Artificial Intelligence in Adaptive Learning for Education: A Bibliometric Analysis
DOI:
https://doi.org/10.53840/e-jpi.v13i2.413Keywords:
Artificial Intelligence, Adaptive Learning, Educational Technology, Personalized Learning, Bibliometric AnalysisAbstract
Artificial intelligence (AI) has emerged as a transformative technology in education, particularly adaptive learning, which supports personalized and learner-centered experiences. Despite the rapid growth of research on AI for adaptive learning, a comprehensive understanding of its publication performance, thematic structure, and technological evolution remains limited. To overcome this gap, the study aims to systematically map and evaluate the development of research on AI for adaptive learning in education using bibliometric methods. A bibliometric analysis was conducted using data from the Scopus database covering the period from 2015 to 2026. The study analyzed 2,354 publications through two complementary methods: performance analysis and science mapping. Performance analysis was used to evaluate publication growth, influential countries, sources, and highly cited documents. Science mapping techniques, including keyword co-occurrence analysis, were used to identify major research themes and the emerging AI technologies landscape. The findings show that research output has significantly risen after 2023 and recorded the highest number of publications in 2025. India emerged as the most productive country, while the United States had the highest citation impact. Citation analysis highlights adaptive and personalized learning systems, intelligent educational systems, and generative AI applications as key intellectual foundations of the field. Science mapping further revealed that machine learning, intelligent tutoring systems, generative AI, learning analytics, and large language models are central AI technological themes in adaptive learning research. These recent trends indicate a growing shift towards conversational AI, generative AI, and personalized intelligent learning environments. In conclusion, this study provides insights into publication trends, contributors, research themes, and emerging AI technologies in adaptive learning, which could assist researchers, educators, policymakers, and educational technology developers in improving intelligent adaptive learning systems.
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Aggarwal, D. (2023). INTEGRATION OF INNOVATIVE TECHNOLOGICAL DEVELOPMENTS AND AI WITH EDUCATION FOR AN ADAPTIVE LEARNING PEDAGOGY. China Petroleum Processing and Petrochemical Technology, 23(2).
Akavova, A., Temirkhanova, Z., & Lorsanova, Z. (2023). Adaptive learning and artificial intelligence in the educational space. E3S Web of Conferences, 451, 06011. https://doi.org/10.1051/e3sconf/202345106011
Akintola, A. S. (2024). Adaptive AI Systems in Education: Real-Time Personalised Learning Pathways for Skill Development. 4th International Conference on AI ML, Data Science and Robotics, 3–3.
https://doi.org/10.51219/URForum.2024.Akinyemi-Sadeeq-Akintola
Alsbou, M. K. K., & Alsaraireh, R. A. I. (2024). Data-Driven Decision-Making in Education: Leveraging AI for School Improvement. 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS), 1, 1–6. https://doi.org/10.1109/ICKECS61492.2024.10616616
Angelov, P. P. (2002). Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems. Springer Science & Business Media.
Apoki, U. C., Hussein, A. M. A., Al-Chalabi, H. K. M., Badica, C., & Mocanu, M. L. (2022). The Role of Pedagogical Agents in Personalised Adaptive Learning: A Review. Sustainability, 14(11), 6442. https://doi.org/10.3390/su14116442
Chakraborty, S. (2024). Generative AI in Modern Education Society (arXiv:2412.08666). arXiv. https://doi.org/10.48550/arXiv.2412.08666
Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023). Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Sciences, 13(12), 1216. https://doi.org/10.3390/educsci13121216
Guettala, M., Bourekkache, S., Kazar, O., & Harous, S. (2024). Generative Artificial Intelligence in Education: Advancing Adaptive and Personalized Learning. Acta Informatica Pragensia, 13(3), 460–489. https://www.ceeol.com/search/article-detail?id=1258972
Gupta, P., Kulkarni, T., & Toksha, B. (2022). AI-Based Predictive Models for Adaptive Learning Systems. In Artificial Intelligence in Higher Education. CRC Press.
Hariyanto, Francisca Xaveria Diah Kristianingsih, & Rizqona Maharani. (2025). Artificial intelligence in adaptive education: A systematic review of techniques for personalized learning - ProQuest. Discover Education. https://doi.org/10.1007/s44217-025-00908-6
Huang, J., Saleh, S., & Liu, Y. (2021). A Review on Artificial Intelligence in Education. Academic Journal of Interdisciplinary Studies, 10(3), 206. https://doi.org/10.36941/ajis-2021-0077
Joshi, M. A. (2023). Adaptive Learning through Artificial Intelligence (SSRN Scholarly Paper No. 4514887). Social Science Research Network. https://doi.org/10.2139/ssrn.4514887
Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, 100017. https://doi.org/10.1016/j.caeai.2021.100017
Kaswan, K. S., Dhatterwal, J. S., & Ojha, R. P. (2024). AI in personalized learning. In Advances in Technological Innovations in Higher Education. CRC Press.
Kavitha, R. K., Krupa, C. R., & Kaarthiekheyan, V. (2025). AI-Powered Digital Solutions for Smart Learning: Revolutionizing Education. In Cybersecurity and Data Science Innovations for Sustainable Development of HEICC. CRC Press.
Khalid, N., Kasbun, R., & Rahman, N. F. A. (2026). EFFICIENCY, ENGAGEMENT, AND COGNITIVE OFFLOADING: A REVIEW OF GENERATIVE AI IN EDUCATION. INTERNATIONAL JOURNAL OF EDUCATION, PSYCHOLOGY AND COUNSELLING (IJEPC), 11(62), 1238–1258. https://doi.org/10.35631/IJEPC.1162072
Kovalchuk, V., Reva, S., Volch, I., Shcherbyna, S., Mykhailyshyn, H., & Lychova, T. (2025). ARTIFICIAL INTELLIGENCE AS AN EFFECTIVE TOOL FOR PERSONALIZED LEARNING IN MODERN EDUCATION. ENVIRONMENT. TECHNOLOGY. RESOURCES. Proceedings of the International Scientific and Practical Conference, 3, 187–194. https://doi.org/10.17770/etr2025vol3.8534
Kumar, R. (2025). Bibliometric Analysis: Comprehensive Insights into Tools, Techniques, Applications, and Solutions for Research Excellence. Spectrum of Engineering and Management Sciences, 3(1), 45–62. https://doi.org/10.31181/sems31202535k
Kumar, Y., Kumar, S., & Khurana, D. D. (2025). A Study On The Application Of Machine Learning In Adaptive Intelligent Tutoring Systems. International Journal of Environmental Sciences, 11(13).
Merino-Campos, C. (2025). The Impact of Artificial Intelligence on Personalized Learning in Higher Education: A Systematic Review. Trends in Higher Education, 4(2), 17. https://doi.org/10.3390/higheredu4020017
Murtaza, M., Ahmed, Y., Shamsi, J. A., Sherwani, F., & Usman, M. (2022). AI-Based Personalized E-Learning Systems: Issues, Challenges, and Solutions. IEEE Access, 10, 81323–81342. https://doi.org/10.1109/ACCESS.2022.3193938
Pessin, V. Z., Yamane, L. H., & Siman, R. R. (2022). Smart bibliometrics: An integrated method of science mapping and bibliometric analysis. Scientometrics, 127(6), 3695–3718. https://doi.org/10.1007/s11192-022-04406-6
Pham, S. T. H., & Sampson, P. M. (2022). The development of artificial intelligence in education: A review in context. Journal of Computer Assisted Learning, 38(5), 1408–1421. https://doi.org/10.1111/jcal.12687
Plooy, E. du, Casteleijn, D., & Franzsen, D. (2024). Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement. Heliyon, 10(21). https://doi.org/10.1016/j.heliyon.2024.e39630
Raj, N. S., & V G, R. (2019). A Rule-Based Approach for Adaptive Content Recommendation in a Personalized Learning Environment: An Experimental Analysis. 2019 IEEE Tenth International Conference on Technology for Education (T4E), 138–141. https://doi.org/10.1109/T4E.2019.00033
Raji, B., Iyer, S. S., & Yaseen, M. M. N. M. (2026). AI-enabled predictive analytics in education: Enhancing student success and retention through intelligent tutoring systems. Journal of Digital Educational Technology, 6(1), ep2610. https://doi.org/10.29333/jdet/18330
Rasul, T., Nair, S., Kalendra, D., Robin, M., de, O. S. F., Ladeira, W., Sun, M., Day, I., Rather, R. A., & Heathcote, L. (2023). The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning & Teaching, 6(1), 41–56.
https://doi.org/10.3316/informit.T2025102700000390990155131
Rincón-Flores, E. G., Mena, J., López-Camacho, E., & Olmos, O. (2019). Adaptive learning based on AI with predictive algorithms. Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality, TEEM’19, 607–612. https://doi.org/10.1145/3362789.3362869
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