# Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution

arXiv: https://arxiv.org/abs/2112.02034 · HTML: https://mlsanity.com/paper/2112.02034/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 52 references that are in the map.
- First posted on arXiv 3 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [EdNet: A Large-Scale Hierarchical Dataset in Education](https://mlsanity.com/paper/1912.03072/) (2019)
- [TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources](https://mlsanity.com/paper/1911.09471/) (2019)
- [Goal-based Course Recommendation](https://mlsanity.com/paper/1812.10078/) (2018)
- [Correct Me If You Can: Learning from Error Corrections and Markings](https://mlsanity.com/paper/2004.11222/) (2020)
- [PEEK: A Large Dataset of Learner Engagement with Educational Videos](https://mlsanity.com/paper/2109.03154/) (2021)
- [Towards an Integrative Educational Recommender for Lifelong Learners](https://mlsanity.com/paper/1912.01592/) (2019)
- [Predicting Engagement in Video Lectures](https://mlsanity.com/paper/2006.00592/) (2020)
- [An AI-based Learning Companion Promoting Lifelong Learning Opportunities for All](https://mlsanity.com/paper/2112.01242/) (2021)

## Built on by (most cited first, 6 of 6)

- [Scalable Educational Question Generation with Pre-trained Language Models](https://mlsanity.com/paper/2305.07871/) (2023)
- [A Toolbox for Modelling Engagement with Educational Videos](https://mlsanity.com/paper/2401.05424/) (2023)
- [Pre-Training With Scientific Text Improves Educational Question Generation](https://mlsanity.com/paper/2212.03869/) (2022)
- [Towards Proactive Information Retrieval in Noisy Text with Wikipedia Concepts](https://mlsanity.com/paper/2210.09877/) (2022)
- [TrueLearn: A Python Library for Personalised Informational Recommendations with (Implicit) Feedback](https://mlsanity.com/paper/2309.11527/) (2023)
- [Speeding up design and making to reduce time-to-project and time-to-market: an AI-Enhanced approach in engineering education](https://mlsanity.com/paper/2503.16307/) (2025)

## Abstract (arXiv)

Artificial Intelligence (AI) in Education has been said to have the potential for building more personalised curricula, as well as democratising education worldwide and creating a Renaissance of new ways of teaching and learning. Millions of students are already starting to benefit from the use of these technologies, but millions more around the world are not. If this trend continues, the first delivery of AI in Education could be greater educational inequality, along with a global misallocation of educational resources motivated by the current technological determinism narrative. In this paper, we focus on speculating and posing questions around the future of AI in Education, with the aim of starting the pressing conversation that would set the right foundations for the new generation of education that is permeated by technology. This paper starts by synthesising how AI might change how we learn and teach, focusing specifically on the case of personalised learning companions, and then move to discuss some socio-technical features that will be crucial for avoiding the perils of these AI systems worldwide (and perhaps ensuring their success). This paper 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 also emphasise the need for collectively designing human-centered, transparent, interactive and collaborative AI-based algorithms that empower and give complete agency to stakeholders, as well as support new emerging pedagogies. Finally, we ask what would it take for this educational revolution to provide egalitarian and empowering access to education, beyond any political, cultural, language, geographical and learning ability barriers.