Artificial intelligence and competency-based education: A bibliometric analysis

Radu, Cătălina; Ciocoiu, Carmen Nadia; Veith, Cristina; Dobrea, Răzvan Cătălin Article Artificial intelligence and competency-based education: A bibliometric analysis Amfiteatru Economic Provided in Cooperation with: The Bucharest University of Economic Studies Suggested Citation: Radu, Cătălina; Ciocoiu, Carmen Nadia; Veith, Cristina; Dobrea, Răzvan Cătălin (2024) : Artificial intelligence and competency-based education: A bibliometric analysis, Amfiteatru Economic, ISSN 2247-9104, The Bucharest University of Economic Studies, Bucharest, Vol. 26, Iss. 65, pp. 220-240, https://doi.org/10.24818/EA/2024/65/220 This Version is available at: https://hdl.handle.net/10419/281818 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ AE Artificial Intelligence and Competency-Based Education: A Bibliometric Analysis 220 Amfiteatru Economic ARTIFICIAL INTELLIGENCE AND COMPETENCY-BASED EDUCATION: A BIBLIOMETRIC ANALYSIS Cătălina Radu1, Carmen Nadia Ciocoiu2, Cristina Veith3 and Răzvan Cătălin Dobrea4 1)2)4) Bucharest University of Economic Studies, Bucharest, Romania. 3)University of Bucharest, Bucharest, Romania. Please cite this article as: Radu, C., Ciocoiu, C.N., Veith, C. and Dobrea, R.C., 2024. Artificial Intelligence and Competency-Based Education: A Bibliometric Analysis. Amfiteatru Economic, 26(65), pp. 220-240. DOI: https://doi.org/10.24818/EA/2024/65/220 Article History Received: 20 September 2023 Revised: 18 November 2023 Accepted: 15 Decemeber 2023 Abstract In the context of the educational transition toward a competency-based approach, this study aimed to identify trends, challenges, and emerging opportunities generated by the intersection of Artificial Intelligence (AI) and Competency-Based Education (CBE). The research was carried out using a bibliometric analysis of 1,028 articles included in the Web of Science database and based on reports provided by the biblioshiny application, the graphical interface of the bibliometrix R package. The results included a quantitative analysis of scientific production, collaborations, and cocitations, as well as the evolution and thematic map of the field. These revealed an annual increase of 8.43% in publications with acceleration after 2017 and global involvement, with the United States and China in leading positions. Thematic analyses have shown the field's evolution from technological foundations to an interdisciplinary approach, highlighting the influences of global events, such as COVID-19. The research confirmed the profound interaction between AI and CBE, demonstrating its potential, complexity, and the need for collaborative and interdisciplinary approaches. The bibliometric analysis performed can serve as a guide for future research directions and for identifying strategic directions in the implementation of AI in education. Keywords: artificial intelligence (AI), competency-based education (CBE), bibliometric analysis, thematic map, Web of Science (WoS). JEL Classification: I20, I21 Corresponding author, Cătălina Radu – e-mail: [email protected] This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2023 The Author(s). Challenges for Competence-Oriented Education in the Context of the Development of Artificial Intelligence Systems AE Vol. 26 • No. 65 • February 2024 221 Introduction In recent decades, education has undergone significant transformations, moving from a traditional model centred on memorisation and standardised assessments to a more flexible and personalised educational system. Competency-Based Education (CBE) is a contemporary approach that emphasises the development of practical skills, pertinent knowledge, and essential cognitive abilities vital for success in our current world (Johnstone and Soares, 2014). A key shift in this transition has been the integration of artificial intelligence (AI) into the educational process by providing advanced solutions and technologies that improve the efficiency, effectiveness, and relevance of the learning experience. AI refers to the capability of a machine to make decisions in a manner akin to human intelligence (Winkler-Schwartz et al., 2019). Indeed, artificial intelligence has emerged as a rapidly expanding field of research and application, leading to a true revolution in various sectors of modern society (Fosso Wamba et al., 2021). Within the educational realm, AI has become an instrumental tool with extraordinary potential to improve teaching and learning processes, transforming traditional methods into innovative and personalised approaches (Mhlanga, 2021). This paper offers a bibliometric analysis that aims to investigate and highlight the benefits and challenges posed by AI applications in CBE. The motivation for this bibliometric scrutiny stems from a need to comprehend the evolution of research in the field, key themes and pivotal moments, identification of leading authors and primary contributions, research sources, collaborations, emerging research directions, and potential gaps or understudied areas. Recently, various bibliometric analysis studies have been conducted on the relationship between artificial intelligence and education. However, these studies either have a general focus on the AI use in an educational setting (Baek and Doleck, 2020; Talan, 2021; Prahani et al., 2022) or concentrate on applying AI for specific educational levels or types, such as higher education (Hinojo-Lucena et al., 2019), e-learning (Jia et al., 2022), mathematics teaching-learning process (Hwang et al., 2020), or leadership education (Harto et al., 2022). A detailed bibliometric analysis, focused exclusively on AI's use within the CBE context, is still absent. In this context, the present study aims to answer the following research question: How has the specialised literature evolved, and what are the main research trends regarding the intersection of AI and CBE? The results of the analysis can contribute to developing both theoretical and practical domains. In this way, the authors can provide clarity on the current research landscape, identifying gaps and the predominant directions of investigation. From a theoretical perspective, the aim of bibliometric analysis is to highlight areas where researchers have primarily focused their efforts and to present the dynamic development of the theoretical landscape. However, from a practical applied point of view, the study aims to contribute to the advancement of knowledge and the improvement of educational processes, directly impacting the development of key competencies in students. To explore the crucial role that AI plays within the context of competency-based education, this paper is structured into four distinct sections. The first part presents a brief yet comprehensive review of the literature on CBE and AI applications in education, identifying the main trends and challenges in the field, as well as how AI can facilitate the development of key abilities and competencies in education. The second part delves into the research methodology and the results of the bibliometric analysis, a method allowing us to trace the research evolution, and pinpoint significant influences and connections between AE Artificial Intelligence and Competency-Based Education: A Bibliometric Analysis 222 Amfiteatru Economic various topics and concepts related to AI and CBE. The third section is dedicated to discussions, where a detailed analysis of research findings and their implications in the context of previous studies and existing theories is conducted, also addressing the limitations of the study and prospective research directions. In the final part, conclusions are presented, emphasising the primary findings of the research and highlighting the contributions to the domain of education and artificial intelligence, alongside its theoretical and practical implications. 1. Review of the scientific literature 1.1. Competency-Based Education: A Holistic and Balanced Approach to Learning Competency-Based Education (CBE) has emerged as a major topic of interest in educational research and pedagogical practice in recent times (Paek, Um, and Kim, 2021). A competent graduate is one who can apply what they have learnt, thereby operating independently in various real-world practical contexts (Yip and Smales, 2000). At present, CBE is still in the early stages of implementation in certain disciplines, while in others, such as medicine, it already has an extensive history and is supported by numerous studies validating its efficacy (Brammer and Goodrich, 2021). CBE emphasises the development and evaluation of specific competencies rather than focussing solely on theoretical knowledge. The primary goal of CBE is to prepare learners to successfully tackle real-world challenges by focussing on developing skills and aptitudes applicable in various contexts (Johnstone and Soares, 2014). This educational approach is tailored and applicable (Brammer and Goodrich, 2021), fostering the growth of practical skills and competencies essential for individuals to achieve their personal and professional objectives. Unlike traditional education, CBE does not adhere to a standard time framework for class attendance and homework. As a result, students often complete courses or even entire programmes in shorter time frames, which can reduce costs and make education more accessible (Lindsay, 2018; Mehall, 2019; Brammer and Goodrich, 2021). Learning takes place in modules and, given the student-centred approach, learners progress to the next topic or skill once they have sufficiently mastered the associated competencies (Evans, Landl and Thompson, 2020; Brammer and Goodrich, 2021). Research indicates that CBE can increase motivation and engagement in the learning process (Evans, Landl, and Thompson, 2020), and foster critical and analytical thinking skills (Chen, Zhang, and Li, 2022). These benefits suggest that the implementation of a competency-based approach in education can contribute significantly to improving the learning experience and the holistic development of students. The CBE has played a pivotal role in the reform of global education systems. Reform advocates, policymakers, educators, and experts have defined “key competencies” for compulsory education in various ways (Anderson-Levitt and Gardinier, 2021). Numerous countries have adopted or piloted competency-based learning in their curricula, attempting to align education with the demands of the labour market and the requisites of modern society. The implementation of CBE is not without challenges; such an approach can be complex and requires significant resources for course design and the technology used (Burnette, 2016). Competency evaluations can occasionally be subjective and challenging Challenges for Competence-Oriented Education in the Context of the Development of Artificial Intelligence Systems AE Vol. 26 • No. 65 • February 2024 223 to administer objectively. For instance, Gielissen et al. (2022) highlight the issue of the proficiency attained by each learner (typically measured) and the pace of individual progress in learning, both being predictors of future success. A further challenge arises from the time-based structures of tertiary institutions (courses, semesters), and abandoning these is a hard-to-implement innovation (Gruppen et al., 2016). Lastly, it is necessary to emphasise that identifying relevant and necessary competencies in a rapidly changing society is a challenge in itself. 1.2. The Role of Artificial Intelligence in Competency-Based Education Artificial intelligence (AI) is defined as a system's capability to correctly interpret external data, learn from these data, and use this knowledge to accomplish specific tasks and objectives through flexible adaptation (Kaplan and Haenlein, 2019). Consequently, these systems can think and act human-like, employing advanced algorithms and intricate mathematical models to achieve specific aims (Akgun and Greenhow, 2022). For example, ChatGPT represents an artificial intelligence model, specifically, a language generation model developed by OpenAI, employing machine learning techniques to produce text that closely resembles human language. The advent of ChatGPT marks a transformative shift in the landscape of education (Kasneci et al., 2023; Peters et al., 2023). One of AI's remarkable facets in education is its ability to customise individual learning experiences (Chen, Chen, and Lin, 2020). By analysing students' behaviour and progress, AI systems can adjust the curriculum and teaching methodologies to align with individual learning paces and needs (Kuleto et al., 2021; Mhlanga, 2021). This leads to more effective and efficient learning, and students feel more motivated and engaged. Furthermore, Huang (2021) examined how artificial intelligence curriculum design is correlated with the cultivation of key competencies in students. This is particularly important, since academia should carefully assess its capabilities and make a concerted effort to provide the latest knowledge and skills to empower students to face the challenges of future realities (Ellahi, Khan and Shah, 2019). Although sometimes criticised for being less empathetic, AI can recognise emotions, such as a student's frustration (Poria et al., 2019). In CBE, this capability can be pivotal, with recognition unlocking student potential. Adaptive learning systems generate precise student profiles and models based on their emotional states, knowledge levels, individual personality traits, and held competencies (Almohammadi et al., 2017). In addition, AI offers real-time assistance and continuous feedback. Virtual tutors and AI assistants can answer student questions, provide supplementary explanations, and give detailed feedback on academic progress (Hwang et al., 2020). This ensures that students have access to personalised support anytime, helping to consolidate knowledge and develop skills. In addition to student benefits, AI also significantly impacts educators and academic institutions. Teachers can perform various administrative functions, such as grading student assignments through an automated process (Chen, Chen, and Lin, 2020), allowing them to focus more on direct educational activities, human interaction, innovative content creation, and fostering a collaborative learning environment (Akgun and Greenhow, 2022), while evaluations are conducted more efficiently. Additionally, AI assists researchers in analysing large data volumes, discerning trends and patterns, thus facilitating the development of new theories and addressing intricate challenges in various study areas (Kuleto et al., 2021). AE Artificial Intelligence and Competency-Based Education: A Bibliometric Analysis 224 Amfiteatru Economic All these aspects emphasise the significant potential of artificial intelligence to transform education and research. Beyond all these advantages, there are a series of risks and challenges associated with AIbased education. Although AI systems may be more cost effective in the long run than well-paid human resources, their selection by university managers is not necessarily a good choice, as it compromises the personalisation of education (Kaplan and Haenlein, 2019). Ethical concerns, such as data protection and ensuring a secure and fair environment for all students, are ongoing debates (Nguyen et al., 2022). Using AI in education typically involves collecting and analysing extensive personal data, ranging from facial recognition (Borenstein and Howard, 2021) to monitoring systems with detailed information about students' and educators' actions and preferences (Akgun and Greenhow, 2022). AI, being human-created, can inherently possess biases that may disadvantage certain students (Baker and Hawn, 2022). Other potential issues include an overreliance on technology and the possible replacement of teachers with robots (Kanungo et al., 2022). While fostering growth in labour productivity, AI technologies contribute to partial job displacement (Lazaroiu and Rogalska, 2023). Lastly, even though AI boasts accessibility, not all students have access to AI technology (Holmes et al., 2022), potentially widening the gap between the haves and have-nots. However, with ethical and responsible management, AI has the potential to bring about significant enhancements in the educational process and student learning experiences around the world. 2. Research methodology The bibliometric study aimed to identify and analyse the evolution of the specialised literature addressing the intersection between artificial intelligence (AI) and education, with a particular focus on competencies, skills, and efficiency. Specifically, we aim to achieve the following objectives:  Identify the main research trends in the literature addressing the relationship between AI and CBE in terms of the most influential authors, publications, institutions involved, and collaboration networks;  Analyse the evolution of the specialised literature on AI and CBE in terms of both the number of publications per year and the topics addressed. The chosen research method is bibliometric analysis because it allows the synthesis of a large amount of information and the identification of essential research characteristics (Aria and Cuccurullo, 2017). The research process followed the steps below according to the PRISMA approach: Data Collection: A systematic search was conducted in the Web of Science (WoS) database by Topic, using the following keywords: (“artificial intelligence”) AND (“education*”) AND (“competenc*” OR “skill*” OR “proficienc*” OR “capabilit*” OR “abilit*”). The selection of keywords was based on a careful analysis of articles addressing aspects of AI and CBE, which necessitated the use of synonyms for the term “competences” alongside the primary keyword. Other words, such as “robot”, “machine learning”, “chatGPT”, etc., were not included because testing showed that they either led to finding works covered by the term “artificial intelligence” or to papers deviating from the research subject. Challenges for Competence-Oriented Education in the Context of the Development of Artificial Intelligence Systems AE Vol. 26 • No. 65 • February 2024 225 The search was carried out in June 2023 and produced n=1,853 results, to which a series of exclusion and inclusion criteria were applied. The exclusion criterion was the year 2023 to obtain a complete and comparable selection across years, resulting in a total of n=1,606 articles. The inclusion criteria applied were document type (article or review article or early access) (n=1076) and English language (n=1,028). For the year 2023, although excluded from the analysis, 40 works were retained, represented by those included in the database since 2022 (early access articles). The database comprising 1,028 articles was reviewed and corrected for import errors or missing records. Furthermore, all articles were analysed to confirm their eligibility with respect to the treated subject. Data Preprocessing: After the data was collected, the search results were imported into the bibliometrix R-package. This package allows for comprehensive bibliometric analyses compared to other major software tools (Aria and Cuccurullo, 2017). Data Analysis: Biblioshiny, a graphical interface for bibliometrix, was used to analyse the data extracted from WoS. Interpretation and Data Visualisation: The analysis results were interpreted and visualised using various tables, graphs, and diagrams generated by biblioshiny. These helped the study identify trends and synthesise information in an easy-to-understand manner. Before starting the research, various research databases were evaluated to determine the most appropriate data source for the study. Ultimately, exclusive use of the Web of Science (WoS) database was chosen for the following reasons (Azañedo et al., 2022): its broad coverage of research literature from various fields of study, including science, technology, social sciences, and humanities; its high-quality standards ensured the credibility and solid foundation of the analysed studies; advanced search and filtering features, which allowed precise topic-based searches and the application of specific filters, such as document type, language, and year range; compatibility with biblioshiny, the tool utilised for bibliometric analysis. 3. Results and discussion Table 1 centrally presents the records retained for bibliometric analysis. The collected data covers a 32-year period (1991-2022), with 1.028 documents found in 573 different sources. The annual growth rate of publications is 8.43%, indicating a continuous increase in interest in the intersection of AI and education. The documents were written by 3.693 different authors, with 192 of them contributing to single-authored docs. This shows a large diversity of contributions and may indicate an interdisciplinary nature of the field. Of the collected documents, 195 are single-authored documents, and, on average, each document has 3.88 coauthors. 23.54% of the documents have international coauthorship, suggesting significant global collaboration in this research field. Most of the documents are articles (848), followed by reviews (103) and early access articles (43). There are also a smaller number of articles classified as book chapters (18 as part of an article and 1 as part of a review), and articles classified as conference papers (10). AE Artificial Intelligence and Competency-Based Education: A Bibliometric Analysis 226 Amfiteatru Economic Table no. 1. Centralisation of articles extracted from WoS Description Results Description Results Main information about data Authors' Collaboration Timespan 1991:2022 Single-authored docs 195 Sources (Journals, Books, etc.) 573 Co-Authors per Doc 3,88 Documents 1028 International co-authorships % 23,54 Annual Growth Rate % 8,43 Document types Average citations per document 10,18 article 848 References 45048 article; book chapter 18 Document contents article; early access 43 Keywords Plus 1375 article; proceedings paper 10 Author's Keywords 2966 review 103 Authors review; book chapter 1 Authors 3693 review; early access 5 Authors of single-authored docs 192 Source: authors, based on data extracted from WoS. The timeline of publications (Figure 1) shows a clear trend of increasing interest in the intersection of AI and education, as reflected in the growing number of articles published over time. In the early period, from the early 1990s to 2016, the number of publications remained relatively low, with less than 10 articles per year. However, starting in 2017, we see a rapid and consistent increase in the number of publications per year. This indicates the growing recognition of the importance and relevance of the intersection between AI and education in the academic community. Figure no. 1. Evolution of the number of published articles Note: The articles for 2022 also include early access ones. Source: The authors, based on data extracted from WoS In 2017, the number of published articles increased to 24, almost triple from the previous year. This upward trend continued, culminating in a surge in research in the last two years of the analysed period. This rapid growth can be attributed to several factors, including the increasing importance of AI in education, advancements in AI technologies, increased research, development, and investments in this field, and a growing need and demand for AI-related skills and abilities in the educational context. The main research trends indicate Challenges for Competence-Oriented Education in the Context of the Development of Artificial Intelligence Systems AE Vol. 26 • No. 65 • February 2024 227 an exponential growth in interest in the intersection between AI and education, with a notable acceleration of research activity in recent years. Top Scientific Production by Countries The United States leads the ranking with a total of 570 publications, indicating a leadership position in this field (Table 2). This can be attributed to a combination of factors, including advanced technological development, a strong culture of innovation, and significant investments in research and development in the field of artificial intelligence and education. China, with 497 publications, comes very close to the United States, ranking second. This reflects China's rapid rise in the fields of technology and education, backed by significant R&D investments and a national strategy focused on promoting artificial intelligence in education. The United Kingdom and Canada also rank high, with 223 and 164 publications, respectively. These are countries with a strong tradition in the educational field and technological innovation, reflected in the high volume of research produced. Germany, Spain, India, Australia, Italy, France, Saudi Arabia, and Turkey round out the top 10, with publication numbers ranging from 107 in Germany to 50 in Turkey. The presence of these countries highlights the wide geographic distribution of research in this field, with significant contributions from Europe, Asia, North America, Australia, and the Middle East. A wide variety of countries contribute to research in the field of artificial intelligence and education, with the United States and China leading the way. This indicates the global relevance of the topic and the broad involvement of the international academic community. Table no. 2. Top production by country Region Frequency Region Frequency USA 570 India 92 China 497 Australia 85 UK 223 Italy 82 Canada 164 France 72 Germany 107 Saudi Arabia 64 Spain 101 Turkey 50 Source: The authors, using biblioshiny, based on data extracted from WoS The Top Most Relevant Affiliations The ranking of affiliations highlights the academic institutions that lead research in this field, providing a view of the diversity and global distribution of these research efforts (Table 3). The University of Toronto ranks first with 29 articles. It is one of Canada's most prestigious universities, renowned for its strong programmes in technology and education fields. Additionally, it is distinguished by significant investments in research and innovation. McGill University, which ranks second with 25 articles, is also one of the top Canadian institutions. This underlines the importance Canada attaches to the field of AI and education. Stanford University, ranked third with 16 articles, is one of the world's most recognised universities in technology and innovation. Its location in Silicon Valley provides a close link to the tech industry. King Abdulaziz University in Saudi Arabia, with 14 articles, and the Chinese University of Hong Kong, with 13 articles, reflect the growing importance given by non-Western countries to the research in this field. AE Artificial Intelligence and Competency-Based Education: A Bibliometric Analysis 234 Amfiteatru Economic Thematic evolution By comparing the two periods (figure 5), the interest areas are clearly evolving. While the first period focused on fundamental technological developments (AI, machine learning, and robotics), in the second period, we see a blend of technology (AI and E-learning) with major global events (COVID). This reflects the adaptability and interconnectedness of research with global events and developments. Figure no. 5. Thematic evolution during two periods of time Source: The authors, using biblioshiny, based on data extracted from WoS Thematic Map The thematic map is divided into four areas and contains eight clusters, positioned according to density and centrality. Each of these clusters consists of several keywords (Figure 7). It is important to mention that the type of algorithm used for grouping/clustering influences the number and grouping method of clusters in the four zones of the thematic map. In this case, the Fast Greedy algorithm was used. The results show that in the quadrant with the “main themes”, there are two clusters with well-structured themes important for the research field, namely “e-learning, training, ethics” and “higher education, automation, chatbot”. The latter focusses on how technology and AI are integrated into higher education and their impact on the job market and student training. Furthermore, a growing interest in adaptive and personalised learning was also shown by the frequent appearance of terms like “intelligent tutoring systems” and “adaptive educational system”. The “basic themes” are the most important for the research field, but are still underdeveloped. Here, two clusters are included; the largest one (consisting of 31 words) is dominated by terms such as “artificial intelligence, education, machine learning”, while the second one, “technology, covid, medical education”, is dedicated to the application of technology in medical education. Themes associated with keywords like “human-computer interaction” and “information technology” are emerging themes, poorly developed, and marginal compared to the AI and CBE fields. Challenges for Competence-Oriented Education in the Context of the Development of Artificial Intelligence Systems AE Vol. 26 • No. 65 • February 2024 235 Figure no. 6. Thematic map Source: The authors, using biblioshiny, based on data extracted from WoS The research results provide a detailed insight into how AI intersects with the CBE field, which has multiple practical and theoretical implications. An interdisciplinary approach is observed, highlighted by works published in prestigious journals such as Nature, MIS Quarterly, and IEEE Access. This approach emphasises the complexity and vast thematic intersections between AI and CBE. Another relevant aspect is the focus of AI research on medical education, as suggested by the medical journals included in the cocitation network and references to articles in this field. This aligns with the literature that demonstrated the effectiveness of CBE in medicine (Brammer and Goodrich, 2021) and, at the same time, the great potential of AI to revolutionise teaching and learning methods in this field (Chen, Chen, and Lin, 2020). The two highlighted periods underscore the adaptability of research to global events. Therefore, while the first period focused on fundamental technologies, the second placed more emphasis on education and incorporated major events such as the COVID-19 pandemic, also being a period when CBE was studied more intensively (Paek, Um and Kim, 2021). As technology continues to evolve, it is essential to stay in touch with trends to maximise the benefits of AI in education. The results show a wide range of subdomains within AI that are relevant for CBE, from "deep learning" and robotics to adaptive educational systems. Thus, a great potential for AI is observed to contribute to various aspects of the educational process. There is a clear trend towards digitalisation, with terms like “e-learning“, “virtual reality”, and “information technology” being the centre of attention. This could suggest a fundamental shift in how education is delivered and experienced. AE Artificial Intelligence and Competency-Based Education: A Bibliometric Analysis 236 Amfiteatru Economic Another noteworthy aspect is the growing interest in adaptive and personalised learning, highlighted by the frequent appearance of terms related to adaptive educational systems. We observe a movement towards more personalised solutions in education that cater to the individual needs of students; as we know, this is a central trend in CBE (Chen, Chen, and Lin, 2020; Hwang et al., 2020; Brammer and Goodrich, 2021; Kuleto et al., 2021; Mhlanga, 2021). The evolution of education in recent decades, especially the transition from a traditional learning model to a competency-based one, emphasises its adaptability and the ongoing need for innovation in this domain. With a growing emphasis on developing practical skills and relevant knowledge, CBE has been proven to efficiently address realworld challenges. Lastly, we want to stress the importance of this study; while there are previous bibliometric analyses that investigated the link between artificial intelligence and education (HinojoLucena et al., 2019; Baek and Doleck, 2020; Hwang et al., 2020; Talan, 2021; Harto et al., 2022; Jia et al., 2022; Prahani et al., 2022), our research specifically focusses on the use of AI within CBE, thus making a significant contribution to fill a knowledge gap in this domain. By bringing a focused perspective to this specific subject, we hope that the results obtained will shape new insights into how AI can influence and optimise CBE processes in an innovative and practical manner. Conclusions The bibliometric analysis provided in this article reveals an exponential growth in academic interest in the intersection between AI and CBE. With a global presence of research and the involvement of numerous countries and top academic institutions, it is clear that this intersection represents a frontier zone for innovation in education. The increased emphasis on this topic in recent years indicates a rapid adaptation of the academic community to technological advancements and global challenges, such as COVID-19. Thematic analyses highlight the evolution of AI in education, illustrating the shift from focussing on technological fundamentals to interdisciplinary approaches that combine technology with global events and various fields, such as medicine. In addition, collaboration networks underline the value added by cooperation and international knowledge exchange. With respect to the limitations of the research, the Web of Science (WoS) database was used exclusively. Consequently, there is a possibility that relevant works indexed in other databases (such as Scopus) or published in regional or niche journals not included in WoS might have been excluded. Additionally, the exclusive selection of works in the English language can limit the global representation of research in the field of AI and CBE, and the search keywords employed can be deemed restrictive. However, we believe that these limitations had a relatively minor impact and that the conclusions drawn are valid. As the main direction of future research, the authors aim to conduct a quantitative study investigating how the practical implementation of AI in CBE influences educational outcomes and the development of competencies among students. The key competencies considered will be those related to leadership. Therefore, the study will aim to assess the level of development of leadership competencies before and after the implementation of AI Challenges for Competence-Oriented Education in the Context of the Development of Artificial Intelligence Systems AE Vol. 26 • No. 65 • February 2024 237 in CBE, identify significant differences in student academic results, and analyse how technology impacts both academic performance and competency development. From a theoretical standpoint, this work not only fills a gap in the specialised literature but also provides a solid foundation for future research. The intersection between AI and CBE is full of opportunities and challenges. The interdisciplinary approach and global collaboration will be essential to navigate this complex and dynamic research area. From a practical perspective, there are several significant managerial implications for educational institutions and leaders in the field of education. 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