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
Citations
Cites
Mapping the Margins: Intersectionality, Identity Politics, and Violence against Women of Color
Crenshaw, K. (1991) · Stanford Law Review · doi:10.2307/1229039 · Cited by 32,792
From local explanations to global understanding with explainable AI for trees
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0138-9 · Cited by 10,551
8 Decolonizing Methodologies: Research and Indigenous Peoples
Smith, L. T. (2024) · Multilingual Matters eBooks · doi:10.21832/9781800418868-012 · Cited by 9,767
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · cited 1×
“…2019; Telford, 2019); racial bias in sentencing decisions (Angwin et al., 2016), racial bias in identifying patients for additional health care (Obermeyer et al., 2019), and -- in education -- bias in standardized testing and its resulting impact on high stakes admission decisions (Dorans,…”
BackgroundA Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812
Ucf101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A. R., & Shah, M. (2012) · arXiv · doi:10.48550/arxiv.1212.0402 · Cited by 4,575
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501
Equality of Opportunity in Supervised Learning
Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2018) · arXiv · doi:10.48550/arxiv.1803.09010 · Cited by 2,474
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., & Crawford, K. (2021) · Communications of the ACM · doi:10.1145/3458723 · Cited by 2,469
Flagged first, then by citations
Research works printed but not linked: 7
- Bird, S., Dale, R., Dorr, B. J., Gibson, B., Joseph, M. T., Kan, M. Y., Lee, D., Powley, B., Radev, D. R., & Tan, Y. F. (2008). No Title. Proceedings of the 6th International Conference on Language Resources and Evaluation, 1755–1759.
- Cramer, H., Holstein, K., Vaughan, J. W., Daumé, H., Dudik, M., Wallach, H., Reddy, S., & Jean, G.-G. [The Conference on Fairness, Accountability, and Transparency (FAT*)]. (2019). FAT* 2019 translation tutorial: Challenges of incorporating algorithmic fairness [video]. YouTube. https://youtu.be/UicKZv93SOY
- Crawford, K. [The Artificial Intelligence Channel]. (2017). The Trouble with Bias - NIPS 2017 Keynote - Kate Crawford [Video]. YouTube. https://youtu.be/fMym_BKWQzk
- Holstein, K. & Doroudi, S. (in press). Equity and artificial intelligence in education: Will “AIEd” Amplify or Alleviate Inequities in Education? Invited chapter in Porayska-Pomsta, K. & Holmes, W. (Eds.), Ethics in AIED: Who Cares? Data, algorithms, equity and biases in educational contexts. Routledge Press.
- Howley, I. (2018) If an algorithm is openly accessible, and no one can understand it, is it actually open? In Artificial Intelligence in Education Workshop on Ethics in AIED 2018.
- Li, X., Song, D., Han, M., Zhang, Y., & Kizilcec, R. F. (2021). On the limits of algorithmic prediction across the globe. arXiv preprint arXiv:2103.15212. · arXiv
- Soundarajan, S., & Clausen, D. L. (2018). Equal Protection Under the Algorithm: A Legal-Inspired Framework for Identifying Discrimination in Machine Learning. Proceedings of the 35th International Conference on Machine Learning.
Cited by
Sorted by citations
Items 201–300 of 994
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Tan, M., Lee, H., Wang, D., & Subramonyam, H. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637358 · Cited by 14
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Wieczorek, M. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00883-8 · Cited by 11
Wang, J., & Chuanbo, Z. (2025) · SAGE Open · doi:10.1177/21582440251342464 · Cited by 15
Wieczorek, M. (2025) · Learning Media and Technology · doi:10.1080/17439884.2025.2517335 · Cited by 11
Emon, M. M. H., & Chowdhury, M. S. A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3316-8.ch008 · Cited by 11
Liu, A., & Min, S. (2025) · AERA Open · doi:10.1177/23328584251374595 · Cited by 19
On the Explanation of AI-Based Student Success Prediction
Afrin, F., Hamilton, M., & Thevathyan, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08754-7_34 · Cited by 11
Moll, I., Agherdien, N., Hoosen, N., Na-Allah, D. M., Nkambule, T., Martin, C. D., Pillay, P., Martin, M., Nkomo, S. A., Phakathi, N., et al. (2022) · doi:10.4102/aosis.2022.bk376 · Cited by 12
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Cui, Y., Zeng, M. L., Du, X. K., & He, W. M. (2025) · IEEE Access · doi:10.1109/access.2025.3611367 · Cited by 18
Wambsganß, T., Su, X., Swamy, V., Neshaei, S. P., Rietsche, R., & Käser, T. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.689 · Cited by 10
Chejara, P., Prieto, L. P., Dimitriadis, Y. A., Rodríguez‐Triana, M. J., Ruiz‐Calleja, A., Kasepalu, R., & Shankar, S. K. (2024) · Journal of Learning Analytics · doi:10.18608/jla.2024.8253 · Cited by 10
Lee, H., Stinar, F., Zong, R., Valdiviejas, H., Wang, D., & Bosch, N. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713960 · Cited by 15
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Roberts, L. N., Solomon, F. N., & Cummings, R. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2728-9.ch012 · Cited by 10
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Gao, R., Ni, Q., & Hu, B. (2024) · International Conference on Intelligent Education and Computer Technology (IECT) · doi:10.1145/3687311.3687404 · Cited by 9
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Thomas, D. R., Gatz, E., Gupta, S., Lin, J., Tipper, C., & Koedinger, K. R. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-73427-4_21 · Cited by 11
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Wilder, T. L., Peercy, M. M., Orland‐Barak, L., & Wilson, S. M. (2024) · Journal of Teacher Education · doi:10.1177/00224871241284086 · Cited by 9
Sandu, N., Gide, E., Karim, S., & Singh, P. (2024) · International Conference on Information Technology Based Higher Education and Training (ITHET) · doi:10.1109/ithet61869.2024.10837623 · Cited by 10
Investigating the use of AI tools in English language learning: A phenomenological approach
Wu, X., & Annamalai, N. (2025) · Contemporary Educational Technology · doi:10.30935/cedtech/16188 · Cited by 25
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Topham, L., Atherton, P., Reynolds, T., Hussain, Y., Hussain, A. J., Kolivand, H., & Khan, W. (2025) · ACM Computing Surveys · doi:10.1145/3768312 · Cited by 9
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Messingschlager, T. V., & Appel, M. (2025) · Information Communication & Society · doi:10.1080/1369118x.2025.2584146 · Cited by 21
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Mikołajczyk, A., & Grochowski, M. (2025) · IEEE Access · doi:10.1109/access.2025.3649998 · Cited by 10
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Deho, O. B., Joksimovíc, S., Liu, L., Li, J., Zhan, C., & Liu, J. (2023) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3573051.3593381 · Cited by 9
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Sukhera, J. I. (2023) · Academic Medicine · doi:10.1097/acm.0000000000005608 · Cited by 9
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Russo, G. M., Nappo, F., Manzari, A., Cicchini, D., & Santis, S. (2025) · Business Process Management Journal · doi:10.1108/bpmj-04-2025-0455 · Cited by 7
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Xu, Z., Olson, J., Pochinki, N., Zheng, Z., & Yu, R. (2024) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3636555.3636936 · Cited by 7
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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