Artificial Intelligence in Higher Education: Strategic Foundations, Practices, and Success Factors from a Bibliometric Perspective

Angel Alberto Nava Chirinos

doi:10.64923/ceniiac.e0009

Authors

Angel Alberto Nava Chirinos University of La Guajira https://orcid.org/0000-0002-7960-432X

DOI:

https://doi.org/10.64923/ceniiac.e0009

Keywords:

artificial intelligence, sustainable education, digital transformation, higher education, bibliometric analysis, ChatGPT, learning analytics, institutional strategies, Education 4.0

Abstract

The strategic integration of Artificial Intelligence (AI) in higher education is a global priority, yet conceptual fragmentation persists regarding its effective adoption. This study identifies key drivers of AI adoption through a bibliometric review of 547 Scopus-indexed documents (2019–2024) using thematic mapping in RStudio to visualize topic evolution and density. Findings are organized into three dimensions: (1) essential elements, including institutional infrastructure, governance, and adoption policies; (2) practical recommendations, such as faculty training in generative AI, ethical guidelines, and curriculum integration of digital competencies; and (3) critical success factors, like stakeholder attitudes, technological trust, and institutional leadership. The study offers theoretical, methodological, and practical contributions. Theoretically, it presents a systemic framework aligning infrastructure, practices, and adoption conditions. Methodologically, it validates thematic mapping as a tool for structuring complex literature. Practically, it provides an evidence-based roadmap for institutional leaders, policymakers, and faculty developers to implement sustainable AI initiatives aligned with Education 4.0. Additionally, it highlights research gaps to inform future agendas, especially in underrepresented regions.

Downloads

Download data is not yet available.

References

Abulibdeh, A., Zaidan, E., & Abulibdeh, R. (2024). Navigating the confluence of artificial intelligence and education for sustainable de-velopment in the era of industry 4.0: Challenges, opportunities, and ethical dimensions. Journal of Cleaner Production, 437. https://doi.org/10.1016/j.jclepro.2023.140527

Akbari, M., & Do, T. N. A. (2021). A systematic review of machine learning in logistics and supply chain management: current trends and future directions. Benchmarking, 28(10), 2977–3005. https://doi.org/10.1108/BIJ-10-2020-0514

Alam, A., & Mohanty, A. (2022). Business Models, Business Strategies, and Innovations in EdTech Companies: Integration of Learning Analytics and Artificial Intelligence in Higher Education. 2022 IEEE 6th Conference on Information and Communication Technology, CICT 2022. https://doi.org/10.1109/CICT56698.2022.9997887

Alkadri, S., Ledwos, N., Mirchi, N., Reich, A., Yilmaz, R., Driscoll, M., & Del Maestro, R. F. (2021). Utilizing a multilayer perceptron artificial neural network to assess a virtual reality surgical procedure. Computers in Biology and Medicine, 136. https://doi.org/10.1016/j.compbiomed.2021.104770

Alm, A. (2024). Exploring Autonomy in the AI Wilderness: Learner Challenges and Choices. Education Sciences, 14(12). https://doi.org/10.3390/educsci14121369

Alzahrani, B., Bahaitham, H., & Elshennawy, A. (2025). A validated framework for assessing the maturity level of implementing quality 4.0 in higher education institutions. Journal of Industrial Information Integration, 45. https://doi.org/10.1016/j.jii.2025.100823

Anik, M. H., Raaz, S. N. C., & Khan, N. (2025). Embracing AI Assistants: Unraveling Young Researchers’ Journey with ChatGPT in Science Education Thesis Writing. International Journal of Artificial Intelligence in Education, 35(1), 225–244. https://doi.org/10.1007/s40593-024-00438-6

Aria, M., & Cuccurullo, C. (2017). bibliometrix : An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007

Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377–386. https://doi.org/10.1162/qss_a_00019

Blankenship, R. J. (2021). Deep fakes, fake news, and misinformation in online teaching and learning technologies. In Deep Fakes, Fake News, and Misinformation in Online Teaching and Learning Technologies. https://doi.org/10.4018/978-1-7998-6474-5

Chan, C. K. Y., & Colloton, T. (2024). Generative AI in Higher Education: The ChatGPT Effect. In Generative AI in Higher Education: The ChatGPT Effect. https://doi.org/10.4324/9781003459026

Chan, C. K. Y., & Tsi, L. H. Y. (2024). Will generative AI replace teachers in higher education? A study of teacher and student perceptions. Studies in Educational Evaluation, 83. https://doi.org/10.1016/j.stueduc.2024.101395

Chatikobo, M. V, & Pasipamire, N. (2024). Readiness to embrace artificial intelligence in information literacy instruction at a Zimbabwean University. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2024.2425209

Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00392-8

Dhamija, A., & Dhamija, D. (2025). Understanding Teachers’ Perspectives on ChatGPT-Generated Assignments in Higher Education. Journal of Interdisciplinary Studies in Education, 14(1), 38–62. https://doi.org/10.32674/ptf9yd75

Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070

Duffy, M., James, J., Campbell, C., & Williams, J. C. (2019). Key success factors to implementing an active learning platform. ASCILITE 2017 - Conference Proceedings - 34th International Conference of Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education, 63–67. https://goo.su/mDR1D

Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B. (2024). Deter-minants of Intention to Use ChatGPT for Educational Purposes: Findings from PLS-SEM and fsQCA. International Journal of Human-Computer Interaction, 40(17), 4501–4520. https://doi.org/10.1080/10447318.2023.2226495

Francke, E., & Alexander, B. (2019). The Potential Influence of Artificial Intelligence on Plagiarism: A Higher Education Perspective. European Conference on the Impact of Artificial Intelligence and Robotics, ECIAIR 2019, 131–140. https://doi.org/10.34190/ECIAIR.19.043

Gürdür Broo, D., Kaynak, O., & Sait, S. M. (2022). Rethinking engineering education at the age of industry 5.0. Journal of Industrial Information Integration, 25. https://doi.org/10.1016/j.jii.2021.100311

Jaboob, M., Hazaimeh, M., & Al-Ansi, A. M. (2025). Integration of Generative AI Techniques and Applications in Student Behavior and Cognitive Achievement in Arab Higher Education. International Journal of Human-Computer Interaction, 41(1), 353–366. https://doi.org/10.1080/10447318.2023.2300016

Johnson, A. W., Blackburn, M. W., Su, M. P., & Finelli, C. J. (2019). How a Flexible Classroom Affords Active Learning in Electrical Engineering. IEEE Transactions on Education, 62(2), 91–98. https://doi.org/10.1109/TE.2018.2867447

Karan, B., & Chakma, C. (2025). Influence of higher education students’ perceived behaviour on their artificial intelligence acceptance: an empirical investigation using technology acceptance model. Journal of Applied Research in Higher Education. https://doi.org/10.1108/JARHE-11-2023-0535

Kavitha, K., & Joshith, V. P. (2024). Factors Shaping the Adoption of AI Tools among Gen Z: An Extended UTAUT2 Model Investigation Using CB-SEM. Bulletin of Science, Technology and Society, 44(1–2), 12–32. https://doi.org/10.1177/02704676241283362

Kavitha, K., Joshith, V. P., Rajeev, N. P., & Asha, S. (2024). Artificial Intelligence in Higher Education: A Bibliometric Approach. European Journal of Educational Research, 13(3), 1121–1137. https://doi.org/10.12973/eu-jer.13.3.1121

Lin, J. (2023). ChatGPT and Moodle Walk into a Bar: Capabilities, Integration, Use Cases, and Challenges. 2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023 - Conference Proceedings. https://doi.org/10.1109/TALE56641.2023.10398369

Lünich, M., & Keller, B. (2024). Explainable Artificial Intelligence for Academic Performance Prediction. An Experimental Study on the Impact of Accuracy and Simplicity of Decision Trees on Causability and Fairness Perceptions. 2024 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2024, 1031–1042. https://doi.org/10.1145/3630106.3658953

Lünich, M., Keller, B., & Marcinkowski, F. (2024a). Diverging perceptions of artificial intelligence in higher education: A comparison of student and public assessments on risks and damages of academic performance prediction in Germany. Computers and Education: Artificial Intelligence, 7. https://doi.org/10.1016/j.caeai.2024.100305

Lünich, M., Keller, B., & Marcinkowski, F. (2024b). Fairness of Academic Performance Prediction for the Distribution of Support Measures for Students: Differences in Perceived Fairness of Distributive Justice Norms. Technology, Knowledge and Learning, 29(2), 1079–1107. https://doi.org/10.1007/s10758-023-09698-y

McAllister, J. T., Lennertz, L., & Atencio Mojica, Z. (2022). Mapping A Discipline: A Guide to Using VOSviewer for Bibliometric and Visual Analysis. Science and Technology Libraries, 41(3), 319–348. https://doi.org/10.1080/0194262X.2021.1991547

Medina-Gual, L., & Parejo, J.-L. (2025). Perceptions and Use of AI in Higher Education Students: Impact on Teaching, Learning, and Ethical Considerations. European Journal of Education, 60(1). https://doi.org/10.1111/ejed.12919

Meyer, D. Z., & Avery, L. M. (2009). Excel as a Qualitative Data Analysis Tool. Field Methods, 21(1), 91–112. https://doi.org/10.1177/1525822X08323985

Mhlanga, D. (2023). The Fundamental Strategies that will Drive Higher Educational Sector Towards Digital Transformation in Industry 4.0. Emerging Science Journal, 7(Special issue 2), 125–134. https://doi.org/10.28991/ESJ-2023-SIED2-010

Michel-Villarreal, R., Vilalta-Perdomo, E., Salinas-Navarro, D. E., Thierry-Aguilera, R., & Gerardou, F. S. (2023). Challenges and Op-portunities of Generative AI for Higher Education as Explained by ChatGPT. Education Sciences, 13(9). https://doi.org/10.3390/educsci13090856

Mogul, I., & Shah, S. (2021). Examine the impact of Technology and Industry 4.0 for Student Performance in Higher Education. 2021 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2021. https://doi.org/10.1109/ICTMOD52902.2021.9739579

Montebello, M. (2021). Personalized Learning Environments. Proceedings - 2021 International Symposium on Educational Technology, ISET 2021, 134–138. https://doi.org/10.1109/ISET52350.2021.00036

Moore, S. J., & McCullagh, P. (2024). Sustainability, Ethics and Artificial Intelligence in Computing Education. Proceedings of the 35th Irish Systems and Signals Conference, ISSC 2024. https://doi.org/10.1109/ISSC61953.2024.10603347

Moorhouse, B. L., Wan, Y., Wu, C., Kohnke, L., Ho, T. Y., & Kwong, T. (2024). Developing language teachers’ professional generative AI competence: An intervention study in an initial language teacher education course. System, 125. https://doi.org/10.1016/j.system.2024.103399

Mosch, L. K., Poncette, A.-S., Spies, C., Weber-Carstens, S., Schieler, M., Krampe, H., & Balzer, F. (2022). Creation of an Evi-dence-Based Implementation Framework for Digital Health Technology in the Intensive Care Unit: Qualitative Study. JMIR Form-ative Research, 6(4). https://doi.org/10.2196/22866

Moye, J. N. (2019). A MACHINE LEARNING, ARTIFICIAL INTELLIGENCE APPROACH TO INSTITUTIONAL EFFECTIVENESS IN HIGHER EDUCATION. In A Machine Learning, Artificial Intelligence Approach to Institutional Effectiveness in Higher Education. https://doi.org/10.1108/9781789738995

Mukherjee, D., Lim, W. M., Kumar, S., & Donthu, N. (2022). Guidelines for advancing theory and practice through bibliometric research. Journal of Business Research, 148, 101–115. https://doi.org/10.1016/j.jbusres.2022.04.042

Nationsonline. (2014). Nations Online. Nations Online Project. https://nationsonline.org/oneworld/sitemap.htm

Neyeloff, J. L., Fuchs, S. C., & Moreira, L. B. (2012). Meta-analyses and Forest plots using a microsoft excel spreadsheet: step-by-step guide focusing on descriptive data analysis. BMC Research Notes, 5(1), 52. https://doi.org/10.1186/1756-0500-5-52

Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2024). Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. British Journal of Educational Technology, 55(3), 1082–1104. https://doi.org/10.1111/bjet.13411

Nuthanapati, A. K., Cherukuri, K., & Dukkipati, N. R. (2022). Education Process Re-engineering through Spectral Pyramid Framework to Achieve Excellence in Engineering Education. Journal of Engineering Education Transformations, 35(Special Issue 1), 81–86. https://doi.org/10.16920/jeet/2022/v35is1/22012

Olasiuk, H., Kumar, S., Singh, S., & Ganushchak, T. (2023). Mapping Research Clusters of Artificial Intelligence for Financial Services Using Topic Modelling: A Machine Learning Insight. 2023 Global Conference on Information Technologies and Communications, GCITC 2023. https://doi.org/10.1109/GCITC60406.2023.10426342

Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893–7925. https://doi.org/10.1007/s10639-022-10925-9

Pandey, N., Andres, C., & Kumar, S. (2023). Mapping the corporate governance scholarship: Current state and future directions. Cor-porate Governance: An International Review, 31(1), 127–160. https://doi.org/10.1111/corg.12444

Popkova, E. G., & Gulzat, K. (2020). Technological Revolution in the 21st Century: Digital Society vs. Artificial Intelligence. In Lecture Notes in Networks and Systems (Vol. 91, pp. 339–345). https://doi.org/10.1007/978-3-030-32015-7_38

Pranckutė, R. (2021). Web of Science (WoS) and Scopus: the titans of bibliographic information in today’s academic world. Publications, 9(1). https://doi.org/10.3390/publications9010012

Ranjan, S., Joshith, V. P., Kavitha, K., & Chittakath, S. (2024). The AI-knowledge management nexus for sustainable learning: A PLS-SEM study. Knowledge Management and E-Learning, 16(4), 811–837. https://doi.org/10.34105/j.kmel.2024.16.037

Rodríguez-Hernández, C. F., Musso, M., Kyndt, E., & Cascallar, E. (2021). Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation. Computers and Education: Artificial Intelligence, 2. https://doi.org/10.1016/j.caeai.2021.100018

Rodriguez-Ruiz, J., Alvarez-Delgado, A., & Caratozzolo, P. (2021). Use of Natural Language Processing (NLP) Tools to Assess Digital Literacy Skills. Future of Educational Innovation Workshop Series - Machine Learning-Driven Digital Technologies for Educational Innovation Workshop 2021. https://doi.org/10.1109/IEEECONF53024.2021.9733779

Sallam, M., Salim, N. A., Barakat, M., & Al-Tammemi, A. B. (2023). ChatGPT applications in medical, dental, pharmacy, and public health education: A descriptive study highlighting the advantages and limitations. Narra J, 3(1). https://doi.org/10.52225/narra.v3i1.103

Sequeira, N., Reis, A., Branco, F., & Alves, P. (2023). Roadmap for Implementing Business Intelligence Systems in Higher Education Institutions: Exploratory Work. ICSBT International Conference on Smart Business Technologies, 2023-July, 162–169. https://doi.org/10.5220/0012118000003552

Shi, J., & Wang, Y. (2024). Prerequisites for the Innovation Performance of Artificial Intelligence Laboratory: A Fuzzy-Set Qualitative Comparative Analysis. IEEE Transactions on Engineering Management, 71, 5341–5356. https://doi.org/10.1109/TEM.2024.3355235

Song, X., Xu, H., Wang, X., Liu, W., Leng, X., Hu, Y., Luo, Z., Chen, Y., Dong, C., & Ma, B. (2024). Use of ultrasound imaging Omics in predicting molecular typing and assessing the risk of postoperative recurrence in breast cancer. BMC Women’s Health, 24(1). https://doi.org/10.1186/s12905-024-03231-8

Subaeki, B., Irfan, M., Adipradana, R. S., Alam, C. N., & Ramdhani, M. A. (2019). Decision support system design of higher education scholarship recipients with android-based. Journal of Physics: Conference Series, 1280(2). https://doi.org/10.1088/1742-6596/1280/2/022016

Tariq, M. U. (2024a). AI-driven learning and engagement in higher education. In AI-Driven Learning and Engagement in Higher Education. https://doi.org/10.4018/979-8-3693-4074-5

Tariq, M. U. (2024b). Generative AI in curriculum development in higher education. In Impacts of Generative AI on Creativity in Higher Education (pp. 227–258). https://doi.org/10.4018/979-8-3693-2418-9.ch009

Tariq, M. U. (2024c). Navigating the personalization pathway: Implementing adaptive learning technologies in higher education. In Adaptive Learning Technologies for Higher Education (pp. 265–291). https://doi.org/10.4018/979-8-3693-3641-0.ch012

Thornhill-Miller, B., Camarda, A., Mercier, M., Burkhardt, J.-M., Morisseau, T., Bourgeois-Bougrine, S., Vinchon, F., El Hayek, S., Augereau-Landais, M., Mourey, F., Feybesse, C., Sundquist, D., & Lubart, T. (2023). Creativity, Critical Thinking, Communication, and Collaboration: Assessment, Certification, and Promotion of 21st Century Skills for the Future of Work and Education. Journal of Intelligence, 11(3). https://doi.org/10.3390/jintelligence11030054

Todeschini, R., & Baccini, A. (2016). Handbook of Bibliometric Indicators: Quantitative Tools for Studying and Evaluating Research. https://doi.org/10.1002/9783527681969

Tominc, P., & Rožman, M. (2023). Artificial Intelligence and Business Studies: Study Cycle Differences Regarding the Perceptions of the Key Future Competences. Education Sciences, 13(6). https://doi.org/10.3390/educsci13060580

Tsekea, S., & Mandoga, E. (2024). The ethics of artificial intelligence use in university libraries in Zimbabwe. Frontiers in Research Metrics and Analytics, 9. https://doi.org/10.3389/frma.2024.1522423

Uddin, S. M. J., Albert, A., Ovid, A., & Alsharef, A. (2023). Leveraging ChatGPT to Aid Construction Hazard Recognition and Support Safety Education and Training. Sustainability (Switzerland), 15(9). https://doi.org/10.3390/su15097121

Ulas, D. (2019). Digital Transformation Process and SMEs. Procedia Computer Science, 158, 662–671. https://doi.org/10.1016/j.procs.2019.09.101

van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3

Villagrán, I. (2021). Exploring the Effects of Applying Learning Analytics for Teaching Procedural Skills in Health Sciences Education. Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW, 299–302. https://doi.org/10.1145/3462204.3481799

Wang, C., Wang, H., Li, Y., Dai, J., Gu, X., & Yu, T. (2024). Factors Influencing University Students’ Behavioral Intention to Use Gen-erative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy. International Journal of Human-Computer Interaction. https://doi.org/10.1080/10447318.2024.2383033

Wang, M., Sun, Z., Jia, M., Wang, Y., Wang, H., Zhu, X., Chen, L., & Ji, H. (2022). Intelligent virtual case learning system based on real medical records and natural language processing. BMC Medical Informatics and Decision Making, 22(1). https://doi.org/10.1186/s12911-022-01797-7

Wu, H., Liu, W., & Zeng, Y. (2024). Validating the AI-assisted second language (L2) learning attitude scale for Chinese college students and its correlation with L2 proficiency. Acta Psychologica, 248. https://doi.org/10.1016/j.actpsy.2024.104376

Yang, J., Qiu, H., & Yu, W. (2024). Surging currents: a systematic review of the literature on dynamic stakeholder engagements in higher education in the generative artificial intelligence era. Journal of Asian Public Policy. https://doi.org/10.1080/17516234.2024.2429046

Yang, L., García-Holgado, A., & Martínez-Abad, F. (2023). Digital competence of K-12 pre-service and in-service teachers in China: a systematic literature review. Asia Pacific Education Review, 24(4), 679–693. https://doi.org/10.1007/s12564-023-09888-4

Yaqin, L. N., Bilad, M. R., Yusof, B., Hassan, H., Damit, A. R., & Chuchu, I. F. (2025). Mapping research evolution in higher education: a scientometric analysis of Brunei Darussalam (1986–2024). Discover Sustainability, 6(1). https://doi.org/10.1007/s43621-025-00917-3

Zipf, S., Petricini, T., & Wu, C. (2024). AI Monsters: An application to student and faculty knowledge and perceptions of generative AI. In The Role of Generative AI in the Communication Classroom (pp. 284–299). https://doi.org/10.4018/979-8-3693-0831-8.ch013

Zupic, I., & Čater, T. (2015). Bibliometric Methods in Management and Organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629