mlsanityData to 7 Oct 2026

Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education?

arXiv 2104.12920 ↗First posted 27 Apr 2021cs.HC cs.AI cs.CYcited by 9

Not on the map. Why?

Abstract (arXiv)

The development of educational AI (AIEd) systems has often been motivated by their potential to promote educational equity and reduce achievement gaps across different groups of learners -- for example, by scaling up the benefits of one-on-one human tutoring to a broader audience, or by filling gaps in existing educational services. Given these noble intentions, why might AIEd systems have inequitable impacts in practice? In this chapter, we discuss four lenses that can be used to examine how and why AIEd systems risk amplifying existing inequities. Building from these lenses, we then outline possible paths towards more equitable futures for AIEd, while highlighting debates surrounding each proposal. In doing so, we hope to provoke new conversations around the design of equitable AIEd, and to push ongoing conversations in the field forward.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 11 of its 83 references that are in the map. First posted on arXiv 27 Apr 2021.

Builds on

11 of 83 references.

References · 11

  1. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
  2. 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
  3. 2017Avoiding Discrimination through Causal Reasoningcited by 191
  4. 2020A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scorescited by 74
  5. 2018Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Makingcited by 66
  6. 2020Algorithmic Fairness in Educationcited by 44
  7. 2018Fair Algorithms for Learning in Allocation Problemscited by 29
  8. 2019Toward Fairness in AI for People with Disabilities: A Research Roadmapcited by 21
  9. 2018AI in Education needs interpretable machine learning: Lessons from Open Learner Modellingcited by 18
  10. 2017Proxy Non-Discrimination in Data-Driven Systemscited by 13
  11. 2020The TA Framework: Designing Real-time Teaching Augmentation for K-12 Classroomscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 1; 2025: 1; 2026: 3.

9 papers.

  1. 2023Assessing Student Errors in Experimentation Using Artificial Intelligence and Large Language Models: A Comparative Study with Human Raterscited by 16
  2. 2023Is Your Model "MADD"? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Modelscited by 7
  3. 2022Equitable Ability Estimation in Neurodivergent Student Populations with Zero-Inflated Learner Modelscited by 1
  4. 2022Lost in Translation: Reimagining the Machine Learning Life Cycle in Educationcited by 1
  5. 2026The Aftermath of DrawEduMath: Vision Language Models Underperform with Struggling Students and Misdiagnose Errorscited by 1
  6. 2024A Fair Post-Processing Method based on the MADD Metric for Predictive Student Modelscited by 0
  7. 2025Who Is Lagging Behind: Profiling Student Behaviors with Graph-Level Encoding in Curriculum-Based Online Learning Systemscited by 0
  8. 2026Deployment of AI-Assisted Interventions: Capacity Constraints and Noisy Compliancecited by 0
  9. 2026Education-centered critical policy analysis of AI: Ghana's AI strategy as a casecited by 0