logo logo International Journal of Educational Methodology

IJEM is a leading, peer-reviewed, open access, research journal that provides an online forum for studies in education, by and for scholars and practitioners, worldwide.

Subscribe to

Receive Email Alerts

for special events, calls for papers, and professional development opportunities.

Subscribe

Publisher (HQ)

RHAPSODE
Eurasian Society of Educational Research
College House, 2nd Floor 17 King Edwards Road, Ruislip, London, HA4 7AE, UK
RHAPSODE
Headquarters
College House, 2nd Floor 17 King Edwards Road, Ruislip, London, HA4 7AE, UK
Review Article

Integration of Artificial Intelligence and Machine Learning in Education: A Systematic Review

Manuel Reina-Parrado , Pedro Román-Graván , Carlos Hervás-Gómez

This PRISMA-based systematic review analyzes how artificial intelligence (AI) and Machine Learning (ML) are integrated into educational institutions, .


  • Pub. date: May 15, 2025
  • Online Pub. date: May 12, 2025
  • Pages: 203-216
  • 49 Downloads
  • 283 Views
  • 0 Citations

How to Cite

Abstract:

T

This PRISMA-based systematic review analyzes how artificial intelligence (AI) and Machine Learning (ML) are integrated into educational institutions, examining the challenges and opportunities associated with their adoption. Through a structured selection process, 27 relevant studies published between 2019 and 2023 were analyzed. The results indicate that AI adoption in education remains uneven, with significant barriers such as limited teacher training, technological accessibility gaps, and ethical concerns. However, findings also highlight promising applications, including AI-driven adaptive learning systems, intelligent tutoring, and automated assessment tools that enhance personalized education. The geographical analysis reveals that most research on AI in education originates from North America, Europe, and East Asia, while developing regions remain underrepresented. Without strategic integration, the uneven implementation of AI in education may widen social inequalities, limiting access to innovative learning opportunities for disadvantaged populations. Consequently, this study underscores the urgent need for policies and teacher training programs to ensure equitable AI adoption in education, fostering an inclusive and technologically prepared learning environment.

Keywords: Artificial intelligence, ChatGPT, education, machine learning, teacher training.

description PDF
file_save XML
Article Metrics
Views
49
Download
283
Citations
Crossref
0

References

Alonso-Arévalo, J. (2015). Zotero: Reference managers: Software for the management and maintenance of bibliographic references in research works. Ediciones del Universo.

Barsky, E. (2010). Mendeley. Issues in Science and Technology Librarianship, (62), Article 2541. https://doi.org/10.29173/istl2541

Billingsley, B., Heyes, J. M., Lesworth, T., & Sarzi, M. (2023). Can a robot be a scientist? Developing students' epistemic insight through a lesson exploring the role of human creativity in astronomy. Physics Education, 58, Article 015501. https://doi.org/10.1088/1361-6552/ac9d19

Biurrun, A. (2023). Italia prohíbe la inteligencia artificial ChatGPT de forma temporal. La Razón. https://bit.ly/italia-no-chatgpt

Chang, C.-Y., Hwang, G.-J., & Gau, M.-L. (2022). Promoting students' learning achievement and self-efficacy: A mobile chatbot approach for nursing training. British Journal of Educational Technology, 53, 171-188. https://doi.org/10.1111/bjet.13158

Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25, 3443-3463. https://doi.org/10.1007/s10639-020-10159-7

Chung, D., Jeong, P., Kwan, D., & Han, H. (2023). Technology acceptance prediction of robo-advisors by machine learning. Intelligent Systems with Applications, 18, Article 200197. https://doi.org/10.1016/j.iswa.2023.200197

Cruz-Jesus, F., Castelli, M., Oliveira, T., Mendes, R., Nunes, C., Sa-Velho, M., & Rosa-Louro, A. (2020). Using artificial intelligence methods to assess academic achievement in public high schools of a European Union country. Heliyon, 6(6), Article e04081. https://doi.org/10.1016/j.heliyon.2020.e04081

Druzhinina, O. V., Karpacheva, I. A., Masina, O. N., & Petrov, A. A. (2021). Development of an integrated complex of knowledge base and tools of expert systems for assessing knowledge of students in mathematics within the framework of a hybrid intelligent learning environment. International Journal of Education and Information Technologies, 15, 122-129. https://doi.org/10.46300/9109.2021.15.12

Gilson, A., Safranek, C. W., Huang, T., Socrates, V., Chi, L., Taylor, R. A., & Chartash, D. (2023). How does ChatGPT perform on the United States medical licensing examination? The implications of large language models for medical education and knowledge assessment. JMIR Medical Education, 9, Article e45312. https://doi.org/10.2196/45312

Grunhut, J., Marques, O., & Wyatt, A. T. M. (2022). Needs, challenges, and applications of artificial intelligence in medical education curriculum. JMIR Medical Education, 8(2), Article e35587. https://doi.org/10.2196/35587

Harati, H., Sujo-Montes, L., Tu, C.-H., Armfield, S. J. W., & Yen, C.-J. (2021). Assessment and learning in knowledge spaces (ALEKS) adaptive system impact on students' perception and self-regulated learning skills. Education Sciences, 11(10), Article 603. https://doi.org/10.3390/educsci11100603

Hoosain, M. S., Paul, B. S., & Ramakrishna, S. (2020). The impact of 4IR digital technologies and circular thinking on the United Nations sustainable development goals. Sustainability, 12(23), Article 10143. https://doi.org/10.3390/su122310143

How, M.-L., & Hung, W. L. D. (2019). Educing AI-thinking in science, technology, engineering, arts, and mathematics (STEAM) education. Education Sciences, 9(3), Article 184. https://doi.org/10.3390/educsci9030184

Hutton, B., Catala-Lopez, F., & Moher, D. (2016). The PRISMA statement extension for systematic reviews incorporating network meta-analysis: PRISMA-NMA. Medicina Clínica, 147(6), 262-266. https://doi.org/10.1016/j.medcli.2016.02.025

Jokhan, A., Chand, A. A., Singh, V., & Mamun, K. A. (2022). Increased digital resource consumption in higher educational institutions and the artificial intelligence role in informing decisions related to student performance. Sustainability, 14(4), Article 2377. https://doi.org/10.3390/su14042377

Kadhim, M. K., & Hassan, A. K. (2020). Towards intelligent e-learning systems: A hybrid model for predicting the learning continuity in Iraqi higher education. Webology, 17(2), 172-188. https://doi.org/10.14704/WEB/V17I2/WEB17023

Kanglang, L., & Afzaal, M. (2021). Artificial intelligence (AI) and translation teaching: A critical perspective on the transformation of education. International Journal of Educational Sciences, 33(1-3), 64-73. https://doi.org/10.31901/24566322.2021/33.1-3.1159

Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O. M. D., Păun, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), Article 10424. https://doi.org/10.3390/su131810424

Lampos, V., Mintz, J., & Qu, X. (2021). An artificial intelligence approach for selecting effective teacher communication strategies in autism education. NPJ Science of Learning, 6, Article 25. https://doi.org/10.1038/s41539-021-00102-x

Li, J., Wang, X., Ahmad, S., Huang, X., & Khan, Y. A. (2023). Optimization of investment strategies through machine learning. Heliyon, 9(5), Article e16155. https://doi.org/10.1016/j.heliyon.2023.e16155

Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology, 50(6), 2824-2838. https://doi.org/10.1111/bjet.12861

Marques, L. S., Gresse Von Wangenheim, C., & Hauck, J. C. R. (2020). Teaching machine learning in school: A systematic mapping of the state of the art. Informatics in Education, 19(2), 283-321. https://doi.org/10.15388/INFEDU.2020.14

Muniasamy, A., & Alasiry, A. (2020). Deep learning: The impact on future eLearning. International Journal of Emerging Technologies in Learning, 15(1), 188–199. https://doi.org/10.3991/IJET.V15I01.11435

Murphy-Kelly, S. (2023, March 29). Elon Musk and other tech leaders call for pause in 'out of control' AI race. CNN Business. https://bit.ly/4igmRmX

Nicoletti, M. C., & de Oliveira, O. L. (2020). A machine learning-based computational system proposal aiming at higher education dropout prediction. Higher Education Studies, 10(4), 12-24. https://doi.org/10.5539/hes.v10n4p12

Niyogisubizo, J., Liao, L., Nziyumva, E., Murwanashyaka, E., & Nshimyumukiza, P. C. (2022). Predicting student’s dropout in university classes using two-layer ensemble machine learning approach: A novel stacked generalization. Computers and Education: Artificial Intelligence, 3, Article 100066. https://doi.org/10.1016/j.caeai.2022.100066

Nuankaew, P. (2022). Self-regulated learning model in educational data mining. International Journal of Emerging Technologies in Learning, 17(17), 4-27. https://doi.org/10.3991/ijet.v17i17.23623

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. International Journal of Surgery, 88, Article 105906. https://doi.org/10.1016/j.ijsu.2021.105906

Page, M. J., & Moher, D. (2017). Evaluations of the uptake and impact of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and extensions: A scoping review. Systematic Reviews, 6, Article 263. https://doi.org/10.1186/s13643-017-0663-8

Palasundram, K., Sharef, N. M., Nasharuddin, N. A., Kasmiran, K. A., & Azman, A. (2019). Sequence to sequence model performance for education chatbot. International Journal of Emerging Technologies in Learning, 14(24), 56-68. https://doi.org/10.3991/ijet.v14i24.12187

Prendes-Espinosa, M. P., & Cerdán-Cartagena, F. (2021). Advanced technologies to face the challenge of educational innovation. RIED-Revista Iberoamericana de Educación a Distancia, 24(1), 35-53. https://doi.org/10.5944/ried.24.1.28415

Pu, S., Ahmad, N. A., Khambari, M. N. M., & Yap, N. K. (2021). Identification and analysis of core topics in educational artificial intelligence research: A bibliometric analysis. Cypriot Journal of Educational Sciences, 16(3), 995-1009. https://doi.org/10.18844/cjes.v16i3.5782

Rodríguez-García, J. D., Moreno-León, J., Román-González, M., & Robles, G. (2020). LearningML: A tool to foster computational thinking skills through practical artificial intelligence projects. Revista de Educación a Distancia, 20(63), Article 07. https://doi.org/10.6018/red.410121

Ruipérez-Valiente, J. A., Muñoz-Merino, P. J., Alexandron, G., & Pritchard, D. E. (2019). Using machine learning to detect 'multiple-account' cheating and analyze the influence of student and problem features. IEEE Transactions on Learning Technologies, 12(1), 112-122. https://doi.org/10.1109/TLT.2017.2784420

Salas-Rueda, R. A., Salas-Rueda, E. P., & Salas-Rueda, R. D. (2020). Impact of the web application for the educational process on the compound interest considering data science. Turkish Online Journal of Distance Education, 21(3), 77-93. https://doi.org/10.17718/tojde.762030

Sharma, K., Papamitsiou, Z., & Giannakos, M. (2019). Building pipelines for educational data using AI and multimodal analytics: A "grey-box" approach. British Journal of Educational Technology, 50(6), 3004-3031. https://doi.org/10.1111/bjet.12854

Singh, S. V., & Hiran, K. K. (2022). The impact of AI on teaching and learning in higher education technology. Journal of Higher Education Theory and Practice, 22(13), 135-147. https://doi.org/10.33423/jhetp.v22i13.5514

Stadelmann, T., Keuzenkamp, J., Grabner, H., & Würsch, C. (2021). The AI-Atlas: Didactics for teaching AI and machine learning on-site, online, and hybrid. Education Sciences, 11(7), Article 318. https://doi.org/10.3390/educsci11070318

Su, J., Zhong, Y., & Ng, D. T. K. (2022). A meta-review of literature on educational approaches for teaching AI at the K-12 levels in the Asia-Pacific region. Computers and Education: Artificial Intelligence, 3, Article 100065. https://doi.org/10.1016/j.caeai.2022.100065

Talan, T. (2021). Artificial intelligence in education: A bibliometric study. International Journal of Research in Education and Science, 7(34), 822-837. https://doi.org/10.46328/ijres.2409

Urrútia, G., & Bonfill, X. (2010). PRISMA declaration: A proposal to improve the publication of systematic reviews and meta-analyses. Medicina Clínica, 135(11), 507-511. https://doi.org/10.1016/j.medcli.2010.01.015

Vázquez-Cano, E., Mengual-Andrés, S., & López-Meneses, E. (2021). Chatbot to improve learning punctuation in Spanish and to enhance open and flexible learning environments. International Journal of Educational Technology in Higher Education, 18, Article 33. https://doi.org/10.1186/s41239-021-00269-8

Zammit, M., Voulgari, I., Liapis, A., & Yannakakis, G. N. (2022). Learn to machine learn via games in the classroom. Frontiers in Education, 7, Article 913530. https://doi.org/10.3389/feduc.2022.913530

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0

...