CoChat
Sign Up
Cited by 269Artificial Intelligence in EducationEthics and Social Impacts of AIDigital Education and Society

Artificial Intelligence Alone Will Not Democratise Education: On Educational Inequality, Techno-Solutionism and Inclusive Tools

Open access
Bulathwela, Sahan, Pérez-Ortiz, María, Holloway, Catherine, Cukurova, Mutlu, Shawe-Taylor, John·2024·Sustainability
DOI 10.3390/su16020781
Open this paper in CoChat — free accountOpen PDF

Abstract

Artificial Intelligence (AI) in Education claims to have the potential for building personalised curricula, as well as bringing opportunities for democratising education and creating a renaissance of new ways of teaching and learning. Millions of students are starting to benefit from the use of these technologies, but millions more around the world are not, due to the digital divide and deep pre-existing social and educational inequalities. If this trend continues, the first large-scale delivery of AI in Education could lead to greater educational inequality, along with a global misallocation of educational resources motivated by the current techno-solutionist narrative, which proposes technological solutions as a quick and flawless way to solve complex real-world problems. This work focuses on posing questions about the future of AI in Education, intending to initiate the pressing conversation that could set the right foundations (e.g., inclusion and diversity) for a new generation of education that is permeated with AI technology. The main goal of our opinion piece is to conceptualise a sustainable, large-scale and inclusive AI for the education ecosystem that facilitates equitable, high-quality lifelong learning opportunities for all. The contribution starts by synthesising how AI might change how we learn and teach, focusing on the case of personalised learning companions and assistive technology for disability. Then, we move on to discuss some socio-technical features that will be crucial to avoiding the perils of these AI systems worldwide (and perhaps ensuring their success by leveraging more inclusive education). This work also discusses the potential of using AI together with free, participatory and democratic resources, such as Wikipedia, Open Educational Resources and open-source tools. We emphasise the need for collectively designing human-centred, transparent, interactive and collaborative AI-based algorithms that empower and give complete agency to stakeholders, as well as supporting new emerging pedagogies. Finally, we ask what it would take for this educational revolution to provide egalitarian and empowering access to education that transcends any political, cultural, language, geographical and learning-ability barriers, so that educational systems can be responsive to all learners’ needs.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/su16020781

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 73 · Cited by 269

Research works printed but not linked: 20

  1. St-Hilaire, F.; Vu, D.D.; Frau, A.; Burns, N.; Faraji, F.; Potochny, J.; Robert, S.; Roussel, A.; Zheng, S.; Glazier, T.; et al. A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions. arXiv 2022, arXiv:2203.03724. · arXiv
  2. Woolf, B.P. Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing E-Learning; Morgan Kaufmann: Cambridge, MA, USA, 2010.
  3. Novak, E.; Urbančič, J.; Jenko, M. Preparing multi-modal data for natural language processing. In Proceedings of the Slovenian KDD Conference on Data Mining and Data Warehouses (SiKDD), Ljubljana, Slovenia, 5 October 2018.
  4. Yudelson, M.V.; Koedinger, K.R.; Gordon, G.J. Individualized Bayesian Knowledge Tracing Models. In Artificial Intelligence in Education: Proceedings of the 16th International Conference, AIED 2013, Memphis, TN, USA, 9-13 July 2013;
  5. Brank, J.; Leban, G.; Grobelnik, M. Annotating Documents with Relevant Wikipedia Concepts. In Proceedings of the Slovenian KDD Conference on Data Mining and Data Warehouses (SiKDD), Ljubljana, Slovenia, 5 October 2017.
  6. Feng, M., Käser, T., Talukdar, P., Eds.; International Educational Data Mining Society: Bengaluru, India , 2023; pp. 575-577. [CrossRef] · DOI
  7. Fawzi, F.; Amini, S.; Bulathwela, S. Small Generative Language Models for Educational Question Generation. In Proceedings of the NeurIPS Workshop on Generative Artificial Intelligence for Education (GAIEd), New Orleans, LA, USA, 15 December 2023.
  8. Phung, T.; Cambronero, J.; Gulwani, S.; Kohn, T.; Majumdar, R.; Singla, A.; Soares, G. Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models. In Proceedings of the 16th International Conference on Educational Data Mining, Bengaluru, India, 11-14 July 2023;
  9. Feng, M., Käser, T., Talukdar, P., Eds.; International Conference on Educational Data Mining: Bengaluru, India, 2023; pp. 370-377.
  10. Leiker, D.; Finnigan, S.; Gyllen, A.R.; Cukurova, M. Prototyping the use of Large Language Models (LLMs) for adult learning content creation at scale. arXiv 2023, arXiv:2306.01815. · arXiv
  11. Toyama, K. Can technology end poverty. Boston Rev. 2010, 36, 12-29.
  12. Gebru, T.; Morgenstern, J.; Vecchione, B.; Vaughan, J.W.; Wallach, H.M.; Daumé, H., III; Crawford, K. Datasheets for Datasets. arXiv 2018, arXiv:1803.09010. · arXiv
  13. Mishra, S. Technology applications in education: Policy and prospects. In Technology-Enabled Learning: Policy, Pedagogy and Practice; Commonwealth of Learning (COL): Vancouver, BC, Canada, 2020; pp. 19-31.
  14. Panda, S. Return on Investment from an Open Online Course on Open Educational Resources. In Technology-Enabled Learning: Policy, Pedagogy and Practice; Commonwealth of Learning: Vancouver, BC, Canada, 2020; pp. 199-212.
  15. Sun, K.; Xu, Y.E.; Zha, H.; Liu, Y.; Dong, X.L. Head-to-Tail: How Knowledgeable are Large Language Models (LLM)? A.K.A. Will LLMs Replace Knowledge Graphs? arXiv 2023, arXiv:308.10168
  16. Bull, S.; Kay, J. Metacognition and open learner models. In Proceedings of the 3rd Workshop on Meta-Cognition and Self- Regulated Learning in Educational Technologies, at ITS2008, Montreal, QC, Canada, 23-27 June 2008; pp. 7-20.
  17. Ferragina, P.; Scaiella, U. TAGME: On-the-Fly Annotation of Short Text Fragments (by Wikipedia Entities). In Proceedings of the ACM International Conference on Information and Knowledge Management, Toronto, ON, Canada, 26-30 October 2010; CIKM'10.
  18. Ahmad, N.; Bull, S. Learner Trust in Learner Model Externalisations. In Proceedings of the 14th International Conference on Artificial Intelligence in Education, AIED 2009, Brighton, UK, 6-10 July 2009.
  19. Auer, S.; Bizer, C.; Kobilarov, G.; Lehmann, J.; Cyganiak, R.; Ives, Z. Dbpedia: A nucleus for a web of open data. In The Semantic Web; Springer: Berlin/Heidelberg, Germany, 2007; pp. 722-735.
  20. Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; et al. Llama: Open and efficient foundation language models. arXiv 2023, arXiv:2302.13971. 105. · arXiv

Authors
Bulathwela, S., Pérez-Ortiz, M., Holloway, C., Cukurova, M., & Shawe-Taylor, J.
Affiliations
  • University College London (GB)
  • Centre for Artificial Intelligence, University College London, London WC1V 6BH, UK
  • Global Disability Innovation Hub, London E20 2AF, UK
Corresponding author
Bulathwela, Sahan
ORCID
Venue
Sustainability
Publisher
MDPI AG
Published
2024-01-16
Volume
16
Issue
2
Pages
781
ISSN
2071-1050 (ISSN-L)
DOI
10.3390/su16020781
OpenAlex
W4390906507
Cited by
269
References
73
Citations by year
2026: 114 · 2025: 115 · 2024: 40
FWCI
101.14
Type
journal article
Language
en
Open access
Licence
Locations
  • Sustainability (publishedVersion, cc-by)
  • UCL Discovery (University College London) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
  • European Commission (952026, 761758, 621586-EPP-1-2020-1-NO-EPPKA2-KA, AT2030)
  • EU’s Horizon 2020 (952026, 761758, 621586-EPP-1-2020-1-NO-EPPKA2-KA, AT2030)
  • EU Erasmus+ project (Encore+) (952026, 761758, 621586-EPP-1-2020-1-NO-EPPKA2-KA, AT2030)
  • UK government (952026, 761758, 621586-EPP-1-2020-1-NO-EPPKA2-KA, AT2030)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Information Systems › Digital Education and Society
Keywords
artificial intelligence in education, educational inequality, digital divide, techno-solutionism, inclusive education, assistive technology for people with disabilities, open educational resources, Wikipedia, lifelong learning, equitable access to education, socio-technical design

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review