Introduction: AI, Inclusion, and ‘Everyone Learning Everything’

Part of the Perspectives on Rethinking and Reforming Education book series (PRRE)


This chapter provides an introduction to the book—Artificial Intelligence and Inclusive Education: speculative futures and emerging practices. It examines the potential intersections, correspondences, divergences, and contestations between the discourses that typically accompany, on the one hand, calls for artificial intelligence technology to disrupt and enhance educational practice and, on the other, appeals for greater inclusion in teaching and learning. Both these areas of discourse are shown to envision a future of ‘education for all’: artificial intelligence in education (AIEd) tends to promote the idea of an automated, and personalised, one-to-one tutor for every learner, while inclusive education often appears concerned with methods of involving marginalised and excluded individuals and organising the communal dimensions of education. However, these approaches are also shown to imply important distinctions: between the attempts at collective educational work through inclusive pedagogies and the drive for personalised learning through AIEd. This chapter presents a critical view of the quest for personalisation found in AIEd, suggesting a problematic grounding in the myth of the one-to-one tutor and questionable associations with simplistic views of ‘learner-centred’ education. In contrast, inclusive pedagogy is suggested to be more concerned with developing a ‘common ground’ for educational activity, rather than developing a one-on-one relationship between the teacher and the student. Inclusive education is therefore portrayed as political, involving the promotion of active, collective, and democratic forms of citizen participation. The chapter concludes with an outline of the subsequent contributions to the book.


Personalisation Individualism One-to-one tutoring Special education Community 


  1. Alpaydin, E. (2016). Machine learning: the new AI. Cambridge: MIT Press.Google Scholar
  2. Biesta, G. (2005). Against learning. Reclaiming a language for education in an age of Learning. Nordisk Pedagogik, 25(1), 54–66.Google Scholar
  3. Biesta, G. (2006). Beyond learning. Democratic education for a human future. Boulder, CO: Paradigm Publishers.Google Scholar
  4. Biesta, G. (2012). Giving teaching back to education: Responding to the disappearance of the teacher. Phenomenology & Practice, 6(2), 35–49.CrossRefGoogle Scholar
  5. Edyburn, D., Higgins, K., & Boone, R. (2005). Handbook of special education technology research and practice. Oviedo: Knowledge By Design, Inc.Google Scholar
  6. Florian, L. (2008). Special or inclusive education: Future trends. British Journal of Special Education, 35(4), 202–208.CrossRefGoogle Scholar
  7. Friesen, N. (forthcoming 2019). The technological imaginary in education, or: Myth and enlightenment in “Personalized Learning”. In M. Stocchetti (Ed.), The digital age and its discontents. University of Helsinki Press. Available:
  8. Ginsburg, M. (2012). Personalisation is political, but what kind of politics? In M. E. Mincu (Ed.), Personalisation of education in contexts: Policy critique and theories of personal improvement, foreword.Google Scholar
  9. Giroux, H. (2011). On critical pedagogy. Continuum. London: The Continuum International Publishing Group Ltd. Google Scholar
  10. Herold, B. (2016). Facebook’s Zuckerberg to bet big on personalized learning. Education Week. Available
  11. Hill, D. (2016). AI teaching assistant helped students online—and no one knew the difference. Singularity Hub. Available
  12. Houser, K. (2017). The solution to our education crisis might be AI. Available:
  13. Knox, J., Williamson, B., & Bayne, S. (forthcoming 2019) Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies. Learning, media and technology, special issue: Education and technology into the 2020s.Google Scholar
  14. Leopold, T. (2016). A secret ops AI aims to save education. Wired. Available
  15. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed an argument for AI in education. Pearson Report. Available
  16. Newton, C. (2016). Can AI fix education? We asked Bill Gates. The Verge.
  17. Olson, P. (2018). Building brains: How pearson plans to automate education with AI. Forbes.
  18. Slee, R. (2011). The irregular school: Exclusion, schooling, and inclusive education. London: Routledge.CrossRefGoogle Scholar
  19. UIS. (2016). The world needs almost 69 million new teachers to reach the 2030 education goals UNESCO Institute for Statistics. Available
  20. UNESCO. (2015). Education 2030: Incheon declaration and framework for action for the implementation of sustainable development goal 4: Ensure inclusive and equitable quality education and promote lifelong learning. Available
  21. von Radowitz, J. (2017). Intelligent machines will replace teachers within 10 years, leading public school headteacher predicts. The Independent. Available
  22. Wakefield, J. (2018). Robot ‘talks’ to MPs about future of AI in the classroom. BBC News, Technology section.
  23. Wang, Y. (2016). Imagining inclusive schooling: An ethnographic inquiry into disabled children’s learning and participation in regular schools in Shanghai (Ph.d. thesis). University of Edinburgh, Edinburgh.Google Scholar
  24. Whittlestone, J., Nyrup, R., Alexandrova, A., Dihal, K., & Cave, S. (2019). Ethical and societal implications of algorithms, data, and artificial intelligence: a roadmap for research. Nuffield report. Available

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Centre for Research in Digital EducationUniversity of EdinburghEdinburghUK

Personalised recommendations