Artificial Intelligence in Education

  • Katashi NagaoEmail author


This book explains how human learning is promoted by applying artificial intelligence to education. Before that, let’s first look back on how information technology including artificial intelligence contributed to education. Various technologies have been developed to make it easier for learners to learn and to create an environment where teachers can more easily teach. An example of this is called e-learning or intelligent tutoring systems (ITS). e-Learning is an educational system using online media and has developed together with web technology. ITS was developed using a rule-based system which is an initial result of artificial intelligence. In the process, user models for learners called learner models and educational contents have been improved. As an application of data science, technology called learning analytics was developed. This is a technique for statistically analyzing learner’s historical data obtained by e-learning, etc. and discovering the characteristics of the learner. This will contribute to personalized learning that adapts the educational system to the learner’s characteristics. Furthermore, the development of learning analytics will clarify the concept of evidence-based education. As with medical care, we should construct a feedback loop that educates in accordance with data-based analysis and the learning strategies obtained from it, and improves if there are problems. Machine learning, which is an important achievement of recent artificial intelligence, is used for data analysis at this time. In addition, we will use a method that lets machines do the feature extraction from data called deep learning. In this chapter, I will touch them in detail.


Intelligence amplification e-Learning Intelligent tutoring system Learning analytics Evidence-based education Deep learning 


  1. S. Bull, J. Kay, Student models that invite the learner in: the SMILI open learner modelling framework. Int. J. Artif. Intell. Educ. 17(2), 89–120 (2007)Google Scholar
  2. J. Carbonell, AI in CAI: an artificial-intelligence approach to computer-assisted instruction. IEEE Trans. Man-Mach. Syst. 11(4), 190–202 (1970)CrossRefGoogle Scholar
  3. W. J. Clancey, Knowledge-Based Tutoring: The GUIDON Program (The MIT Press, 1987)Google Scholar
  4. A. Collins, J. S. Brown, S. E. Newman, in Cognitive Apprenticeship: Teaching the Craft of Reading, Writing, and Mathematics. Technical Report 403 (BBN Laboratories, Cambridge, MA, Centre for the Study of Reading, University of Illinois, 1987)Google Scholar
  5. A. Kashihara, Modeling learning in engineering. J. Jpn. Soc. Artif. Intell. 30(4), 473–476 (2015). (in Japanese)Google Scholar
  6. P. Long, G. Siemens, Penetrating the fog: Analytics in learning and education. EDUCAUSE Rev. 46(5), 31–40 (2011)Google Scholar
  7. M. Lovett, O. Meyer, C. Thille, The open learning initiative: measuring the effectiveness of the OLI statistics course in accelerating student learning. J. Interact. Media Educ. 1, 2008 (2008)Google Scholar
  8. K. Nakabayashi, Technical standardization trend of educational support systems. J. Jpn. Soc. Artif. Intell. 17(4), 465–470 (2002). (in Japanese)Google Scholar
  9. D.A. Norman, Learner-centered education. Commun. ACM 39(4), 24–27 (1996)CrossRefGoogle Scholar
  10. K. Nakabayashi, e-Testing and standardization, e-Testing (Baifukan Co., Ltd, 2008), pp. 74–94. (in Japanese)Google Scholar
  11. K. Nakabayashi, Standardization of e-learning technology and design of learning activities. J. Jpn. Soc. Artif. Intell. 25(2), 250–258 (2010). (in Japanese)Google Scholar
  12. R. Nkambou et al., Advances in Intelligent Tutoring Systems (Studies in Computational Intelligence), vol. 308 (Springer, Berlin, 2010)zbMATHGoogle Scholar
  13. X. Ochoa, D. Suthers, K. Verbert, E. Duval, Analysis and reflections on the third learning analytics and knowledge conference (LAK 2013). J. Learn. Analytics. 1(2), 5–22 (2014)CrossRefGoogle Scholar
  14. F. Watanabe, Y. Mori, C. Kogo, Analyzing learners’ subjective evaluation of peer assessment in japan massive open online courses, Waseda. J. Human Sci. 28(2), 237–245 (2015)Google Scholar
  15. E. Wenger, Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge (Morgan Kaufmann Publishers Inc., 1987)Google Scholar
  16. B. Woolf, Building intelligent interactive tutors (Morgan Kaufmann, Burlington, MA, 2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Nagoya UniversityNagoyaJapan

Personalised recommendations