mlsanityData to 14 Oct 2026

The Ethics of AI in Education

arXiv 2406.11842 ↗First posted 22 Mar 2024cs.CY cs.AIcited by 7

Not on the map. Why?

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.

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

9 of 57 references.

References · 9

  1. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,312
  2. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 956
  3. 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
  4. 2019Artificial Intelligence: the global landscape of ethics guidelinescited by 107
  5. 2019Discrimination in the Age of Algorithmscited by 51
  6. 2018POTs: Protective Optimization Technologiescited by 28
  7. 2018AI in Education needs interpretable machine learning: Lessons from Open Learner Modellingcited by 18
  8. 2021Beyond "Fairness:" Structural (In)justice Lenses on AI for Educationcited by 12
  9. 2020Evaluation of Fairness Trade-offs in Predicting Student Successcited by 10

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 2.

7 papers.

  1. 2024Foundation Models for Education: Promises and Prospectscited by 10
  2. 2025Towards responsible AI for education: Hybrid human-AI to confront the Elephant in the roomcited by 3
  3. 2024A Manifesto for a Pro-Actively Responsible AI in Educationcited by 2
  4. 2025A Participatory Strategy for AI Ethics in Education and Rehabilitation grounded in the Capability Approachcited by 2
  5. 2026Neural-Symbolic Knowledge Tracing: Injecting Educational Knowledge into Deep Learning for Responsible Learner Modellingcited by 1
  6. 2025Framework, Standards, Applications and Best practices of Responsible AI : A Comprehensive Surveycited by 0
  7. 2026Relational AI in Education: Reciprocity, Participatory Design, and Indigenous Worldviewscited by 0