Artificial Intelligence in Intelligent Tutoring Systems for Education Literature Review and Bibliometric Analysis Using R-Biblioshiny
DOI:
https://doi.org/10.70152/leotech.v3i1.341Keywords:
artificial intelligence, education, intelligent tutoring systems, literature review, r-biblioshinyArticle Metrics
Abstract
The rapid advancement of Artificial Intelligence (AI) has accelerated the integration of technology into digital learning, particularly through Intelligent Tutoring Systems (ITS) that are capable of adapting instructional content, feedback, and learning pathways to students’ individual needs. The growing volume of publications on AI-based ITS highlights the need for a systematic mapping of the literature to better understand research trends, thematic emphases, and future research directions. This study aims to analyze publication trends, identify influential authors, institutions, journals, and countries, map the conceptual structure of the research field, and uncover research gaps and potential avenues for future studies. A quantitative approach was employed using bibliometric analysis. Data were retrieved from the Scopus database through searches of titles, abstracts, and keywords, and were subsequently screened using the PRISMA flow diagram, resulting in 322 articles published between 2012 and 2026. Bibliometric analysis was conducted using the Bibliometrix package and Biblioshiny to examine publication patterns, citation performance, collaboration networks, and keyword and thematic relationships. The findings indicate a steady increase in publications, with dominant themes centered on AI, intelligent tutoring systems, and adaptive learning. However, studies focusing on pedagogical implementation and long-term learning outcomes remain relatively limited. These results point to significant opportunities for future research, particularly in empirical evaluation and pedagogical integration. Overall, this study provides a comprehensive overview of the development of AI-based ITS research and serves as a valuable reference for researchers and practitioners in designing learning systems that align with educational needs.
Downloads
References
Abulibdeh, A. (2025). A systematic and bibliometric review of artificial intelligence in sustainable education: Current trends and future research directions. Sustainable Futures, 10(2), 1–43. https://doi.org/10.1016/j.sftr.2025.101033
Adayilo, D. M., Oyefolahan, I. O., Ndunagu, J. N., Otuya, C., Malcalm, E., & Twabu, K. (2025). AI-powered tutoring systems for personalized learning feedback in developing secondary education contexts. Journal of Future Artificial Intelligence and Technologies, 2(4), 549–564. https://doi.org/10.62411/faith.3048-3719-287
Akhmadieva, R. S., Kalmazova, N. A., Belova, T., Prokopyev, A., Molodozhnikova, N. M., & Spichak, V. Y. (2024). Research trends in the use of artificial intelligence in higher education. Front.Educ, 9(1), 1–13. https://doi.org/10.3389/feduc.2024.1438715
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
AVŞAR, İ. İ., & PELİT, İ. (2025). Identify globalisation, logistics and port trends using bibliometric mapping: WoS and Scopus data from 1996 to 2025. Humanities and Social Sciences Communications, 12(1), 1–16. https://doi.org/10.1057/s41599-025-05717-8
Böck, F., Ochs, M., Henrich, A., Leidner, J. L., Sedelmaier, Y., & Landes, D. (2025). Learner models: Design , components, structure, and a systematic literature review. User Modeling and User-Adapted Interaction, 35(4), 1–81. https://doi.org/10.1007/s11257-025-09434-4
Büyükkidik, S. (2022). A bibliometric analysis: A tutorial for the bibliometrix package in r using IRT literature. Journal of Measurement and Evaluation in Education and Psychology Research Article, 13(3), 164–193. https://doi.org/10.21031/epod.1069307
Chen, H., Tsang, Y. P., & Wu, C. H. (2023). When text mining meets science mapping in the bibliometric analysis: A review and future opportunities. International Journal of Engineering Business, 15(1), 1–15. https://doi.org/10.1177/18479790231222349
Chrysafiadi, K., Virvou, M., Tsihrintzis, G. A., & Hatzilygeroudis, I. (2023). Evaluating the user’s experience, adaptivity and learning outcomes of a fuzzy‑based intelligent tutoring system for computer programming for academic students in Greece. Education and Information Technologies, 28(6), 6453–6483. https://doi.org/10.1007/s10639-022-11444-3
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Marc, W. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133(April), 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Elnaffar, S., Rashidi, F., & Abualkishik, A. Z. (2026). Teaching with AI: A systematic review of chatbots, generative tools, and tutoring systems in programming education. International Journal of Learning, Teaching and Educational Research, 25(1), 1–28. https://doi.org/10.26803/ijlter.25.1.1
Fernández-Herrero, J. (2024). Evaluating recent advances in affective intelligent tutoring systems: A scoping review of educational impacts and future prospects. Education Sciences, 14(1), 1–35. https://doi.org/ 10.3390/educsci14080839
Fu, Y., Weng, Z., & Wang, J. (2025). Examining AI use in educational contexts: A scoping meta‑review and bibliometric analysis. International Journal of Artificial Intelligence in Education, 35(3), 1388–1444. https://doi.org/10.1007/s40593-024-00442-w
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(1), 1–27. https://doi.org/10.3390/educsci13121216
Haruna, E. U., Asiedu, W. K., & Baek, Y. J. (2024). Mapping the research trends on technological innovation in east asia: A bibliometric analysis using the scopus database for future research direction (1982-2022). Journal of Scientometric Research, 13(3), 3–21. https://doi.org/10.5530/jscires.20041153
Hidayat, M., & Anggreini, D. (2025). The effectiveness of artificial intelligence-based tutoring systems in personalized learning. Education Studies and Teaching Journal (EDUTECH), 2(1), 512–529. https://doi.org/10.62207/pdm6w811
Irwanto, I. (2025). Research trends on artificial intelligence in K-12 education in Asia: A bibliometric analysis using the Scopus database (1996-2025). Discover Artificial Intelligence, 5(1), 1–42. https://doi.org/10.1007/s44163-025-00389-4
Klarin, A. (2024). How to conduct a bibliometric content analysis: Guidelines and contributions of content co-occurrence or co-word literature reviews. International Journal of Consumer Studies, 2(1), 1–20. https://doi.org/10.1111/ijcs.13031
Lachheb, A., Leung, J., Abramenka-Lachheb, V., & Sankaranarayanan, R. (2025). AI in higher education: A bibliometric analysis, synthesis, and a critique of research. The Internet and Higher Education, 67(1), 1–8. https://doi.org/10.1016/j.iheduc.2025.101021
Lai, C., & Lin, C. (2025). Analysis of learning behaviors and outcomes for students with different knowledge levels: A case study of intelligent tutoring system for coding and learning (ITS-CALl). Applied Sciences, 15(1), 1922. https://doi.org/10.3390/app15041922
Lampropoulos, G. (2025). Augmented reality, virtual reality, and intelligent tutoring systems in education and training: A systematic literature review. Applied Sciences, 15(1), 1–23. https://doi.org/10.3390/app15063223
Latif, E., Liu, V., & Zhai, X. (2026). A systematic review of intelligent and robot tutoring systems: evolution, pedagogical design, and AI-driven classification. Smart Learning Environments, 13(1), 1–22. https://doi.org/10.1186/s40561-025-00427-9
Létourneau, A., Martineau, M. D., Charland, P., Karran, J. A., Boasen, J., & Léger, P. M. (2025). A systematic review of AI-driven intelligent tutoring systems (ITS) in K-12 education. Npj Science of Learning, 10(29), 1–13. https://doi.org/10.1038/s41539-025-00320-7
Lim, W. M., & Kumar, S. (2024). Guidelines for interpreting the results of bibliometric analysis: A sensemaking approach. Wiley Online Library, 43(2), 17–26. https://doi.org/10.1002/joe.22229
Lin, C. C., Huang, A. Y. Q., & Lu, O. H. T. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: A systematic review. Smart Learning Environments, 10(41), 1–22. https://doi.org/10.1186/s40561-023-00260-y
Ma, F., Zhu, C., Lei, P., & Yuan, P. (2025). Enhanced learning behaviors and ability knowledge tracing. Applied Sciences, 15(1), 1–16. https://doi.org/10.3390/app15020883
Marquez-Carpintero, L., Lopez-Sellers, A., & Cazorla, M. (2026). Simulation of teaching behaviours in intelligent tutoring systems: A review using large language models. Artificial Intelligence Review, 59(56), 1–36. https://doi.org/10.1007/s10462-025-11464-8
Marzi, G., Balzano, M., & Pellegrini, M. M. (2025). Guidelines for Bibliometric-Systematic Literature Reviews: 10 steps to combine analysis, synthesis and theory development. International Journal Management Reviews, September 2024, 81–103. https://doi.org/10.1111/ijmr.12381
Mondal, H. (2025). A technical note on bibliometric analysis by biblioshiny and VOSviewer. Indian Journal of Radiology and Imaging., 2(1), 1–8. https://doi.org/10.1055/s-0045-1810060
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(2), 4221–4241. https://doi.org/10.1007/s10639-022-11316-w
Nguyen, T. T. K., Nguyen, M. T., & Tran, H. T. (2023). Artificial intelligent based teaching and learning approaches: A comprehensive review. International Journal of Evaluation and Research in Education (IJERE), 12(4), 2387–2400. https://doi.org/10.11591/ijere.v12i4.26623
Nowakowska, M. (2025). A comprehensive approach to preprocessing data for bibliometric analysis. In Scientometrics (Vol. 130, Issue 9). Springer International Publishing. https://doi.org/10.1007/s11192-025-05415-x
Öztürk, O., Kocaman, R., & Kanbach, D. K. (2024). How to design bibliometric research: an overview and a framework proposal. Review of Managerial Science, 18(11), 3333–3361. https://doi.org/10.1007/s11846-024-00738-0
Pelánek, R. (2025). Adaptive learning is hard: challenges, nuances, and trade-offs in modeling. Int J Artif Intell Educ, 35(1), 304–329. https://doi.org/10.1007/s40593-024-00400-6
Pessin, V. Z., Yamane, L. H., & Siman, R. R. (2022). Smart bibliometrics: An integrated method of science mapping and bibliometric analysis. Scientometrics, 4(1), 1–24. https://doi.org/10.1007/s11192-022-04406-6
Prayuda, M. S., Ginting, F. Y. A., & Tamba, L. (2025). Tracing two decades of artificial intelligence in education: A bibliometric analysis of trends, themes, and future directions (2000-2025). European Journal of Educational Research, 15(1), 285–304. https://doi.org/10.12973/eu-jer.15.1.285
Rahman, A., Raj, A., Tomy, P., & Hameed, M. S. (2024). A comprehensive bibliometric and content analysis of artificial intelligence in language learning: Tracing between the years 2017 and 2023. Artificial Intelligence Review, 57(1), 1–27. https://doi.org/10.1007/s10462-023-10643-9
Rethlefsen, M. L., & Page, M. J. (2022). PRISMA 2020 and PRISMA-S: common questions on tracking records and the flow diagram. Journal of the Medical Library Association, 110(2), 253–257. https://doi.org/10.5195/jmla.2022.1449
Romano, G., Schneider, J., Mitri, D. Di, & Drachsler, H. (2025). Through the telescope: A systematic review of intelligent tutoring systems and their applications in psychomotor skill learning. International Journal of Artificial Intelligence in Education, 35(1), 2756–2796. https:doi.org/10.1007/s40593-025-00526-1
Sabeima, M., Lamolle, M., & Nanne, M. F. (2022). Towards Personalized Adaptive Learning in e-Learning Recommender Systems. International Journal of Advanced Computer Science and Applications, 13(8), 14–20. https://doi.org/ 10.14569/IJACSA.2022.0130803
Saltos, W. R. F., Saltos, F. E. F., Alexandra, V. S. E., & Guzmán, E. F. R. (2025). Leveraging artificial intelligence for sustainable tutoring and dropout prevention in higher education: A scoping review on digital transformation. Information, 16(1), 1–24. https://doi.org/10.3390/info16090819
Son, T. (2024). Intelligent tutoring systems in mathematics education: A systematic literature review using the substitution, augmentation, modification, redefinition model. Computers, 13(2), 1–24. https://doi.org/10.3390/computers13100270
Turmuzi, M., & Tyaningsih, R. Y. (2025). A bibliometric analysis of the development of artificial intelligence (AI) research in education in scopus indexed journals: What are the future trends of this research? TEM Journal, 14(1), 671–683. https://doi.org/10.18421/TEM141
Villegas-Ch, W., Fernandez, D. B., Navarro, A. M., & Mera‑Navarrete, A. (2025). Adaptive intelligent tutoring systems for STEM education: analysis of the learning impact and effectiveness of personalized feedback. Smart Learning Environments, 12(41), 1–31. https://doi.org/10.1186/s40561‑025‑00389‑y
Wang, H., Tlili, A., Huang, R., & Cai, Z. (2022). Examining the applications of intelligent tutoring systems in real educational contexts: A systematic literature review from the social experiment perspective. 28(1), 9113–9148. https://doi.org/10.1007/s10639-022-11555-x
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems With Applications, 252(1), 124167. https://doi.org/10.1016/j.eswa.2024.124167
Wu, S., & Yang, K.-K. (2022). The effectiveness of teacher support for students’ learning of artificial intelligence popular science activities. Front. Psycho, 13(1), 1–10. https://doi.org/10.3389/fpsyg.2022.868623
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Raudhatul Haura, Farouq Sessah Mensah, Alaa Hussein Jafar Al-Anbari, Hariharasudan Anandhan, Mustafa Kayyali

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright for this article is held by the authors under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0). The article may be used, shared, and adapted for any purpose with proper attribution and distribution under the same license. Full license details: https://creativecommons.org/licenses/by-sa/4.0/









