Next Article in Journal
The Impacts of the COVID-19 Traffic Light System on Staff in Tertiary Education in New Zealand
Previous Article in Journal
Exploring the Impact of Different Leadership Styles on Job Satisfaction among Primary School Teachers in the Achaia Region, Greece
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Artificial Intelligence in Higher Education: An Analysis of Existing Bibliometrics

Roberto López-Chila
Joe Llerena-Izquierdo
Nicolás Sumba-Nacipucha
1,2 and
Jorge Cueva-Estrada
GieTICEA Research Group, Universidad Politécnica Salesiana, Guayaquil 010105, Ecuador
Social and Legal Sciences, Universidad Rey Juan Carlos, 28032 Madrid, Spain
Authors to whom correspondence should be addressed.
Educ. Sci. 2024, 14(1), 47;
Submission received: 15 November 2023 / Revised: 10 December 2023 / Accepted: 20 December 2023 / Published: 31 December 2023


Since its origin in the 1950s, artificial intelligence (AI) has evolved from technological to educational applications. AI is emerging as an essential tool in education. Its integration into education promises the personalization and the globalization of learning. Despite its potential, it is crucial to consider its ethical challenges and uses. This bibliometric study sought to understand the current state of AI in higher education in order to provide a basis for future research. A bibliometric analysis was conducted between 2017 and 2023, using the Scopus database. The query was performed on 23 October 2023 and focused on titles, keywords, and abstracts. A total of 870 articles were found, and their metadata were analyzed after removing incorrect data. VOSviewer software was used to visualize the similarities, and the publications were studied by country, authors, and collaborations. A steady growth in AI studies in higher education was found, highlighting areas such as computer science and social sciences. China and the United States led in production and citations. Keywords such as “artificial intelligence”, “chatgpt”, and “machine learning” indicated trends and areas of interest.

1. Introduction

Years ago, when new technologies were mentioned, the focus was on the use of computers and the Internet, tools that undoubtedly contributed significantly to the development of society [1]. On the other hand, artificial intelligence (AI) was not mentioned as a tool for technological discussion. However, the vision has changed. Now, the dialogue revolves around concepts such as digital transformation, digital signature, metaverse, avatars and, of course, artificial intelligence. These developments not only represent significant technological change, but are also transforming dynamics in all contexts [2].
Since the development of artificial intelligence (AI) in the 1950s [3], experts have been constantly searching for programs and applications that could positively change and optimize the different areas in which society carries out its daily activities [4,5]. Although in the beginning, AI was thought of as a tool mainly oriented to technological and industrial fields, over the years, its potential to revolutionize the field of education has become evident. The relationship between AI and education has begun to emerge, not only as a possibility, but also as a necessary evolution in the way knowledge is acquired and imparted [6]. Furthermore, that undoubtedly has had a positive influence on the teaching and learning process [7]. Education, as one of the pillars for the development of societies, has been consistently transformed in order to adapt to the social, cultural, and technological changes of each era [8]. In this context, AI applications have been shown as a strategic partner for the teaching process developed by teachers, allowing them to explore novel scenarios that enhanced their teaching skills for the creation of innovative and personalized experiences, which also improved the attitudes of students towards the learning process [9]. The objective pursued by the study was to describe the current state-of-the-art of scientific production related to artificial intelligence in the context of higher education, through a bibliometric study, with the purpose of showing: trends, important authors, prominent journals, and thematic areas, during the period from 2017 to 2023. This synthesis could provide a strong understanding of the current state-of-the-art and could also guide future research at the intersection of AI and higher education. Section 2 describes the state-of-the-art of the use and the applications of AI in the field of higher education. Section 3 details the methodology used for data collection and subsequent bibliometric analysis, and also describes the source for data collection and the query used. In Section 4, the bibliometric analysis is presented in tables and graphs, highlighting the most-used keywords; the countries and the authors with the highest scientific production; and the research areas in which AI in higher education has been involved. Subsequently, Section 5 contrasts the results described in the previous section with the position and results obtained by other researchers. Finally, Section 6 presents the conclusions reached after analyzing the bibliometric results and the comparison with the results of previous research.

2. State-of-the-Art

Definitions for artificial intelligence (AI) have evolved over time and continue to adapt as technology and research advances. Domingos [10] viewed artificial intelligence as machine learning, which aimed to create an algorithm that could learn from the data provided. Tegmark [11] suggested that AI was an entity of non-biological means that could evolve and adapt to different types of situations, which could, in turn, have a significant impact on society. In the same line, Lee [12] described AI not just as a technology, but also as a force that was redefining economic dynamics globally. He added that AI acted as a technological superpower that could change power structures, with China and Silicon Valley, in the United State, as major players in this revolution. He argued that, in the coming decades, AI would influence economic dynamics, the behavior of societies, and international relations. From another perspective, Marcus and Davis [13] argue that, although impressive progress had been made in AI development, AI had limitations of its own, especially in tasks that required reasoning and an understanding of context. They advocated a vision of AI that not only relied on deep learning, but also added symbolic reasoning, logic, and knowledge of the real world. They added that true artificial intelligence needed a combination of approaches and techniques. Furthermore, Crawford [14] stressed that AI was much more than a technology, highlighting its physical and political impact. He criticized viewing AI as an “intangible”, stressing the importance of taking into account its ethical, socioeconomic, and environmental consequences.
AI has been integrated into almost every part of society. Its ability to process and learn from vast volumes of data has changed the way numerous organizations operate. Moreover, with the emergence of end-user applications, AI is now an everyday tool for workers, professionals and other members of today’s society [15]. An example of this was observed in healthcare, where artificial intelligence, through the use of machine-learning algorithms, has spurred the advancement of clinical research. This has enabled the early detection of disorders such as diabetes and cancer, optimizing the exchange of medical data and benefiting patients [16]. In transportation, the presence of AI manifested with autonomous vehicles and systems that improved routes and optimized fuel consumption, advancements with the potential to improve traffic problems [17]. In retail, personalized recommendations, automated inventory management, and trend analysis have improved the customer experience and optimized business processes [18]. In the financial sector, AI has improved fraud detection and risk management, and with the presence of robotic financial advisors, banking services have been transformed, improving the positioning of these institutions through their users [19]. Another important sector where AI has the potential to have a transformative impact is in education. In this sense, ref. [20] maintained that in Latin America, AI has influenced higher-education institutions, transforming learning and teaching processes. This impact has been decisive for the future of higher education, especially considering that AI has the potential to personalize learning by creating online content while considering the needs and expectations of students. In this regard, the authors [6] added that AI tools offered specific and contextual solutions to optimize the solution of academic problems. In addition, AI trends were driving globalized teaching–learning models, which undoubtedly promoted the socialization of knowledge. However, the authors added that AI, despite its potential, was unlikely to completely replace traditional teaching.
Several authors have argued that education, as an agent of the socialization of knowledge, should take advantage of the benefits of AI in the training context [8] in order to maximize educational achievements. To achieve this purpose, multiple technologies could be applied in the classroom, including virtual reality, augmented reality, AI chats, and video games, among others [18,21]. These enriching applications promoted the creation of more dynamic and disruptive classrooms, such as the design of virtual environments for instant student exploration [22,23]. After observing the points of view of various authors on the relationship between AI and education, it was clear that this technology could revolutionize current methods in this sector. The benefits, the challenges, and the applications of AI in education can be observed clearly in Figure 1. This diagram summarizes the main areas of interaction between AI and higher education, providing a broad view of the topic under analysis.
This diagram shows some of the dimensions in which AI could impact higher education. From the personalization of learning to the development of advanced virtual classrooms, to a more interactive student experience through the development of AI-based end-applications, this technological advancement could transform education through a more adaptive, interactive, and globalized environment. However, it would be necessary to take into account the ethical challenges and uses when integrating this technology into the educational environment.
As AI continues to advance and adapt to the dynamic needs of society, there is a need for the academic community to understand its potential and limitations. Integrating AI into education could also open new avenues for research, collaboration, and innovation. However, as with any emerging technology, it would be essential to address ethical concerns, ensure equity of access, and ensure that the adoption of AI in education reinforces, rather than minimizes, the core values of education. Considering what was stated in the previous paragraph, the authors of [24] maintained that AI had the capacity to transform both teachers’ teaching methods and students’ learning processes, but they highlighted that it was essential to prioritize the primary objective of achieving meaningful and deep learning. Through this bibliometric study, we sought to shed light on the current state of the research in this rapidly and constantly developing area in order to provide a strong foundation for future research and discussion.

3. Materials and Methods

Given the purpose of the research, it was proposed to carry out a bibliometric analysis that included a descriptive examination of publications during a given period and the creation of bibliometric maps while following the guidelines recognized in the scientific field for such research [5,25].
Our research focused on finding articles related to the use of artificial intelligence and its applications in higher education in the Scopus database. The query was conducted on 23 October 2023, considering titles, common keywords, and abstracts, as search criteria, all with the aim of obtaining a clear perspective on the direction of studies related to artificial intelligence in the field of higher education.
The formula and search filters employed in Scopus included the following parameters: TITLE-ABS-KEY (artificial AND intelligence AND higher AND education) AND PUBYEAR > 2016 AND PUBYEAR < 2024 AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (LANGUAGE, “English”) OR LIMIT-TO (LANGUAGE, “Spanish”)) AND (LIMIT-TO (SRCTYPE, “j”)) AND (LIMIT-TO (PUBSTAGE, “final”)). We chose research articles that had been completed and published between 2017 and 2023, in both English and Spanish.
A total of 870 articles were found, and the metadata of these articles were exported in CSV format for further analysis. The data were initially processed in Microsoft Excel to detect and eliminate records with incorrect or incomplete information. Using this program, a preliminary descriptive analysis was carried out. Subsequently, the metadata were analyzed with the Visualization of Similarities VOSviewer version 1.6.19 software [26]. A study of the publications in this field was carried out according to country, authors, and collaborations.
An analysis of research collaborations between nations was developed, taking as requirements to have at least 10 publications and 25 references. For keywords, a minimum of 15 citations was established.

4. Results

Querying the Scopus database for papers linked to artificial intelligence in higher education from 2017 to 2023 yielded a total of 870 articles. Figure 2 presents the annual publications registered in Scopus until 2022, where a growing trend in research in this field of study was evident, according to the current context in which society was developing and where artificial intelligence had emerged strongly, particularly since November 2022, the date on which OpenAI began to release its generative artificial intelligence product, ChatGPT. This subsequently gave way to the emergence of similar technologies, such as Google Bard, and POE, among others.
Artificial intelligence has opened up a wide spectrum of opportunities in almost all areas of societal development, and one such area has been education. In [27], the authors highlighted three approaches to AI in education: (1) chatbots, or intelligent conversational agents; (2) the creation of online platforms for self-learning; and (3) educational robotics. Now, with the boom of artificial intelligence worldwide and the proliferation of applications, web pages, and resources based on this technology, the insertion of AI in classrooms is inevitable, both for the adoption of teachers and students, who sometimes, because of their capabilities and closer relationship with technology as digital natives, are more up-to-date regarding new trends and the use of technological tools in their training processes, which could be used—or not—in an ethical and responsible manner [18,23]. This integration of AI in education must entail a change in the way educational processes have been developed thus far in higher education institutions [6].
Table 1 presents the top 10 research areas with the highest number of published articles on the application of AI in higher education. It should be noted that the sum of the articles for the areas presented exceeded 100% of the articles selected in Scopus (870) because an article could be cataloged in more than one research area.
Table 2 shows the 10 journals with the highest number of articles published on artificial intelligence in higher education. A total of 870 articles were published in about 160 journals. The journal with the largest presence in this field was Sustainability with 29 articles, followed by IEEE Access with 19 articles. The SCImago Journal Rank (SJR) is a metric that reflects the visibility and prestige of scientific journals based on the number of citations received and the importance of the journals where these citations were published. In the context of SJR classified journals, articles were categorized within their field, as well as into four categories (Q1, Q2, Q3, Q4), with Q1 for journals in the top 25% in terms of impact and Q4 for the bottom 25%. The H-index measured both the productivity and the impact of a journal’s publications. It was calculated by counting the number of publications (N) that had received at least N citations. This index helped to assess the relevance and the consistent contribution of a journal in its field.
Table 3 presents the 10 articles with the highest number of citations that address the field of artificial intelligence in higher education. It was noteworthy that these ten articles alone had accumulated 3316 citations.
It was noteworthy that these 10 articles were relatively recent with respect to their publication date (between 3 and 6 years), and that they had already garnered a large number of citations. This phenomenon may have been attributable to the speed with which artificial intelligence applications were being developed in recent months and their availability to end-users in a wide variety of contexts.
The article by [30], with 330 citations, reflected on the future of higher education and noted that it was closely linked to technological advances, especially in AI, which had the potential to reshape the governance and the internal structures of higher education institutions. However, they also agreed that, although AI could change administrative services in universities, in terms of teaching and learning, it presented different challenges that would need to be analyzed and debated among all stakeholders in university communities in order to achieve a consensus on its use and application for strengthening the teaching and learning processes for the benefit of students.
Of the 870 articles examined, 51 countries were identified as participating in international scientific publications in the field of study. Of this group of countries, 27 reached the minimum standard of having at least 10 articles and 25 citations. Table 4 shows the ranking of the 10 leading countries based on the number of publications and citations obtained. The presence of China, the United States, and the United Kingdom, economic and technological powers [38], stood out.
Through a bibliometric map based on published articles, Figure 3 shows the cooperation between the countries participating in this analysis, while Figure 4 visually shows the number of citations per country.
Furthermore, it was determined that the 863 authors participating in the 870 articles analyzed had, at most, 3 articles published in the field of study of this bibliometric study, where 98% of the authors had only one article published in this area of study. Table 5 shows the top 10 authors positioned by number of citations and published papers.
With regard to the keywords defined by the authors of the published scientific articles and in order to know the trends in the research on the use and application of artificial intelligence in higher education, it was found that out of the 2436 keywords identified, only 16 words met the criterion of 15 as the minimum number of occurrences. Table 6 shows the 10 most-mentioned keywords in the papers, and Figure 4 illustrates all the keywords that met the criterion previously described.
Accordingly, Saltos [39] pointed out that there was a trend in the use of technological tools in the educational field, such as artificial intelligence, robotics, and educational neuroscience, with the intention of improving the teaching–learning process and the academic performance of students. Montesdeoca [40] highlighted one application of AI in English language teaching, where artificial intelligence and big data had led to the creation of virtual learning platforms in order to propose innovative models in the teaching of this language within the context of digitization. Another of the keywords frequently mentioned in scientific works was ChatGPT. Ref. [41] mentioned that this tool had a number of advantages with potential in the training field, but for this, teachers would need to be prepared in the use of this tool, so they could improve and strengthen the teaching and learning processes in the post-globalization era.

5. Discussion

The growing and notable presence of artificial intelligence (AI) in the field of higher education has generated increasing academic interest in understanding and analyzing its ramifications. Through meticulous bibliometric analysis, as evidenced by the 1043 articles identified in the Scopus database query, we investigated the progress of the research in this field, discerning trends, patterns, and predominant topic areas. This research contributed to understanding how AI has exerted its influence on instructional and acquisition processes in higher education institutions while contributing to the formulation of innovative pedagogical methodologies and the improvement of academic effectiveness.
In the field of current bibliometrics on artificial intelligence (AI) in higher education, the emphasis has been placed on the importance of assessing not only the quantity but also the quality and relevance of the research. In addition, attention has been given to potential knowledge gaps and areas that have not been thoroughly explored, which could provide opportunities for future research. This thorough review provided a comprehensive overview of the ongoing research and helped to identify the challenges and priority areas required for advancing the successful integration of artificial intelligence in higher education.
This trend was related to the rise of artificial intelligence since the launch of innovative products, such as OpenAI’s ChatGPT in November 2022, which then spurred the emergence of comparable technologies, such as Google Bard and POE. This phenomenon has underscored the importance of critically examining and understanding how artificial intelligence is reshaping the higher education landscape and the implications of these developments on contemporary society.
The comprehensive review of research domains in the use of artificial intelligence (AI) in higher education, as highlighted by the findings, revealed a varied and extensive terrain. The 100% surplus in the total number of selected articles underscored the multi-dimensionality of the research, as a solitary article could span multiple domains. The findings also underlined that the most prominent journals in the dissemination of these studies, namely Sustainability and IEEE Access, featured prominently. The presence of these journals implied a convergence of interests between AI and sustainability, as well as a strong technical emphasis in IEEE Access.
In contrast, the remarkable impact of specific articles was emphasized, with only 10 having accumulated a staggering total of 3316 citations in a relatively short time period of 3–6 years. This phenomenon underscored the urgency and importance of AI research in higher education, with applications being rapidly developed and implemented in a variety of contexts, producing substantial impact and sparking continued interest in the academic community.
The bibliometric research revealed a wide dispersion across different regions, as evidenced by the participation of 51 nations in the analyzed 870 articles. Among these, 27 countries met the requirement of 10 articles and 25 citations, highlighting the dominance of China, the United States, and the United Kingdom in the field of research related to the use of artificial intelligence in higher education.
An analysis of the authors indicated that a majority (98%) of the authors had published exclusively in a unique publication in this field, and a significant concentration was observed among the top 10 authors, both in citations and published papers. Furthermore, an examination of keywords indicated a discernible inclination towards the integration of technologies, such as artificial intelligence, robotics, and educational neuroscience, in order to improve the teaching–learning process. In this context, noteworthy terms, such as ChatGPT and virtual platforms, emerged as key components, accentuating the need to prepare teachers to effectively harness the potential of these tools in the post-globalization era.

6. Conclusions

The bibliometric research conducted in the Scopus database showed a strong growth in scientific production on artificial intelligence in higher education, from 41 publications in 2017 to 245 in 2022, with high growth projections for the future. This quantitative perspective allowed us to clearly observe the interest of the scientific–academic community in studying the relationship between artificial intelligence and higher education, highlighting the acceleration of research in this area as a result of recent technological advances.
The research areas with the highest scientific production on the application of AI in higher education were computer science and social sciences. It was interesting to note how, beyond the technical disciplines, AI had also entered areas such as medicine, psychology and environmental sciences. This conclusion reinforced what had previously been stated about the growth in AI research in higher education, showing its interdisciplinarity and the broad scope of academic fields that have been exploring and adopting these technologies. The classification of articles into multiple areas highlighted the cross-disciplinary nature of AI in current research.
China and the United States have been the leaders in the production of articles on artificial intelligence and also in the ranking of citations, which showed the influence and impact of their research on a global level. Although the United Kingdom ranked third in number of articles, it was surpassed by Saudi Arabia in citations, suggesting that, although Saudi Arabia had produced less, their research may have had a more significant impact. Spain and Australia showed balanced productions and citations, occupying close places in both rankings. It was notable that countries such as India and Saudi Arabia, despite not traditionally being at the forefront of AI research, were emerging as significant players in terms of impact and output.
With regard to the keywords defined by the authors, “artificial intelligence” and “higher education” were at the top of the list, given the central theme of the study. However, it was interesting to note the presence of terms such as “chatgpt”, which reflected the influence of emerging technologies targeting end-users, and “COVID-19”, which suggested research on the interaction between the pandemic and AI-mediated education. Terms such as “machine learning”, “deep learning”, and “big data” highlighted the specific techniques and tools that have been being widely explored in this educational context. These key terms showed a broad view of the prevailing themes and areas of interest in the relationship between AI and higher education.
The researchers recognized the dependence on a single database, in this case Scopus, and a mainly descriptive approach as the limitations of this study. In future research, the authors proposed to undertake an in-depth bibliographic analysis in order to explore the results of the research and methodologies used to measure the specific impact of AI in higher education and to consider the influence of global events and international collaborations.

Author Contributions

Conceptualization, R.L.-C. and J.L.-I.; methodology, N.S.-N.; software, J.C.-E.; validation, R.L.-C., J.L.-I. and N.S.-N.; formal analysis, J.C.-E.; investigation, R.L.-C.; resources, J.L.-I.; data curation, N.S.-N.; writing—original draft preparation, J.C.-E.; writing—review and editing, R.L.-C.; visualization, J.L.-I.; supervision, N.S.-N.; project administration, J.C.-E.; funding acquisition, R.L.-C. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.


  1. Blázquez-Jiménez, C.; Sanchis, J.R. La coopetencia interempresarial. Descripción teórica y aplicación a sectores tecnológicos. RETOS Revista de Ciencias de la Administración y Economía 2023, 13, 325–340. [Google Scholar] [CrossRef]
  2. Torres-Cruz, F.; Yucra-Mamani, Y.J. Técnicas de inteligencia artificial en la valoración de la ense nanza virtual por estudiantes de nivel universitario. Rev. Int. Humanid. 2022, 11, 1–11. [Google Scholar] [CrossRef]
  3. Russell, S.; Norvig, P. Inteligencia Artificial. Un Enfoque Moderno, 2nd ed.; Pearson Prentice Hall: Upper Saddle River, NJ, USA, 2004. [Google Scholar]
  4. Forero-Bautista, A.; Ortegón-Cortázar, L. ¿ Por qué visitar lifestyle centers? Variables alternativas de atracción a través de un modelo de ecuaciones estructurales. RETOS Rev. Cienc. Adm. Econ. 2023, 13, 87–103. [Google Scholar] [CrossRef]
  5. Dávila, M.; Guzmán, R.; Macareno, H.; Piñeres, D.; de la Rosa, D.; Caballero-Uribe, C. Bibliometría: Conceptos y utilidades para el estudio médico y la formación profesional. Salud Uninorte 2009, 25, 319–330. [Google Scholar]
  6. Ocaña-Fernández, Y.; Valenzuela-Fernández, L.A.; Garro-Aburto, L.L. Inteligencia artificial y sus implicaciones en la educación superior. Propos. Represent. 2019, 7, 536–568. [Google Scholar] [CrossRef]
  7. Parra-Sánchez, J.S. Potencialidades de la Inteligencia Artificial en Educación Superior: Un Enfoque desde la Personalización. Rev. Docentes 2.0 2022, 14, 19–27. [Google Scholar] [CrossRef]
  8. Carbonell-García, C.E.; Burgos-Goicochea, S.; Calderón-de-los Ríos, D.O.; Paredes-Fernández, O.W. La Inteligencia Artificial en el contexto de la formación educativa. Epistem. Koin. Rev. Electrón. Cienc. Educ. Humanid. Artes Bellas Artes 2023, 6, 152–166. [Google Scholar] [CrossRef]
  9. Fajardo Aguilar, G.M.; Ayala Gavilanes, D.C.; Arroba Freire, E.M.; López Quincha, M. Inteligencia Artificial y la Educación Universitaria: Una revisión sistemática. Mag. Cienc. Rev. Investig. Innov. 2023, 8, 109–131. [Google Scholar] [CrossRef]
  10. Domingos, P. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World; Basic Books: New York, NY, USA, 2015. [Google Scholar]
  11. Tegmark, M. Life 3.0: Being Human in the Age of Artificial Intelligence; Knopf Publishing Group: New York, NY, USA, 2018. [Google Scholar]
  12. Lee, K.F. AI Superpowers: China, Silicon Valley, and the New World Order; Houghton Mifflin: Boston, MA, USA, 2018. [Google Scholar]
  13. Marcus, G.; Davis, E. Rebooting AI: Building Artificial Intelligence We Can Trust; Knopf Publishing Group: New York, NY, USA, 2019. [Google Scholar]
  14. Crawford, K. The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence; Yale University Press: New Haven, CT, USA, 2021. [Google Scholar]
  15. Salas-Pilco, S.Z.; Yang, Y. Artificial intelligence applications in Latin American higher education: A systematic review. Int. J. Educ. Technol. High. Educ. 2022, 19, 21. [Google Scholar] [CrossRef]
  16. Ahmed, Z.; Mohamed, K.; Zeeshan, S.; Dong, X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database 2020, 2020, baaa010. [Google Scholar] [CrossRef]
  17. Núñez Zorrilla, M.C. Los nuevos avances en la regulación europea de la responsabilidad civil por los daños ocasionados en el ámbito del transporte con inteligencia artificial. Rev. Española Derecho Eur. 2021, 78–79, 201–256. [Google Scholar] [CrossRef]
  18. Martínez-Ortega, A.G.; Medina-Chicaiza, R.P. Tecnologías en la inteligencia artificial para el Marketing: Una revisión de la literatura. Pro Sci. 2020, 4, 36–47. [Google Scholar] [CrossRef]
  19. Ortiz, C.V.J.; Guzmán-Seraquive, J.E. Análisis de las técnicas de machine learning aplicadas en la detección de fraudes bancarios. Cienc. Tecnol. Rev. Cient. Multidiscip. 2022, 22, 10. [Google Scholar] [CrossRef]
  20. Shrivastava, R. Role of Artificial Intelligence in Future of Education. Int. J. Prof. Bus. Rev. Int. J. Prof. Bus. Rev. 2023, 8, 2. Available online: (accessed on 1 December 2023). [CrossRef]
  21. Castrillón, O.D.; Sarache, W.; Ruiz-Herrera, S. Predicción del rendimiento académico por medio de técnicas de inteligencia artificial. Form. Univ. 2020, 13, 93–102. [Google Scholar] [CrossRef]
  22. Barrios-Tao, H.; Díaz, V.; Guerra, Y.M. Propósitos de la educación frente a desarrollos de inteligencia artificial. Cad. Pesqui. 2021, 51, e07767. [Google Scholar] [CrossRef]
  23. Vera, F. Integración de la Inteligencia Artificial en la Educación superior: Desafíos y oportunidades. Transformar 2023, 4, 17–34. [Google Scholar]
  24. Holmes, W.; Bialik, M.; Fadel, C. Artificial Intelligence in Education; Globethics Publications: New York, NY, USA, 2023. [Google Scholar] [CrossRef]
  25. Belinchón Romero, I. La bibliometría como herramienta de conocimiento de la situación de la investigación clínica espa nola en dermatología y sus implicaciones futuras. Actas Dermo-Sifiliogr. 2018, 109, 2. [Google Scholar] [CrossRef]
  26. Centre for Science and Technology Studies. VOSviewer—Visualizing Scientific Landscapes. 2023. Available online: (accessed on 1 December 2023).
  27. Moreno Padilla, R.D. La llegada de la inteligencia artificial a la educación. Rev. Investig. Tecnol. Inf. 2019, 7, 260–270. [Google Scholar] [CrossRef]
  28. Sidiropoulos, N.D.; De Lathauwer, L.; Fu, X.; Huang, K.; Papalexakis, E.E.; Faloutsos, C. Tensor decomposition for signal processing and machine learning. IEEE Trans. Signal Process. 2017, 65, 3551–3582. [Google Scholar] [CrossRef]
  29. Lu, Y.; Yi, S.; Zeng, N.; Liu, Y.; Zhang, Y. Identification of rice diseases using deep convolutional neural networks. Neurocomputing 2017, 267, 378–384. [Google Scholar] [CrossRef]
  30. Popenici, S.A.; Kerr, S. Exploring the impact of artificial intelligence on teaching and learning in higher education. Res. Pract. Technol. Enhanc. Learn. 2017, 12, 22. [Google Scholar] [CrossRef]
  31. Chen, L.; Chen, P.; Lin, Z. Artificial intelligence in education: A review. IEEE Access 2020, 8, 75264–75278. [Google Scholar] [CrossRef]
  32. Wang, D.; He, H.; Liu, D. Adaptive critic nonlinear robust control: A survey. IEEE Trans. Cybern. 2017, 47, 3429–3451. [Google Scholar] [CrossRef]
  33. Zheng, T.; Xie, W.; Xu, L.; He, X.; Zhang, Y.; You, M.; Yang, G.; Chen, Y. A machine learning-based framework to identify type 2 diabetes through electronic health records. Int. J. Med. Inform. 2017, 97, 120–127. [Google Scholar] [CrossRef]
  34. Androutsopoulou, A.; Karacapilidis, N.; Loukis, E.; Charalabidis, Y. Transforming the communication between citizens and government through AI-guided chatbots. Gov. Inf. Q. 2019, 36, 358–367. [Google Scholar] [CrossRef]
  35. Ding, Z.; Shi, H.; Zhang, H.; Meng, L.; Fan, M.; Han, C.; Zhang, K.; Ming, F.; Xie, X.; Liu, H.; et al. Gastroenterologist-level identification of small-bowel diseases and normal variants by capsule endoscopy using a deep-learning model. Gastroenterology 2019, 157, 1044–1054. [Google Scholar] [CrossRef]
  36. Molinillo, S.; Aguilar-Illescas, R.; Anaya-Sánchez, R.; Vallespín-Arán, M. Exploring the impacts of interactions, social presence and emotional engagement on active collaborative learning in a social web-based environment. Comput. Educ. 2018, 123, 41–52. [Google Scholar] [CrossRef]
  37. Tran, B.; Xue, B.; Zhang, M. A new representation in PSO for discretization-based feature selection. IEEE Trans. Cybern. 2017, 48, 1733–1746. [Google Scholar] [CrossRef]
  38. CEPAL NU. Estudio Económico de América Latina y el Caribe 2020: Principales Condicionantes de las Políticas Fiscal y Monetaria en la era Pospandemia de COVID-19; Cepal: Santiago, Chile, 2020; Available online: (accessed on 1 December 2023).
  39. Saltos, G.D.C.; Oyarvide, W.V.; Sánchez, E.A.; Reyes, Y.M. Análisis bibliométrico sobre estudios de la neurociencia, la inteligencia artificial y la robótica: énfasis en las tecnologías disruptivas en educación. Salud Cienc. Tecnol. 2023, 3, 362. [Google Scholar] [CrossRef]
  40. Montesdeoca-Delgado, E.F.; Espinoza-Espinoza, I.R.; De la Torre, T.E.G. Estudio Bibliométrico de la producción científica en SCOPUS: Métodos de ense nanza del inglés en la Educación Superior. Polo Del Conoc. 2022, 7, 1585–1605. [Google Scholar]
  41. Sánchez, O.V.G. Uso y Percepción de ChatGPT en la Educación Superior. Rev. Investig. Tecnol. Inf. 2023, 11, 98–107. [Google Scholar] [CrossRef]
Figure 1. Benefits, vision, challenges, and applications of artificial intelligence.
Figure 1. Benefits, vision, challenges, and applications of artificial intelligence.
Education 14 00047 g001
Figure 2. Scientific publications per year, 2017–2022.
Figure 2. Scientific publications per year, 2017–2022.
Education 14 00047 g002
Figure 3. Global collaboration through documents published by country, based on data obtained from Scopus.
Figure 3. Global collaboration through documents published by country, based on data obtained from Scopus.
Education 14 00047 g003
Figure 4. Keyword network based on data obtained from Scopus.
Figure 4. Keyword network based on data obtained from Scopus.
Education 14 00047 g004
Table 1. Research areas of the analyzed articles.
Table 1. Research areas of the analyzed articles.
Research AreaDocuments
Computer Science373
Social Sciences370
Business, Management and Accounting73
Environmental Science53
Materials Science35
Table 2. Top 10 journals with the highest number of publications on artificial intelligence in higher education.
Table 2. Top 10 journals with the highest number of publications on artificial intelligence in higher education.
2IEEE Access190.93Q1204
3Frontiers in Psychology180.89Q2157
4Mobile Information Systems180.36Q342
5Wireless Communications and Mobile Computing160.45Q273
6Computers and Education Artificial Intelligence150.17Q117
7Education and Information Technologies131.25Q161
8International Journal of Emerging Technologies in Learning130.54Q239
9International Journal Of Educational Technology in Higher Education122.05Q149
10Computational Intelligence and Neuroscience110SQ70
Table 3. Most-cited articles.
Table 3. Most-cited articles.
1IEEE Transactions on Signal ProcessingTensor decomposition for signal processing and machine learning2017919[28]
2NeurocomputingIdentification of rice diseases using deep convolutional neural networks2017625[29]
3Research and Practice in Technology Enhanced LearningExploring the impact of artificial intelligence on teaching and learning in higher education2017330[30]
4IEEE AccessArtificial intelligence in education: A review2020276[31]
5IEEE Transactions on CyberneticsAdaptive critic nonlinear robust control: A survey2017255[32]
6International Journal of Medical InformaticsA machine learning-based framework to identify type-2 diabetes through electronic health records2017243[33]
7Government Information QuarterlyTransforming the communication between citizens and government through AI-guided chatbots2019214[34]
8GastroenterologyGastroenterologist-level identification of small-bowel diseases and normal variants by capsule endoscopy using a deep-learning model2019179[35]
9Computers and EducationExploring the impacts of interactions, social presence and emotional engagement on active collaborative learning in a social web-based environment2018142[36]
10IEEE Transactions on CyberneticsA new representation in PSO for discretization-based feature selection2018133[37]
Table 4. Publications by country and citations by country.
Table 4. Publications by country and citations by country.
2United States1502United States2928
3United Kingdom643Saudi Arabia1086
4India504United Kingdom888
7Saudi Arabia387India494
8South Korea328Turkey342
Table 5. Authors with the highest number of citations and publications related to artificial intelligence in higher education.
Table 5. Authors with the highest number of citations and publications related to artificial intelligence in higher education.
1Fomunyam K.G.36
2Hu Y.; Donald C.; Giacaman N.23
3Jiang B.212
4McCoy C.; Rosenbaum H.215
5Moşteanu N.R.25
6Romero-Rodríguez J.-M.; Ramírez-Montoya M.-S.; Buenestado-Fernández M.; Lara-Lara F.20
7Rudolph J.; Tan S.; Tan S.2157
8Wang Y.20
9Wu J.23
10Xiao M.; Yi H.219
Table 6. Top 10 most used keywords.
Table 6. Top 10 most used keywords.
1Artificial Intelligence381
2Higher Education157
3Machine Learning83
6Deep Learning31
7Active Learning30
10Big Data17
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

López-Chila, R.; Llerena-Izquierdo, J.; Sumba-Nacipucha, N.; Cueva-Estrada, J. Artificial Intelligence in Higher Education: An Analysis of Existing Bibliometrics. Educ. Sci. 2024, 14, 47.

AMA Style

López-Chila R, Llerena-Izquierdo J, Sumba-Nacipucha N, Cueva-Estrada J. Artificial Intelligence in Higher Education: An Analysis of Existing Bibliometrics. Education Sciences. 2024; 14(1):47.

Chicago/Turabian Style

López-Chila, Roberto, Joe Llerena-Izquierdo, Nicolás Sumba-Nacipucha, and Jorge Cueva-Estrada. 2024. "Artificial Intelligence in Higher Education: An Analysis of Existing Bibliometrics" Education Sciences 14, no. 1: 47.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop