Artificial Intelligence and Machine Learning Applications in Education

Abstract views: 981 / PDF downloads: 501


  • Yetkin Yildirim Rice University
  • Akif Celepcikay Fort Bend ISD



artificial intelligence, machine learning, educational data mining, data analytics, intelligent tutoring systems


Artificial Intelligence (AI), Data Analytics, and Machine Learning technologies are poised to transform the field of education as we know it. They have already upended industries from retail to manufacturing and now that the coronavirus pandemic has accelerated the shift to online classrooms, with remote teacher-student interaction and remote curriculum test, AI-powered tools are more critical for teachers and students than ever before. AI-powered intelligent tutoring systems, AI chatbots can interact with students to increase engagement of students in studies, and Machine Learning algorithms can analyze student data. Together these provide great opportunities for improving student learning, will help teachers also and will also help in many other aspects of education. This chapter will highlight some of the most interesting real-world applications of AI and Machine Learning and explain the methodology of their implementation, then describe how they can improve student learning and the effectiveness of education systems. This chapter will also discuss the critical challenges that educators and researchers face when applying these technologies in the field of education. Finally, the chapter is concluded with a discussion of the roles AI and Machine Learning can play in post-pandemic education world and of promising technologies that could be significant driving forces for even more AI and Machine Learning applications in education in the future.


Southgate, E., Blackmore, K., Pieschl, S., Grimes, S., McGuire, J., & Smithers, K. (2019) Artificial Intelligence and Emerging Technologies in Schools, A Research report Commissioned by the Australian Government Department of Education

Hutson, Matthew (2021). Who needs a teacher? Artificial intelligence designs lesson plans for itself. Science Magazine.

Wang, H. C., Chang, C. Y., & Li, T. Y. (2008) Assessing creative problem-solving with automated text grading. Computers & Education, 51(4), 1450–1466.

Abbott, R. G. (2006) Automated expert modeling for automated student evaluation. International Conference on Intelligent Tutoring Systems Springer, Berlin, Heidelberg, pp. 1–10.

Kucak, D., Juricic, V. and Dambic, G. (2018) Machine Learning in Education—A Survey of Current Research Trends. Proceedings of the 29th DAAAM International Symposium, B. Katalinic (Ed.), DAAAM International, Vienna, Austria, pp.0406–0410.

Alice Liu, Back to the Future Classroom: VR/AR/AI Transformation,

Gorad, N., Zalte, I., Nandi, A., & Nayak, D. (2017) Career counselling using data mining. International Journal of Innovative Research in Computer and Communication Engineering.

Bixler, R. and D’Mello, S.K. (2013) Detecting boredom and engagement during writing with keystroke analysis, task appraisals, and stable traits. Proceedings of the 2013 International Conference on Intelligent User Interfaces, p. 225.

D’Mello, S.K., Craig, S.D., Witherspoon, A.W., McDaniel, B.T. and Graesser, A.C. (2008) Automatic detection of learner’s affect from conversational cues. User Modeling and User-Adapted Interaction, 18, 45–80.

Baker, R.S.J.D. (2007) Modeling and Understanding Students’ Off-Task Behavior in Intelligent Tutoring Systems. Proceedings of the ACM CHI 2007: Computer-Human Interaction Conference, 1059-1068

Dekker, G., Pechenizkiy, M. and Vleeshouwers, J. (2009) Predicting Students Drop Out: A Case Study. Proceedings of the International Conference on Educational Data Mining, Barnes, T., Desmarais, M., Romero, C., and Ventura, S. (eds.), Cordoba, Spain, 41-50.

Romero, C., Ventura, S., Espejo, P.G. and Hervas, C. (2008) Data Mining Algorithms to Classify Students. Proceedings of the 1st International Conference on Educational Data Mining, 8-17.

Superby, J.F., Vandamme, J.P. and Meskens, N. (2006) Determination of factors influencing the achievement of the first-year university students using data mining methods. Proceedings of the Workshop on Educational Data Mining at the 8th International Conference on Intelligent Tutoring Systems, 37-44.

Arnold, Kimberly & Pistilli, Matthew (2012) Course signals at Purdue: Using learning analytics to increase student success. ACM International Conference Proceeding Series. 10.1145/2330601.2330666.

Graesser, A. C., Li, H., & Forsyth, C. (2014). Learning by communicating in natural language with conversational agents. Current Directions in Psychological Science, 23(5), 374-380. doi:10.1177/0963721414540680

Kulik, J.A. and Fletcher, J.D. (2016) Effectiveness of intelligent tutoring systems: a meta-analytic review. Review of Educational Research, 86(1), 42-78.

US Department of Education (2011) Winning the education future: the role of ARPA-ED.

McNeal, Marguerite (2016) A Siri for Higher Ed Aims to Boost Student Engagement. EdSurge-Digital Learner in Higher ED.

Hao, Karen (2019) China has started a grand experiment in AI education. It could reshape how the world learns. MIT Technology Review.

Settles, Burr & Meeder, Brendan. (2016). A Trainable Spaced Repetition Model for Language Learning. 1848-1858. 10.18653/v1/P16-1174.

Bisen, I., Arslan, E., Yildirim, K., Yildirim, Y., “Artificial Intelligence and Machine Learning in Higher Education,” Machine Learning Approaches for Improvising Modern Learning Systems edited by Gulzar, IGI Global, May, 2021.

Klein, Alyson (2019) Can artificial intelligence predict student engagement? Researchers investigate. Education Week.

Rouhiainen, Lasse (2019) How AI and data could personalize higher education. Harvard Business Review.

Kersting K. (2018). Machine learning and Artificial Intelligence: Two fellow travelers on the quest for intelligent behavior in machines. Frontiers in Big Data, 1(6). doi: 10.3389/fdata.2018.00006

Radu, Iulian. (2014). Augmented reality in education: A meta-review and cross-media analysis. Personal and Ubiquitous Computing. 18. 1533-1543. 10.1007/s00779-013-0747-y.

Fletcher, J. D. (2011) DARPA Education Dominance Program: April 2010 and November 2010 Digital Tutor Assessments. 31.

Guo, J., Bai, L., Yu, Z., Zhao, Z., & Wan, B. (2021). An AI-Application-Oriented In-Class Teaching Evaluation Model by Using Statistical Modeling and Ensemble Learning. Sensors (Basel, Switzerland), 21(1), 241.

Doug Bonderud (2019), Artificial Intelligence, Authentic Impact: How Educational AI is Making the Grade. Ed Tech Magazine.




How to Cite

Yildirim, Y., & Celepcikay, A. . (2021). Artificial Intelligence and Machine Learning Applications in Education. Eurasian Journal of Higher Education, (4), 1–11.




Most read articles by the same author(s)