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

arXiv: https://arxiv.org/abs/2104.12920 · HTML: https://mlsanity.com/paper/2104.12920/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 83 references that are in the map.
- First posted on arXiv 27 Apr 2021.

## Builds on (most cited first, 11 of 11)

- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Avoiding Discrimination through Causal Reasoning](https://mlsanity.com/paper/1706.02744/) (2017)
- [A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores](https://mlsanity.com/paper/2002.08035/) (2020)
- [Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making](https://mlsanity.com/paper/1802.01029/) (2018)
- [Algorithmic Fairness in Education](https://mlsanity.com/paper/2007.05443/) (2020)
- [Fair Algorithms for Learning in Allocation Problems](https://mlsanity.com/paper/1808.10549/) (2018)
- [Toward Fairness in AI for People with Disabilities: A Research Roadmap](https://mlsanity.com/paper/1907.02227/) (2019)
- [AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling](https://mlsanity.com/paper/1807.00154/) (2018)
- [Proxy Non-Discrimination in Data-Driven Systems](https://mlsanity.com/paper/1707.08120/) (2017)
- [The TA Framework: Designing Real-time Teaching Augmentation for K-12 Classrooms](https://mlsanity.com/paper/2001.02985/) (2020)

## Built on by (most cited first, 9 of 9)

- [Assessing Student Errors in Experimentation Using Artificial Intelligence and Large Language Models: A Comparative Study with Human Raters](https://mlsanity.com/paper/2308.06088/) (2023)
- [Is Your Model "MADD"? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Models](https://mlsanity.com/paper/2305.15342/) (2023)
- [Equitable Ability Estimation in Neurodivergent Student Populations with Zero-Inflated Learner Models](https://mlsanity.com/paper/2203.10170/) (2022)
- [Lost in Translation: Reimagining the Machine Learning Life Cycle in Education](https://mlsanity.com/paper/2209.03929/) (2022)
- [The Aftermath of DrawEduMath: Vision Language Models Underperform with Struggling Students and Misdiagnose Errors](https://mlsanity.com/paper/2603.00925/) (2026)
- [A Fair Post-Processing Method based on the MADD Metric for Predictive Student Models](https://mlsanity.com/paper/2407.05398/) (2024)
- [Who Is Lagging Behind: Profiling Student Behaviors with Graph-Level Encoding in Curriculum-Based Online Learning Systems](https://mlsanity.com/paper/2508.18925/) (2025)
- [Deployment of AI-Assisted Interventions: Capacity Constraints and Noisy Compliance](https://mlsanity.com/paper/2604.14370/) (2026)
- [Education-centered critical policy analysis of AI: Ghana's AI strategy as a case](https://mlsanity.com/paper/2608.16910/) (2026)

## 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.