# The Ethics of AI in Education

arXiv: https://arxiv.org/abs/2406.11842 · HTML: https://mlsanity.com/paper/2406.11842/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 57 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [Discrimination in the Age of Algorithms](https://mlsanity.com/paper/1902.03731/) (2019)
- [POTs: Protective Optimization Technologies](https://mlsanity.com/paper/1806.02711/) (2018)
- [AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling](https://mlsanity.com/paper/1807.00154/) (2018)
- [Beyond "Fairness:" Structural (In)justice Lenses on AI for Education](https://mlsanity.com/paper/2105.08847/) (2021)
- [Evaluation of Fairness Trade-offs in Predicting Student Success](https://mlsanity.com/paper/2007.00088/) (2020)

## Built on by (most cited first, 7 of 7)

- [Foundation Models for Education: Promises and Prospects](https://mlsanity.com/paper/2405.10959/) (2024)
- [Towards responsible AI for education: Hybrid human-AI to confront the Elephant in the room](https://mlsanity.com/paper/2504.16148/) (2025)
- [A Manifesto for a Pro-Actively Responsible AI in Education](https://mlsanity.com/paper/2407.05423/) (2024)
- [A Participatory Strategy for AI Ethics in Education and Rehabilitation grounded in the Capability Approach](https://mlsanity.com/paper/2505.15466/) (2025)
- [Neural-Symbolic Knowledge Tracing: Injecting Educational Knowledge into Deep Learning for Responsible Learner Modelling](https://mlsanity.com/paper/2604.08263/) (2026)
- [Framework, Standards, Applications and Best practices of Responsible AI : A Comprehensive Survey](https://mlsanity.com/paper/2504.13979/) (2025)
- [Relational AI in Education: Reciprocity, Participatory Design, and Indigenous Worldviews](https://mlsanity.com/paper/2604.19099/) (2026)

## Abstract (arXiv)

The transition of Artificial Intelligence (AI) from a lab-based science to live human contexts brings into sharp focus many historic, socio-cultural biases, inequalities, and moral dilemmas. Many questions that have been raised regarding the broader ethics of AI are also relevant for AI in Education (AIED). AIED raises further specific challenges related to the impact of its technologies on users, how such technologies might be used to reinforce or alter the way that we learn and teach, and what we, as a society and individuals, value as outcomes of education. This chapter discusses key ethical dimensions of AI and contextualises them within AIED design and engineering practices to draw connections between the AIED systems we build, the questions about human learning and development we ask, the ethics of the pedagogies we use, and the considerations of values that we promote in and through AIED within a wider socio-technical system.