Skip to main content
SpringerLink
Log in

Artificial Intelligence in Education

  • Reference work entry
  • First Online:
Encyclopedia of Education and Information Technologies

Synonyms

Adaptive learning; Augmented intelligence in education; Dialogue-based Tutoring systems; Intelligent tutoring systems; Personalised learning

Introduction

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 intellig...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 799.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 999.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Alexander R (2010) Dialogic teaching essentials. National Institute of Education, Singapore

    Google Scholar 

  • 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 361–392

    Google Scholar 

  • Black P, Harrison C, Lee C, Marshall B, Wiliam D (2003) Assessment for Learning. Putting It into Practice. McGraw-Hill Education, Maidenhead, England

    Google Scholar 

  • Bloom BS (1984) The 2 sigma problem: the search for methods of group instruction as effective as one-to-one tutoring. Educ Res 13:4–16

    Article  Google Scholar 

  • 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 316–334

    Google Scholar 

  • Bower M, Howe C, McCredie N et al (2014) Augmented reality in education–cases, places and potentials. Educ Media Int 51:1–15

    Article  Google Scholar 

  • Bruner JS (1961) The act of discovery. Harv Educ Rev 31:21–32

    Google Scholar 

  • Burstein J, Marcu D (2003) A machine learning approach for identification thesis and conclusion statements in student essays. Comput Humanit 37:455–467

    Article  Google Scholar 

  • Caliskan A, Bryson JJ, Narayanan A (2017) Semantics derived automatically from language corpora contain human-like biases. Science 356:183–186

    Article  Google Scholar 

  • Cumming G, McDougall A (2000) Mainstreaming AIED into education? Int J Artif Intell Educ IJAIED 11:197–207

    Google Scholar 

  • Dillenbourg P (1999) What do you mean by collaborative learning? In: Dillenbourg P (ed) Collaborative-learning: Cognitive and Computational Approaches. Elsevier, Oxford, pp 1–19

    Google Scholar 

  • 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 17:217–226

    Google Scholar 

  • Domingos P (2017) The master algorithm: how the quest for the ultimate learning machine will remake our world, 1st edn. Penguin, London

    Google Scholar 

  • du Boulay B (2016) Artificial intelligence as an effective classroom assistant. IEEE Intell Syst 31:76–81. https://doi.org/10.1109/MIS.2016.93

    Article  Google Scholar 

  • Ericsson KA, Krampe RT, Tesch-Römer C (1993) The role of deliberate practice in the acquisition of expert performance. Psychol Rev 100:363

    Article  Google Scholar 

  • Evens M, Michael J (2006) One-on-one tutoring by humans and computers. Psychology Press, New York, NY

    Google Scholar 

  • Fiorillo CD (2003) Discrete coding of reward probability and uncertainty by dopamine neurons. Science 299:1898–1902. https://doi.org/10.1126/science.1077349

    Article  Google Scholar 

  • 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 1–18

    Google Scholar 

  • Franzke M, Streeter LA (2006) Building student summarization, writing and reading comprehension skills with guided practice and automated feedback. Pearson, London

    Google Scholar 

  • 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 133–141

    Google Scholar 

  • 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 351–364

    Google Scholar 

  • Goel AK, Polepeddi L (2017) Jill Watson: a virtual teaching assistant for online education. Georgia Tech, Atlanta, GA

    Google Scholar 

  • 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-x

    Article  Google Scholar 

  • Graesser AC, VanLehn K, Rosé CP et al (2001) Intelligent tutoring systems with conversational dialogue. AI Mag 22:39

    Google Scholar 

  • 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

  • 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 2014

    Google Scholar 

  • Hodgen J, Foster C, Marks R, Brown M (2018) Improving mathematics in key stages two and three: evidence review. 204

    Google Scholar 

  • 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 620–623

    Chapter  Google Scholar 

  • Holmes W, Anastopoulou S, Schaumburg H, Mavrikis M (2018) Technology-enhanced personalised learning. Untangling the evidence. Robert Bosch Stftung, Stuttgart

    Google Scholar 

  • Holmes W, Bialik M, Fadel C (2019) Artificial Intelligence in Education. Promises and Implications for Teaching and Learning. Center for Curriculum Redesign, Boston, MA

    Google Scholar 

  • Kapur M (2008) Productive failure. Cogn Instr 26:379–424. https://doi.org/10.1080/07370000802212669

    Article  Google Scholar 

  • Landauer TK, Lochbaum KE, Dooley S (2009) A new formative assessment technology for reading and writing. Theory Pract 48:44–52. https://doi.org/10.1080/00405840802577593

    Article  Google Scholar 

  • Luckin R (2010) Re-designing learning contexts: technology-rich Learner-centred ecologies. Routledge, London

    Google Scholar 

  • Luckin R, Holmes W, Griffiths M, Forcier LB (2016) Intelligence unleashed. An argument for AI in education. Pearson, London

    Google Scholar 

  • Mathews M, Robles D, Bowe B (2017) BIM+Blockchain: a solution to the trust problem in collaboration? In: CITA BIM Gathering 2017

    Google Scholar 

  • Mayer RE, Moreno R (2003) Nine ways to reduce cognitive load in multimedia learning. Educ Psychol 38:43–52

    Article  Google Scholar 

  • Mayer-Schonberger V, Cukier K (2013) Big data: a revolution that will transform how we live, work and think. John Murray, London

    Google Scholar 

  • 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, Cham

    Google Scholar 

  • Morcos AS, Barrett DGT, Rabinowitz NC, Botvinick M (2018) On the importance of single directions for generalization. ArXiv180306959 Cs Stat

    Google Scholar 

  • 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 50–57

    Google Scholar 

  • 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 553–570

    Google Scholar 

  • O’Neil C (2017) Weapons of math destruction: how big data increases inequality and threatens democracy, 01 edition. Penguin, London

    MATH  Google Scholar 

  • 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/10.3102/0162373713507480

    Article  Google Scholar 

  • Radu I (2014) Augmented reality in education: a meta-review and cross-media analysis. Pers Ubiquit Comput 18:1533–1543

    Article  Google Scholar 

  • Rohrer D, Taylor K (2007) The shuffling of mathematics problems improves learning. Instr Sci 35:481–498

    Article  Google Scholar 

  • Rudner LM, Garcia V, Welch C (2006) An Evaluation of the IntelliMetricSM Essay Scoring System. The Journal of Technology, Learning, and Assessment 4:22

    Google Scholar 

  • Rummel N, Mavrikis M, Wiedmann M et al (2016) Combining exploratory learning with structured practice to Foster conceptual and procedural fractions knowledge. ICLS, Singapore

    Google Scholar 

  • Russell S, Norvig P (2016) Artificial intelligence: a modern approach, 3rd edn. Pearson, Boston

    MATH  Google Scholar 

  • 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.2014.14

    Article  Google Scholar 

  • 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 490–496

    Google Scholar 

  • Shute VJ (2008) Focus on formative feedback. Rev Educ Res 78:153–189. https://doi.org/10.3102/0034654307313795

    Article  Google Scholar 

  • Shute VJ (2011) Stealth assessment in computer-based games to support learning. Comput Game Instr 55:503–524

    Google Scholar 

  • 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

  • Slade S, Prinsloo P (2013) Learning analytics ethical issues and dilemmas. Am Behav Sci 57:1510–1529

    Article  Google Scholar 

  • 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 161–178

    Google Scholar 

  • 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 128–137

    Google Scholar 

  • VanLehn K (2011) The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educ Psychol 46:197–221. https://doi.org/10.1080/00461520.2011.611369

    Article  Google Scholar 

  • Vygotsky LS (1978) Mind in society: development of higher psychological processes. Harvard University Press, Cambridge, MA

    Google Scholar 

  • 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, Kuwait

    Google Scholar 

  • 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 208–212

    Google Scholar 

  • 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 1–43

    Google Scholar 

  • Woolf BP (2008) Building intelligent interactive tutors: student-centered strategies for revolutionizing e-learning. Morgan Kaufmann, Amsterdam/Boston

    Google Scholar 

  • World Economic Forum, The Boston Consulting Group (2016) New vision for education: fostering social and emotional learning through technology, World Economic Forum, Geneva, Switzerland

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Educational Technology, The Open University, Milton Keynes, UK

    Wayne Holmes

Authors
  1. Wayne Holmes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wayne Holmes .

Editor information

Editors and Affiliations

  1. Victoria University, Melbourne, VIC, Australia

    Arthur Tatnall

Section Editor information

  1. Satakunta University of Applied Sciences, Pori, Finland

    Jari Multisilta

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Holmes, W. (2020). Artificial Intelligence in Education. In: Tatnall, A. (eds) Encyclopedia of Education and Information Technologies. Springer, Cham. https://doi.org/10.1007/978-3-030-10576-1_107

Download citation

Publish with us

Policies and ethics

Search

Navigation