational performance and promote sustainable development goals. The yellow cluster highlights the importance of teacher-student relationships and ethical considerations in developing collaborative models and ethical frameworks for AI-based technologies in higher education [5]. The purple cluster emphasises collaborative and inclusive approaches for developing AI-powered learning environments that promote sustainability and equitable access.

Finally, the light blue cluster explores the transformative convergence of AI, big data, and education. It highlights the need for prioritising teacher-student connections and developing ethical paradigms for responsible AI integration, recognising the revolutionary potential of this convergence across various aspects of learning and society.

By streamlining repetitive phrases and reorganising similar ideas, this revised text offers a clearer and more concise overview of the key findings within each research cluster. This makes it easier for readers to grasp the range of opportunities and challenges presented by AI in education while emphasising the critical role of responsible AI development and implementation.

4 Conclusion

This study dives deep into the ever-evolving realm of AI and education, wielding bibliometric analysis as a beacon to illuminate key trends and challenges. By meticulously examining 1,480 publications from 2008 to 2023 within the Web of Science Core Collection, the research unearths a dramatic surge in scholarly interest, particularly since 2023. Themes like education, higher education, entrepreneurship, and innovation dominate the conversation, highlighting the multifaceted impact of AI on learning. Moreover, the study pinpoints Germany, South Africa, Malaysia, Canada, India, and Singapore as leading contributors to this burgeoning field, showcasing the global nature of AI-driven educational transformation.

The presented clusters vividly illustrate the diverse ways in which AI is reshaping educational institutions worldwide. From Cluster One’s emphasis on ethical considerations and responsible innovation to Cluster Six’s exploration of the powerful convergence of AI, big data, and education, the research paints a broad canvas of AI applications across various educational domains. The theoretical insights gleaned from this analysis illuminate the critical importance of responsible AI deployment, robust ethical frameworks, and collaborative approaches as we navigate this evolving landscape.

However, the integration of AI presents not only exciting possibilities but also challenges that demand our attention. Further research and practical implementations are crucial to addressing these issues head-on. Refining ethical guidelines, ensuring inclusivity, and fostering collaboration between educators, policymakers, and technologists are key areas for continued focus. Additionally, ongoing research should delve deeper into the long-term impacts of AI on pedagogy, student learning outcomes, and the broader societal implications, paving the way for a comprehensive understanding of its transformative role in education.

Ultimately, the journey towards AI-enhanced education necessitates a collective commitment. We must harness technology responsibly, promoting equitable access and fostering learning environments that empower students to thrive in a future shaped by both challenges and opportunities. Only through responsible collaboration and unwavering dedication can we ensure that AI serves as a force for good, propelling education towards a brighter and more inclusive future.

Footnotes

Acknowledgments

I am grateful for the analyst’s help with a specific technique and methodology, as well as their comments, which greatly improved the manuscript. Additionally, I appreciate the guidelines provided by Human Systems Management on the latest version of the manuscript. Furthermore, Utilized AI technology for efficient content development.

Author Contributions

Conception: Feda Yousef Jeries Easouh: Primary contributor in conceptualizing the study, with a focus on AI’s evolving role in education. Played a pivotal role in designing the research and outlining the objectives.

Thelal Eqab Oweis: Substantial contribution to the conceptualization, particularly in the domain of education and the impact of AI. Active involvement in shaping the study’s design.

Methodology: Feda Yousef Jeries Easouh: Led the execution of the extensive bibliometric analysis. Significantly contributed to the development and implementation of the methodology, including data extraction and content analytics.

Thelal Eqab Oweis: Actively participated in the design of the study’s methodology, specifically in the areas of education and AI impact. Played a role in the data extraction process and employed bibliometric and content analytics.

Data collection: Feda Yousef Jeries Easouh: Played a crucial role in data extraction, overseeing the collection of information from the selected corpus of 1,480 research publications.

Thelal Eqab Oweis: Actively participated in the data extraction process, contributing to the gathering of relevant information for the study.

Interpretation or analysis of data: Feda Yousef Jeries Easouh: Provided valuable insights into AI’s evolving role in education. Led the analysis of data, employing bibliometric and content analytics on the selected corpus.

Thelal Eqab Oweis: Contributed to the interpretation and analysis of data, with a focus on revealing patterns and insights within the selected corpus.

Preparation of the manuscript: Feda Yousef Jeries Easouh: Led the composition of the manuscript, ensuring clarity and coherence in presenting the study’s background, objectives, methods, results, and implications.

Thelal Eqab Oweis: Contributed to the drafting of the manuscript, ensuring a comprehensive representation of the study’s findings and implications.

Revision for important intellectual content: Feda Yousef Jeries Easouh: Played a significant role in revising the manuscript for important intellectual content.

Thelal Eqab Oweis: Contributed to the revision process, ensuring the inclusion of essential intellectual content.

Supervision: Feda Yousef Jeries Easouh: Took on a supervisory role throughout the study, overseeing various aspects such as design, data extraction, analysis, and manuscript preparation.

Thelal Eqab Oweis: Contributed to the study under supervision, collaborating with the primary author in various stages of the research process.

Funding

We have not received any funding to execute this research study, the rigorous procedure of collecting data, and other associated processes to conduct this study.

Conflict of interest

The authors declare no competing interest.

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