Techniques and applications of Machine Learning and Artificial Intelligence in education: a systematic review

Authors

DOI:

https://doi.org/10.5944/ried.27.1.37491

Keywords:

machine learning, artificial intelligence, educational innovation, emerging technology, educational revolution

Abstract

Machine learning is a field of artificial intelligence that is impacting lately in all areas of knowledge. The areas of social sciences, especially education, are no stranger to it, so, a systematic review of the literature on the techniques and applications of machine learning and artificial intelligence in Education is performed. The lack of knowledge and skills of educators in machine learning and artificial intelligence limits the optimal implementation of these technologies in education. The objective of this research is to identify opportunities for improving teaching-learning processes and educational management at all levels of the educational context through the application of machine learning and artificial intelligence. The databases used for the bibliographic search were Web of Science and Scopus and the methodology applied is based on the PRISMA statement for obtaining and analyzing 55 articles published in high impact journals between the years 2021-2023. The results showed that the studies addressed a total of 33 machine learning and artificial intelligence techniques and multiple applications that were implemented in educational contexts at primary, secondary and higher education levels in 38 countries. The conclusions showed the strong impact of the use of machine learning and artificial intelligence. This impact is reflected in the use of different intelligent techniques in educational contexts and the increase of research in secondary schools on artificial intelligence.

FULL ARTICLE:
https://revistas.uned.es/index.php/ried/article/view/37491/28245

Downloads

Download data is not yet available.

Author Biographies

Wiston Forero-Corba, Universitat de les Illes Balears, UIB (Spain)

PhD Candidate in Educational Technology at the University of Balearic Islands (UIB) in field of machine learning and artificial intelligence for education. He obtained a MSc in Computing Engineering at the Public University of Navarra (UPNA) and Specialist in the Application of ICT for Education at the University of Santander. He completed a BS in Computing and Systems Engineering at the National University of Colombia (UNAL) and BS in Physics in District University F.J.C. (UD). His lines of research are computer science, artificial intelligence, machine learning, education, programming, STEM, and other related fields.

Francisca Negre Bennasar, Universitat de les Illes Balears, UIB (Spain)

Doctor in Educational Sciences, MSc in Educational Technology. Professor of the Department of Applied Pedagogy and Educational Psychology of the University of the Balearic Islands (UIB). Researcher of the Educational Technology Group (GTE) of the UIB. Deputy Director of the Laboratory of Hospital Pedagogy (InèditLab) and secretary of the Unit of video games and artificial intelligence (UVJIA). Her lines of research focus on digital technologies applied to education in general and in the field of people with special educational needs and Hospital Pedagogy.

References

Ahajjam, T., Moutaib, M., Aissa, H., Azrour, M., Farhaoui, Y., & Fattah, M. (2022). Predicting Students’ Final Performance Using Artificial Neural Networks. Big Data Mining and Analytics, 5(4), 294-301. https://doi.org/10.26599/BDMA.2021.9020030

Ahmed, A., Aziz, S., Qidwai, U., Farooq, F., Shan, J., Subramanian, M., Chouchane, L., EINatour, R., Abd-Alrazaq, A., Pandas, S., & Sheikh, J. (2023). Wearable Artificial Intelligence for Assessing Physical Activity in High School Children. Sustainability (Switzerland), 15(1), 1-12. https://doi.org/10.3390/su15010638

Ali, S., DiPaola, D., Lee, I., Sindato, V., Kim, G., Blumofe, R., & Breazeal, C. (2021). Children as creators, thinkers and citizens in an AI-driven future. Computers and Education: Artificial Intelligence, 2, 100040. https://doi.org/10.1016/j.caeai.2021.100040

Ali, S., DiPaola, D., Lee, I., Hong, J., & Breazeal, C. (2021). Exploring generative models with middle school students. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3411764.3445226

Aljabri, M., Chrouf, S. M. B., Alzahrani, N. A., Alghamdi, L., Alfehaid, R., Alqarawi, R., Alhuthayfi, J., & Alduhailan, N. (2021). Sentiment analysis of arabic tweets regarding distance learning in saudi arabia during the covid-19 pandemic. Sensors, 21(16). https://doi.org/10.3390/s21165431

Almeida Pereira Abar, C. A., Dos Santos Dos Santos, J. M., & de Almeida, M. V. (2021). Computational Thinking in Elementary School in the Age of Artificial Intelligence: Where is the Teacher? Revista de Ensino de Ciencias y Matemática, 23(6), 270-299. https://doi.org/10.17648/acta.scientiae.6869

Almoqbil, A., O’Connor, B. C., Anderson, R., Shittu, J., & McLeod, P. (2021). Modeling deception: A case study of email phishing. Proceedings from the Document Academy, 8(2). https://doi.org/10.35492/docam/8/2/8

Alshaikh, K., Bahurmuz, N., Torabah, O., Alzahrani, S., Alshingiti, Z., & Meccawy, M. (2021). Using Recommender Systems for Matching Students with Suitable Specialization: An Exploratory Study at King Abdulaziz University. International Journal of Emerging Technologies in Learning, 16(3), 316-324. https://doi.org/10.3991/ijet.v16i03.17829

Alvarado Uribe, J., Mejía Almada, P., Masetto Herrera, A. L., Molontay, R., Hilliger, I., Hegde, V., Montemayor Gallegos, J. E., Ramírez Díaz, R. A., & Ceballos, H. G. (2022). Student Dataset from Tecnologico de Monterrey in Mexico to Predict Dropout in Higher Education. Data, 7(9). https://doi.org/10.3390/data7090119

An, X., Chai, C. S., Li, Y., Zhou, Y., Shen, X., Zheng, C., & Chen, M. (2022). Modeling English teachers’ behavioral intention to use artificial intelligence in middle schools. Education and Information Technologies. https://doi.org/10.1007/s10639-022-11286-z

Angara, P. P., Stege, U., MacLean, A., Muller, H. A., & Markham, T. (2022). Teaching Quantum Computing to High-School-Aged Youth: A Hands-On Approach. IEEE Transactions on Quantum Engineering, 3. https://doi.org/10.1109/TQE.2021.3127503

Araya, R., & Sossa-Rivera, J. (2021). Automatic Detection of Gaze and Body Orientation in Elementary School Classrooms. Frontiers in Robotics and AI, 8(September), 1-11. https://doi.org/10.3389/frobt.2021.729832

Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3(August), 100099. https://doi.org/10.1016/j.caeai.2022.100099

Baashar, Y., Hamed, Y., Alkawsi, G., Fernando Capretz, L., Alhussian, H., Alwadain, A., & Al-amri, R. (2022). Evaluation of postgraduate academic performance using artificial intelligence models. Alexandria Engineering Journal, 61(12), 9867-9878. https://doi.org/10.1016/j.aej.2022.03.021

Bakker, T., Krabbendam, L., Bhulai, S., Meeter, M., & Begeer, S. (2023). Predicting academic success of autistic students in higher education. Autism. https://doi.org/10.1177/13623613221146439

Ban, H., & Ning, J. (2021). Online English Teaching Based on Artificial Intelligence Internet Technology Embedded System. Mobile Information Systems, 2021. https://doi.org/10.1155/2021/2593656

Bellas, F., Guerreiro-Santalla, S., Naya, M., & Duro, R. J. (2022). AI Curriculum for European High Schools: An Embedded Intelligence Approach. International Journal of Artificial Intelligence in Education, 0123456789. https://doi.org/10.1007/s40593-022-00315-0

Bhavana, S., & Vijayalakshmi, V. (2022). AI-Based Metaverse Technologies Advancement Impact on Higher Education Learners. WSEAS Transactions on Systems, 21, 178-184. https://doi.org/10.37394/23202.2022.21.19

Blease, C., Kharko, A., Annoni, M., Gaab, J., & Locher, C. (2021). Machine Learning in Clinical Psychology and Psychotherapy Education: A Mixed Methods Pilot Survey of Postgraduate Students at a Swiss University. Frontiers in Public Health, 9(April). https://doi.org/10.3389/fpubh.2021.623088

Bogina, V., Hartman, A., Kuflik, T., & Shulner-Tal, A. (2022). Educating Software and AI Stakeholders About Algorithmic Fairness, Accountability, Transparency and Ethics. International Journal of Artificial Intelligence in Education, 32(3), 808-833. https://doi.org/10.1007/s40593-021-00248-0

Bosch, N. (2021). Identifying supportive student factors for mindset interventions: A two-model machine learning approach. Computers and Education, 167(March), 104190. https://doi.org/10.1016/j.compedu.2021.104190

Bruno, G. di D. (2021). Erwhi Hedgehog: A New Learning Platform for Mobile Robotics. In Lecture Notes in Networks and Systems (Vol. 240). Springer International Publishing. https://doi.org/10.1007/978-3-030-77040-2_32

Burgess, S., Metcalfe, R., & Sadoff, S. (2021). Understanding the response to financial and non-financial incentives in education: Field experimental evidence using high-stakes assessments. Economics of Education Review, 85(July), 102195. https://doi.org/10.1016/j.econedurev.2021.102195

Byun, A., & Kim, H. (2022). The Effect of Design Classes Using Artificial Intelligence in the Era of COVID-19 on Social Responsibility of High School Students. Archives of Design Research, 35(4), 251-266. https://doi.org/10.15187/adr.2022.11.35.4.251

Ceha, J., Law, E., Kulić, D., Oudeyer, P. Y., & Roy, D. (2022). Identifying Functions and Behaviours of Social Robots for In-Class Learning Activities: Teachers’ Perspective. International Journal of Social Robotics, 14(3), 747-761. https://doi.org/10.1007/s12369-021-00820-7

Chen, B., Chen, H., & Li, M. (2021). Improvement and Optimization of Feature Selection Algorithm in Swarm Intelligence Algorithm Based on Complexity. Complexity, 2021. https://doi.org/10.1155/2021/9985185

Cheng, J., Chae, M. H. C., & Feng, R. (2021). Stem education-career pathway for emerging forensic analytics: Innovative professional development in multimodal environments. Journal of Higher Education Theory and Practice, 21(8), 115-130. https://doi.org/10.33423/jhetp.v21i8.4509

Chrysafiadi, K., Virvou, M., Tsihrintzis, G. A., & Hatzilygeroudis, I. (2022). Evaluating the user’s experience, adaptivity and learning outcomes of a fuzzy-based intelligent tutoring system for computer programming for academic students in Greece. In Education and Information Technologies (Issue 0123456789). Springer US. https://doi.org/10.1007/s10639-022-11444-3

Costa Mendes, R., Oliveira, T., Castelli, M., & Cruz Jesus, F. (2021). A machine learning approximation of the 2015 Portuguese high school student grades: A hybrid approach. Education and Information Technologies, 26(2), 1527-1547. https://doi.org/10.1007/s10639-020-10316-y

Dai, Y., Liu, A., Qin, J., Guo, Y., Jong, M. S. Y., Chai, C. S., & Lin, Z. (2022). Collaborative construction of artificial intelligence curriculum in primary schools. Journal of Engineering Education, October 2022, 23-42. https://doi.org/10.1002/jee.20503

Demchenko, M. V., Gulieva, M. E., Larina, T. V., & Simaeva, E. P. (2021). Digital Transformation of Legal Education: Problems, Risks and Prospects. European Journal of Contemporary Education, 10(2), 297-307. https://doi.org/10.13187/ejced.2021.2.297

Demir, K., & Güraksın, G. E. (2022). Determining middle school students’ perceptions of the concept of artificial intelligence: A metaphor analysis. Participatory Educational Research, 9(2), 297-312. https://doi.org/10.17275/per.22.41.9.2

Dietz, G., Chen, J. K., Beason, J., Tarrow, M., Hilliard, A., & Shapiro, R. B. (2022). ARtonomous: Introducing Middle School Students to Reinforcement Learning Through Virtual Robotics. Proceedings of Interaction Design and Children, IDC 2022, 430-441. https://doi.org/10.1145/3501712.3529736

Dogadina, E. P., Smirnov, M. V., Osipov, A. V., & Suvorov, S. V. (2021). Formation of the optimal load of high school students using a genetic algorithm and a neural network. Applied Sciences (Switzerland), 11(11). https://doi.org/10.3390/app11115263

Duncan, D., Garner, R., Bennett, A., Sinclair, M., Ramirez-De La Cruz, G., & Pasik-Duncan, B. (2022). Interdisciplinary K-12 Control Education in Biomedical and Public Health Applications. IFAC-PapersOnLine, 55(17), 242-248. https://doi.org/10.1016/j.ifacol.2022.09.286

Duzhin, F., & Gustafsson, A. (2018). Machine learning-based app for self-evaluation of teacher-specific instructional style and tools. Education Sciences, 8(1), 1-15. https://doi.org/10.3390/educsci8010007

Eegdeman, I., Cornelisz, I., van Klaveren, C., & Meeter, M. (2022). Computer or teacher: Who predicts dropout best? Frontiers in Education, 7(November), 1-10. https://doi.org/10.3389/feduc.2022.976922

Eguchi, A., Okada, H., & Muto, Y. (2021). Contextualizing AI Education for K-12 Students to Enhance Their Learning of AI Literacy Through Culturally Responsive Approaches. KI - Kunstliche Intelligenz, 35(2), 153-161. https://doi.org/10.1007/s13218-021-00737-3

Fernández-Martínez, C., Hernán-Losada, I., & Fernández, A. (2021). Early Introduction of AI in Spanish Middle Schools. A Motivational Study. KI - Kunstliche Intelligenz, 35(2), 163-170. https://doi.org/10.1007/s13218-021-00735-5

Forero Corba, W., & Negre Bennasar, F. (2022). Revisión sistemática de la aplicación del Machine Learning en la Educación. EDUTEC Educación transformadora en un mundo digital: Conectando paisajes de aprendizaje, 416-419.

http://dspace.uib.es/xmlui/handle/11201/160593

Gerlache, H. A. M., Ger, P. M., & Valentín, L. de la F. (2022). Towards the Grade’s Prediction. A Study of Different Machine Learning Approaches to Predict Grades from Student Interaction Data. International Journal of Interactive Multimedia and Artificial Intelligence, 7(4), 196-204. https://doi.org/10.9781/ijimai.2021.11.007

Giam, N. M., Nam, N. T. H., & Giang, N. T. H. (2022). Situation and Proposals for Implementing Artificial Intelligence-based Instructional Technology in Vietnamese Secondary Schools. International Journal of Emerging Technologies in Learning, 17(18), 53-75. https://doi.org/10.3991/ijet.v17i18.31503

Horanai, H., Maejima, Y., & Ding, L. (2022). An Education Tool at Supports Junior Learners in Studying Machine Learning. Frontiers in Artificial Intelligence and Applications, 360, 111-116. https://doi.org/10.3233/FAIA220432

Houngue, P., Hountondji, M., & Dagba, T. (2022). An Effective Decision-Making Support for Student Academic Path Selection using Machine Learning. International Journal of Advanced Computer Science and Applications, 13(11), 727-734. https://doi.org/10.14569/IJACSA.2022.0131184

Liu, Y., Chen, L., & Yao, Z. (2022). The application of artificial intelligence assistant to deep learning in teachers’ teaching and students’ learning processes. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.929175

Luan, H., & Tsai, C. C. (2021). A Review of Using Machine Learning Approaches for Precision Education. Educational Technology and Society, 24(1), 250–266.

Luo, F., Jiang, L., Tian, X., Xiao, M., Ma, Y., & Zhang, S. (2021). Shyness prediction and language style model construction of elementary school students. Acta Psychologica Sinica, 53(2), 155-169. https://doi.org/10.3724/SP.J.1041.2021.00155

Marín, V. I. (2022). The systematic review in Educational Technology research: observations and advice. RiiTE Revista Interuniversitaria de Investigación En Tecnología Educativa, 13, 62-79. https://doi.org/10.6018/riite.533231

Mittal, S., Mahendra, S., Sanap, V., & Churi, P. (2022). International Journal of Information Management Data Insights How can machine learning be used in stress management : A systematic literature review of applications in workplaces and education. International Journal of Information Management Data Insights, 2(2), 100110. https://doi.org/10.1016/j.jjimei.2022.100110

Nafea, I. T. (2018). Machine Learning in Educational Technology. Machine Learning - Advanced Techniques and Emerging Applications. https://doi.org/10.5772/intechopen.72906

Oskotsky, T., Bajaj, R., Burchard, J., Cavazos, T., Chen, I., Connell, W., Eaneff, S., Grant, T., Kanungo, I., Lindquist, K., Myers-Turnbull, D., Naing, Z. Z. C., Tang, A., Vora, B., Wang, J., Karim, I., Swadling, C., Yang, J., Lindstaedt, B., & Sirota, M. (2022). Nurturing diversity and inclusion in AI in Biomedicine through a virtual summer program for high school students. PLoS Computational Biology, 18(1), 1-12. https://doi.org/10.1371/journal.pcbi.1009719

Pimentel, J. S., Ospina, R., & Ara, A. (2021). Learning Time Acceleration in Support Vector Regression: A Case Study in Educational Data Mining. Stats, 4(3), 682-700. https://doi.org/10.3390/stats4030041

Salas-Pilco, S. Z., & Yang, Y. (2022). Artificial intelligence applications in Latin American higher education: a systematic review. International Journal of Educational Technology in Higher Education, 19(1). https://doi.org/10.1186/s41239-022-00326-w

Salas Rueda, R. A., De la cruz Martínez, G., Eslava Cervantes, A. L., Castañeda Martínez, R., & Ramírez Ortega, J. (2022). Teachers’ opinion about collaborative virtual walls and massive open online course during the COVID-19 pandemic. Online Journal of Communication and Media Technologies, 12(1), 1-13. https://doi.org/10.30935/ojcmt/11305

Santos García, F., Valdivieso, K. D., Rienow, A., & Gairín, J. (2021). Urban–Rural Gradients Predict Educational Gaps: Evidence from a Machine Learning Approach Involving Academic Performance and Impervious Surfaces in Ecuador. ISPRS International Journal of Geo-Information, 10(12). https://doi.org/10.3390/ijgi10120830

Sanusi, I. T., Oyelere, S. S., & Omidiora, J. O. (2022). Exploring teachers’ preconceptions of teaching machine learning in high school: A preliminary insight from Africa. Computers and Education Open, 3 (November 2021), 100072. https://doi.org/10.1016/j.caeo.2021.100072

Sasmita, F., & Mulyanti, B. (2020). Development of machine learning implementation in engineering education: A literature review. IOP Conference Series: Materials Science and Engineering, 830(3). https://doi.org/10.1088/1757-899X/830/3/032061

Segura, M., Mello, J., & Herná, A. (2022). Machine Learning Prediction of University Student Dropout: Does Preference Play a Key Role? 1-20. https://doi.org/10.3390/math10183359

Su, J., Zhong, Y., Tsz, D., & Ng, K. (2022). Computers and Education : Artificial Intelligence 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(March), 100065. https://doi.org/10.1016/j.caeai.2022.100065

Suzuki, H., Hong, M., Ober, T., & Cheng, Y. (2022). Prediction of differential performance between advanced placement exam scores and class grades using machine learning. Frontiers in Education, 7(December). https://doi.org/10.3389/feduc.2022.1007779

Taha, S. A., Shihab, R. A., & Sadik, M. C. (2018). Studying of Educational Data Mining Techniques. International Journal of Advanced Research in Science, Engineering and Technology, 5(5), 5742-5750. http://www.ijarset.com/upload/2018/may/9-IJARSET-SAJATAHA.pdf

Tarik, A., Aissa, H., & Yousef, F. (2021). Artificial intelligence and machine learning to predict student performance during the COVID-19. Procedia Computer Science, 184, 835-840. https://doi.org/10.1016/j.procs.2021.03.104

Temitayo, I., Sunday, S., & Olamide, J. (2022). Exploring teachers ’ preconceptions of teaching machine learning in high school : A preliminary insight from Africa. Computers and Education Open, 3(November 2021), 100072. https://doi.org/10.1016/j.caeo.2021.100072

Van Brummelen, J., Tabunshchyk, V., & Heng, T. (2021). “Alexa, Can I Program You?”: Student Perceptions of Conversational Artificial Intelligence before and after Programming Alexa. Proceedings of Interaction Design and Children, IDC 2021, 305-313. https://doi.org/10.1145/3459990.3460730

Yamamoto, S. H., & Alverson, C. Y. (2022). From high school to postsecondary education, training, and employment: Predicting outcomes for young adults with autism spectrum disorder. Autism and Developmental Language Impairments, 7. https://doi.org/10.1177/23969415221095019

Yepes-Nuñez, J. J., Urrútia, G., Romero-García, M., & Alonso-Fernández, S. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Revista Espanola de Cardiologia, 74(9), 790-799. https://doi.org/10.1016/j.recesp.2021.06.016

Yu, Y., Fan, J., Xian, Y., & Wang, Z. (2022). Graph Neural Network for Senior High Student’s Grade Prediction. Applied Sciences (Switzerland), 12(8). https://doi.org/10.3390/app12083881

Zafari, M., Sadeghi-Niaraki, A., Choi, S. M., & Esmaeily, A. (2021). A practical model for the evaluation of high school student performance based on machine learning. Applied Sciences (Switzerland), 11(23). https://doi.org/10.3390/app112311534

Zawacki-Richter, O., Kerres, M., Bedenlier, S., & Buntins, K. (2020). Systematic Reviews in Educational Research. In Systematic Reviews in Educational Research. https://doi.org/10.1007/978-3-658-27602-7

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(1). https://doi.org/10.1186/s41239-019-0171-0

Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J. B., Yuan, J., & Li, Y. (2021). A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity, 2021. https://doi.org/10.1155/2021/8812542

Zhu, J., & Liu, W. (2020). A tale of two databases: the use of Web of Science and Scopus in academic papers. Scientometrics, 123(1), 321-335. https://doi.org/10.1007/s11192-020-03387-8

Published

2024-01-01

How to Cite

Forero-Corba, W., & Negre Bennasar, F. (2024). Techniques and applications of Machine Learning and Artificial Intelligence in education: a systematic review. RIED. Revista Iberoamericana De Educación a Distancia, 27(1), 209–253. https://doi.org/10.5944/ried.27.1.37491

Issue

Section

Research and Case Studies