Encyclopedia of Education and Information Technologies

2020 Edition
| Editors: Arthur Tatnall

Artificial Intelligence in Education

  • Wayne HolmesEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-3-030-10576-1_107



Artificial Intelligence (AI) technologies have been researched in educational contexts for more than 30 years (Woolf 1988; Cumming and McDougall 2000; du Boulay 2016). More recently, commercial AI products have also entered the classroom. However, while many assume that Artificial Intelligence in Education (AIED) means students taught by robot teachers, the reality is more prosaic yet still has the potential to be transformative (Holmes et al. 2019). This chapter introduces AIED, an approach that has so far received little mainstream attention, both as a set of technologies and as a field of inquiry. It discusses AIED’s AI foundations, its use of models, its possible future, and the human context. It begins with some brief examples of AIED technologies.

The first example, Cognitive Tutor, is a type of AIED known as an int...

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  1. Alexander R (2010) Dialogic teaching essentials. National Institute of Education, SingaporeGoogle Scholar
  2. Bereiter C, Scardamalia M (1989) Intentional learning as a goal of instruction. In: Resnick LB (ed) Knowing, Learning, and Instruction: Essays in Honor of Robert Glaser. Lawrence Erlbaum Associates, Inc., Hillsdale, NJ, pp 361–392Google Scholar
  3. Black P, Harrison C, Lee C, Marshall B, Wiliam D (2003) Assessment for Learning. Putting It into Practice. McGraw-Hill Education, Maidenhead, EnglandGoogle Scholar
  4. Bloom BS (1984) The 2 sigma problem: the search for methods of group instruction as effective as one-to-one tutoring. Educ Res 13:4–16CrossRefGoogle Scholar
  5. Bostrom N, Yudkowsky E (2014) The ethics of artificial intelligence. In: Ramsey W, Frankish K (eds) Cambridge Handbook of Artificial Intelligence. Cambridge University Press, Cambridge, UK, pp 316–334Google Scholar
  6. Bower M, Howe C, McCredie N et al (2014) Augmented reality in education–cases, places and potentials. Educ Media Int 51:1–15CrossRefGoogle Scholar
  7. Bruner JS (1961) The act of discovery. Harv Educ Rev 31:21–32Google Scholar
  8. Burstein J, Marcu D (2003) A machine learning approach for identification thesis and conclusion statements in student essays. Comput Humanit 37:455–467CrossRefGoogle Scholar
  9. Caliskan A, Bryson JJ, Narayanan A (2017) Semantics derived automatically from language corpora contain human-like biases. Science 356:183–186CrossRefGoogle Scholar
  10. Cumming G, McDougall A (2000) Mainstreaming AIED into education? Int J Artif Intell Educ IJAIED 11:197–207Google Scholar
  11. Dillenbourg P (1999) What do you mean by collaborative learning? In: Dillenbourg P (ed) Collaborative-learning: Cognitive and Computational Approaches. Elsevier, Oxford, pp 1–19Google Scholar
  12. Dimitrova V, Mccalla G, Bull S (2007) Preface: Open learner models: Future research directions Special Issue of the IJAIED (Part 2). International Journal of Artificial Intelligence in Education 17:217–226Google Scholar
  13. Domingos P (2017) The master algorithm: how the quest for the ultimate learning machine will remake our world, 1st edn. Penguin, LondonGoogle Scholar
  14. du Boulay B (2016) Artificial intelligence as an effective classroom assistant. IEEE Intell Syst 31:76–81.  https://doi.org/10.1109/MIS.2016.93CrossRefGoogle Scholar
  15. Ericsson KA, Krampe RT, Tesch-Römer C (1993) The role of deliberate practice in the acquisition of expert performance. Psychol Rev 100:363CrossRefGoogle Scholar
  16. Evens M, Michael J (2006) One-on-one tutoring by humans and computers. Psychology Press, New York, NYGoogle Scholar
  17. Fiorillo CD (2003) Discrete coding of reward probability and uncertainty by dopamine neurons. Science 299:1898–1902.  https://doi.org/10.1126/science.1077349CrossRefGoogle Scholar
  18. Foltz PW (2014) Improving student writing through automated formative assessment: practices and results. In: International Association for Educational Assessment (IAEA) Conference Singapore. Pearson, London, pp 1–18Google Scholar
  19. Franzke M, Streeter LA (2006) Building student summarization, writing and reading comprehension skills with guided practice and automated feedback. Pearson, LondonGoogle Scholar
  20. Freina L, Ott M (2015) A literature review on immersive virtual reality in education: state of the art and perspectives. In: Roceanu I, Florica Moldoveanu, Trausan-Matu S, Dragos Barbieru, Beligan D, Ionita A (eds) The International Scientific Conference eLearning and Software for Education. pp 133–141Google Scholar
  21. Gaved M, Luley P, Efremidis S, et al (2014) Challenges in context-aware mobile language learning: the MASELTOV approach. In: Kalz M, Bayyurt Y, Specht M (eds) Mobile as a Mainstream – Towards Future Challenges in Mobile Learning. mLearn 2014. Communications in Computer and Information Science. Springer, Cham, pp 351–364Google Scholar
  22. Goel AK, Polepeddi L (2017) Jill Watson: a virtual teaching assistant for online education. Georgia Tech, Atlanta, GAGoogle Scholar
  23. Goodman BA, Linton FN, Gaimari RD et al (2005) Using dialogue features to predict trouble during collaborative learning. User Model User-Adapt Interact 15:85–134.  https://doi.org/10.1007/s11257-004-5269-xCrossRefGoogle Scholar
  24. Graesser AC, VanLehn K, Rosé CP et al (2001) Intelligent tutoring systems with conversational dialogue. AI Mag 22:39Google Scholar
  25. Hassani K, Nahvi A, Ahmadi A (2013) Design and implementation of an intelligent virtual environment for improving speaking and listening skills. Interact Learn Environ 24(1):252–271.  https://doi.org/10.1080/10494820.2013.846265
  26. Hawkin S, Russell S, Tegmark M, Wilczek F (2014) Transcendence looks at the implications of artificial intelligence – but are we taking AI seriously enough? The Independent, 1 May 2014Google Scholar
  27. Hodgen J, Foster C, Marks R, Brown M (2018) Improving mathematics in key stages two and three: evidence review. 204Google Scholar
  28. Holmes W, Mavrikis M, Hansen A, Grawemeyer B (2015) Purpose and level of feedback in an exploratory learning environment for fractions. In: Conati C, Heffernan N, Mitrovic A, Verdejo MF (eds) Artificial intelligence in education. Springer International Publishing, Cham, pp 620–623CrossRefGoogle Scholar
  29. Holmes W, Anastopoulou S, Schaumburg H, Mavrikis M (2018) Technology-enhanced personalised learning. Untangling the evidence. Robert Bosch Stftung, StuttgartGoogle Scholar
  30. Holmes W, Bialik M, Fadel C (2019) Artificial Intelligence in Education. Promises and Implications for Teaching and Learning. Center for Curriculum Redesign, Boston, MAGoogle Scholar
  31. Kapur M (2008) Productive failure. Cogn Instr 26:379–424.  https://doi.org/10.1080/07370000802212669CrossRefGoogle Scholar
  32. Landauer TK, Lochbaum KE, Dooley S (2009) A new formative assessment technology for reading and writing. Theory Pract 48:44–52.  https://doi.org/10.1080/00405840802577593CrossRefGoogle Scholar
  33. Luckin R (2010) Re-designing learning contexts: technology-rich Learner-centred ecologies. Routledge, LondonGoogle Scholar
  34. Luckin R, Holmes W, Griffiths M, Forcier LB (2016) Intelligence unleashed. An argument for AI in education. Pearson, LondonGoogle Scholar
  35. Mathews M, Robles D, Bowe B (2017) BIM+Blockchain: a solution to the trust problem in collaboration? In: CITA BIM Gathering 2017Google Scholar
  36. Mayer RE, Moreno R (2003) Nine ways to reduce cognitive load in multimedia learning. Educ Psychol 38:43–52CrossRefGoogle Scholar
  37. Mayer-Schonberger V, Cukier K (2013) Big data: a revolution that will transform how we live, work and think. John Murray, LondonGoogle Scholar
  38. Mazziotti C, Holmes W, Wiedmann M, et al (2015) Robust student knowledge: adapting to individual student needs as they explore the concepts and practice the procedures of fractions (workshop paper). In: Conati, C, Heffernan N, Mitrovic A, Verdejo MF (eds) Artificial Intelligence in Education. 17th International Conference, AIED 2015, Madrid, Spain, Proceedings. Springer, ChamGoogle Scholar
  39. Morcos AS, Barrett DGT, Rabinowitz NC, Botvinick M (2018) On the importance of single directions for generalization. ArXiv180306959 Cs StatGoogle Scholar
  40. Mujkanovic A, Lowe D, Willey K, Guetl C (2012) Unsupervised learning algorithm for adaptive group formation: collaborative learning support in remotely accessible laboratories. In: International Conference on Information Society (i-Society 2012) London. IEEE, Piscataway, NJ, pp 50–57Google Scholar
  41. Müller VC, Bostrom N (2016) Future progress in artificial intelligence: a survey of expert opinion. In: Müller V (ed) Fundamental Issues of Artificial Intelligence. Synthese Library (Studies in Epistemology, Logic, Methodology, and Philosophy of Science). Springer, Cham, pp 553–570Google Scholar
  42. O’Neil C (2017) Weapons of math destruction: how big data increases inequality and threatens democracy, 01 edition. Penguin, LondonzbMATHGoogle Scholar
  43. Pane JF, Griffin BA, McCaffrey DF, Karam R (2014) Effectiveness of cognitive tutor Algebra I at scale. Educ Eval Policy Anal 36:127–144.  https://doi.org/10.3102/0162373713507480CrossRefGoogle Scholar
  44. Radu I (2014) Augmented reality in education: a meta-review and cross-media analysis. Pers Ubiquit Comput 18:1533–1543CrossRefGoogle Scholar
  45. Rohrer D, Taylor K (2007) The shuffling of mathematics problems improves learning. Instr Sci 35:481–498CrossRefGoogle Scholar
  46. Rudner LM, Garcia V, Welch C (2006) An Evaluation of the IntelliMetricSM Essay Scoring System. The Journal of Technology, Learning, and Assessment 4:22Google Scholar
  47. Rummel N, Mavrikis M, Wiedmann M et al (2016) Combining exploratory learning with structured practice to Foster conceptual and procedural fractions knowledge. ICLS, SingaporeGoogle Scholar
  48. Russell S, Norvig P (2016) Artificial intelligence: a modern approach, 3rd edn. Pearson, BostonzbMATHGoogle Scholar
  49. Ruthenbeck GS, Reynolds KJ (2015) Virtual reality for medical training: the state-of-the-art. J Simul 9:16–26.  https://doi.org/10.1057/jos.2014.14CrossRefGoogle Scholar
  50. Sharples M, Domingue J (2016) The blockchain and kudos: A distributed system for educational record, reputation and reward. In: Verbert K, Sharples M, Klobucar T (eds) European Conference on Technology Enhanced Learning. Springer, Cham, pp 490–496Google Scholar
  51. Shute VJ (2008) Focus on formative feedback. Rev Educ Res 78:153–189.  https://doi.org/10.3102/0034654307313795CrossRefGoogle Scholar
  52. Shute VJ (2011) Stealth assessment in computer-based games to support learning. Comput Game Instr 55:503–524Google Scholar
  53. Siemens G (2011) 1st international conference on learning analytics and knowledge 2011|connecting the technical, pedagogical, and social dimensions of learning analytics. https://tekri.athabascau.ca/analytics/about. Accessed 29 Oct 2017
  54. Slade S, Prinsloo P (2013) Learning analytics ethical issues and dilemmas. Am Behav Sci 57:1510–1529CrossRefGoogle Scholar
  55. Slavin RE (2010) Co-operative learning. What makes group-work work? In: Dumont H, Istance D, Benavides F (eds) The nature of learning. Using research to inspire practice. OECD, pp 161–178Google Scholar
  56. Soller A, Wiebe J, Lesgold A (2002) A Machine Learning Approach to Assessing Knowledge Sharing During Collaborative Learning Activities. In: Stahl G (ed) CSCL ’02 Proceedings of the Conference on Computer Support for Collaborative Learning: Foundations for a CSCL Community. International Society of the Learning Sciences, Alpharetta, GA, pp 128–137Google Scholar
  57. VanLehn K (2011) The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educ Psychol 46:197–221.  https://doi.org/10.1080/00461520.2011.611369CrossRefGoogle Scholar
  58. Vygotsky LS (1978) Mind in society: development of higher psychological processes. Harvard University Press, Cambridge, MAGoogle Scholar
  59. Whitelock D, Field D, Pulman S, et al (2013) OpenEssayist: an automated feedback system that supports university students as they write summative essays. In: 1st International Conference on Open Learning: Role, Challenges and Aspirations. Arab Open University, KuwaitGoogle Scholar
  60. Whitelock D, Twiner A, Richardson JT, et al (2015) OpenEssayist: A Supply and Demand Learning Analytics Tool for Drafting Academic Essays. In: Baron J, Lynch G, Maziarz N, Blikstein P, Merceron A, Siemens G (eds) LAK ’15 Proceedings of the Fifth International Conference on Learning Analytics And Knowledge. ACM, New York, NY, pp 208–212Google Scholar
  61. Woolf B (1988) Intelligent Tutoring Systems: A Survey. In: Shrobe HE, American Association for Artificial Intelligence (eds) Exploring Artificial Intelligence. Morgan Kaufmann, San Mateo, CA, pp 1–43Google Scholar
  62. Woolf BP (2008) Building intelligent interactive tutors: student-centered strategies for revolutionizing e-learning. Morgan Kaufmann, Amsterdam/BostonGoogle Scholar
  63. World Economic Forum, The Boston Consulting Group (2016) New vision for education: fostering social and emotional learning through technology, World Economic Forum, Geneva, SwitzerlandGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Educational TechnologyThe Open UniversityMilton KeynesUK

Section editors and affiliations

  • Jari Multisilta
    • 1
  1. 1.Satakunta University of Applied SciencesPoriFinland