The DOI matches its Crossref record; 122 of the 122 references it cites were found in a registry or an index.
Algorithmic Bias in Education
Open accessAbstract
In this paper, we review algorithmic bias in education, discussing the causes of that bias and reviewing the empirical literature on the specific ways that algorithmic bias is known to have manifested in education. While other recent work has reviewed mathematical definitions of fairness and expanded algorithmic approaches to reducing bias, our review focuses instead on solidifying the current understanding of the concrete impacts of algorithmic bias in education—which groups are known to be impacted and which stages and agents in the development and deployment of educational algorithms are implicated. We discuss theoretical and formal perspectives on algorithmic bias, connect those perspectives to the machine learning pipeline, and review metrics for assessing bias. Next, we review the evidence around algorithmic bias in education, beginning with the most heavily-studied categories of race/ethnicity, gender, and nationality, and moving to the available evidence of bias for less-studied categories, such as socioeconomic status, disability, and military-connected status. Acknowledging the gaps in what has been studied, we propose a framework for moving from unknown bias to known bias and from fairness to equity. We discuss obstacles to addressing these challenges and propose four areas of effort for mitigating and resolving the problems of algorithmic bias in AIED systems and other educational technology.
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In the citing paper
“Empirical studies show that AI systems may replicate social and cultural inequalities embedded in training data, influencing assessment outcomes and learning opportunities (Baker & Hawn, 2022;Barbierato et al., 2022;Deho et al., 2023;Jiang & Pardos, 2021).”
From Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education (2026) · cites this paper as Baker & Hawn, 2022
In the citing paper
“Similarly, ChatGPT's apparent fluency can encourage students to skip ethical consideration or critical examination, particularly when its biases remain hidden (Baker & Hawn, 2022).”
From Generative AI in Design Thinking Pedagogy: Enhancing Creativity, Critical Thinking, and Ethical Reasoning in Higher Education (2025) · cites this paper as Baker & Hawn, 2022
In the citing paper
“Their review shows that algorithmic harms are often amplified in educational systems that rely heavily on predictive modelling for assessments, admissions, or instructional personalisation.”
From Generative AI in Design Thinking Pedagogy: Enhancing Creativity, Critical Thinking, and Ethical Reasoning in Higher Education (2025) · cites this paper as Baker & Hawn, 2022
In the citing paper
“Moreover, Baker and Hawn (2022) caution that automation may subtly displace critical judgment, especially in repetitive tasks or environments with ambiguous accountability structures.”
From Generative AI in Design Thinking Pedagogy: Enhancing Creativity, Critical Thinking, and Ethical Reasoning in Higher Education (2025) · cites this paper as Baker & Hawn, 2022
In the citing paper
“Students consistently identified the limitations of GenAI tools, whether factual errors in ChatGPT's market data or Eurocentric imagery generated by DALL•E, and responded by applying evaluative strategies (Rana, 2024;Baker & Hawn, 2022).”
From Generative AI in Design Thinking Pedagogy: Enhancing Creativity, Critical Thinking, and Ethical Reasoning in Higher Education (2025) · cites this paper as Baker & Hawn, 2022
In the citing paper
“When not accompanied by ethical framing, structured experimentation, and critical reflection, GenAI tools may encourage automation bias, epistemic shallowness, and dependency (Zhai et al., 2024;Baker & Hawn, 2022).”
From Generative AI in Design Thinking Pedagogy: Enhancing Creativity, Critical Thinking, and Ethical Reasoning in Higher Education (2025) · cites this paper as Baker & Hawn, 2022
In the citing paper
“shows that bias can enter at multiple stages of the machine-learning pipeline and affect groups defined not only by race, gender, or nationality but also by socioeconomic status and disability (Baker & Hawn, 2022).”
From Responsible AI in Polish Higher-Education Administration under the EU AI Act: High-Risk Use Cases, Fundamental-Rights Impact Assessment, and Institutional Accountability (2026) · cites this paper as Baker & Hawn, 2022
In the citing paper
“Researchers have also highlighted the lack of perspective of people with disabilities in AI fairness (Baker & Hawn, 2021;Trewin, 2018).”
From Exploring the role of generative AI in higher education: Semi-structured interviews with students with disabilities (2025) · cites this paper as Baker & Hawn, 2021
In the citing paper
“Research on the use of AI in education and disability is still scarce (Baker & Hawn, 2021)”
From Exploring the role of generative AI in higher education: Semi-structured interviews with students with disabilities (2025) · cites this paper as Baker & Hawn, 2021
In the citing paper
“The opinions and experiences of students with disabilities are essential because they are often overlooked (Baker & Hawn, 2021;Sullivan et al., 2023).”
From Exploring the role of generative AI in higher education: Semi-structured interviews with students with disabilities (2025) · cites this paper as Baker & Hawn, 2021
In the citing paper
“Educational uses of generative AI pose several other challenges. One is the perpetuation of biases and discrimination, potentially reinforcing racial or gender-based stereotypes during personalized learning, automated scoring, and admission processes (88, 90).”
From The impact of generative artificial intelligence on socioeconomic inequalities and policy making (2024) · cites this paper as [90]
In the citing paper
“However, the proliferation of AI in education also raises pertinent ethical concerns, including data privacy, algorithmic biases, and the digital divide (Baker & Hawn, 2021;Ifenthaler, 2023).”
From Artificial Intelligence in Education: Implications for Policymakers, Researchers, and Practitioners (2024) · cites this paper as Baker & Hawn, 2021
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Beccari, M. N., Kussler, L. M., & Oliveira, J. (2023) · SCIAS Educação Comunicação e Tecnologia · doi:10.36704/sciaseducomtec.v5i2.7896
Generative AI Soaring in the Skies
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dos Santos, L. C., Silva, L. M., da Silva, M. L. P., & Filho, S. G. D. S. (2024) · doi:10.36227/techrxiv.170956708.87181699/v1
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Who is Legion?: Surfacing Demographic Voices in Large Language ModelsPreprint
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Teachers' use of digital tools (2018)
(2023) · doi:10.1787/98dd57af-en · Cited by 1
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Pedagogical approaches, curricula and assessments for digital education
OECD (2023) · OECD eBooks · doi:10.1787/01b37cb5-en
Capacity building for digital education
OECD (2023) · OECD eBooks · doi:10.1787/532d43d1-en
Accessible, innovative and high- quality infrastructure for digital education
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Metadata
- Authors
- Baker, R. S., & Hawn, A.
- Affiliations
- University of Pennsylvania (US)
- Corresponding author
- Baker, Ryan S.
- ORCID
- Baker, Ryan S.
- Venue
- International Journal of Artificial Intelligence in Education
- Publisher
- Elsevier BV
- Published
- 2022-12
- Volume
- 32
- Issue
- 4
- Pages
- 1052-1092
- ISSN
- 1560-4292 (ISSN-L)
- OpenAlex
- W3214911045
- Semantic Scholar
- 38472f9d5ba1c93fcbc33b294ff9747a63988d3a
- Cited by
- 993
- References
- 122
- Citations by year
- 2026: 393 · 2025: 276 · 2024: 165 · 2023: 96 · 2022: 59
- FWCI
- 101.28
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Artificial Intelligence in Education (publishedVersion, cc-by-nc-nd)
- EdArXiv (OSF Preprints) (submittedVersion)
- Funders
- Schmidt Futures
- University of Pennsylvania
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- algorithmic bias, artificial intelligence in education, algorithmic fairness, bias metrics, racial and ethnic bias, gender bias, nationality bias, socioeconomic status, disability, military-connected students, educational equity, bias mitigation, machine learning pipelines
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