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
Fair prediction with disparate impact: a study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · Big Data · doi:10.1089/big.2016.0047 · Cited by 2,159
The Unreasonable Effectiveness of Data
Halevy, A. Y., Norvig, P., & Pereira, F. M. B. (2009) · IEEE Intelligent Systems · doi:10.1109/mis.2009.36 · Cited by 1,776
D'Ignazio, C., & Klein, L. F. (2020) · doi:10.7551/mitpress/11805.001.0001 · Cited by 1,738
Lee, M. K. (2018) · Big Data & Society · doi:10.1177/2053951718756684 · Cited by 1,338
Friedman, B., & Nissenbaum, H. (1996) · ACM Transactions on Information Systems · doi:10.1145/230538.230561 · Cited by 1,203 · cited 1×
“Landmark early work by Friedman and Nissenbaum (1996) set a precedent for this sequential framing of bias, categorizing biases based on the context where they arise, whether from preexisting social biases, technical limitations, or use of the models by real users.”
BackgroundFairness definitions explained
Verma, S., & Rubin, J. (2018) · Proceedings of the International Workshop on Software Fairness · doi:10.1145/3194770.3194776 · Cited by 1,124
Language (Technology) is Power: A Critical Survey of “Bias” in NLP
Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.485 · Cited by 1,050 · cited 4×
“For example, a recent survey of the uses of the term bias in natural language processing across 146 papers found several areas for clarification in how authors define and write about bias, from a lack of explanation about how exactly systems were biased to confusion as to the eventual harms these forms of bias might c…”
BackgroundInfluentialFairness in Criminal Justice Risk Assessments: The State of the Art
Berk, R., Heidari, H., Jabbari, S., Kearns, M., & Roth, A. (2021) · Sociological Methods & Research · doi:10.1177/0049124118782533 · Cited by 988 · cited 1×
“…technical obstacles to the use of fairness metrics manifest in several “impossibility” results (Chouldechova, 2017; Kleinberg et al., 2017; Berk et al., 2018; Loukina et al., 2019; Lee & Kizilcec, 2020, Darlington, 1971), where satisfaction of one statistical criterion of fairness makes…”
BackgroundInherent trade-offs in the fair determination of risk scores
Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.itcs.2017.43 · Cited by 943 · cited 2×
“Specifically, technical obstacles to the use of fairness metrics manifest in several “impossibility” results (Chouldechova, 2017; Kleinberg et al., 2017; Berk et al., 2018; Loukina et al., 2019; Lee & Kizilcec, 2020, Darlington, 1971), where satisfaction of one statistical criterion of fairness…”
BackgroundHow Many Subjects?: Statistical Power Analysis in Research
KRAEMER, H. C., & Blasey, C. M. (2016) · SAGE Publications Ltd eBooks · doi:10.4135/9781483398761 · Cited by 918
AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias
Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilović, A., et al. (2019) · IBM Journal of Research and Development · doi:10.1147/jrd.2019.2942287 · Cited by 884
Discrimination in online ad delivery
Sweeney, L. (2013) · Communications of the ACM · doi:10.1145/2447976.2447990 · Cited by 783 · cited 1×
“Work by Sweeney (2013) identifies representational harms of denigration and stereotyping , where the word “criminal” was more frequently returned in online ads after searches for black-identifying first names.”
BackgroundImproving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?
Holstein, K., Wortman Vaughan, J., Daumé, H., Dudik, M., & Wallach, H. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300830 · Cited by 777
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · cited 2×
“While raising overlapping concerns with Suresh and Guttag (2020), Olteanu et al. (2019) highlight the potential for bias specifically in the analysis of social data, such as user-generated content, online behavior and networks.”
BackgroundPrediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions
Mitchell, S., Potash, E., Barocas, S., D'Amour, A., & Lum, K. (2018) · arXiv · doi:10.48550/arxiv.1811.07867 · Cited by 687
Algorithmic Fairness: Choices, Assumptions, and Definitions
Mitchell, S., Potash, E., Barocas, S., D'Amour, A., & Lum, K. (2021) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-042720-125902 · Cited by 687
Fairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646
Fairness in Machine Learning: A Survey
Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646
Data and its (dis)contents: A survey of dataset development and use in machine learning research
Paullada, A., Raji, I. D., Bender, E. M., Denton, E., & Hanna, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100336 · Cited by 548 · cited 3×
“Researchers of algorithmic bias in other domains have recommended the creation and curation of highquality reference data sets (Paullada et al., 2020).”
BackgroundAutomatic speech recognition and speech variability: A review
Benzeghiba, M., De Mori, R., Deroo, O., Dupont, S., Erbes, T., Jouvet, D., Fissore, L., Laface, P., Mertins, A., Ris, C., et al. (2007) · Speech Communication · doi:10.1016/j.specom.2007.02.006 · Cited by 529
Face Recognition Performance: Role of Demographic Information
Klare, B. F., Burge, M. J., Klontz, J. C., Vorder Bruegge, R. W., & Jain, A. K. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2214212 · Cited by 516 · cited 2×
“, 2020), to computer vision (Klare et al., 2012), to hiring (Garcia, 2016).”
BackgroundUnequal Representation and Gender Stereotypes in Image Search Results for Occupations
Kay, M., Matuszek, C., & Munson, S. A. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702520 · Cited by 509
A meta-analysis of ratee race effects in performance ratings.
Kraiger, K., & Ford, J. K. (1985) · Journal of Applied Psychology · doi:10.1037/0021-9010.70.1.56 · Cited by 376
Gender and Dialect Bias in YouTube’s Automatic Captions
Tatman, R. (2017) · Proceedings of the First ACL Workshop on Ethics in Natural Language Processing · doi:10.18653/v1/w17-1606 · Cited by 338
Algorithmic fairness in education
Kizilcec, R. F., & Lee, H. (2022) · Routledge eBooks · doi:10.4324/9780429329067-10 · Cited by 337
Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining
Hajian, S., Bonchi, F., & Castillo, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2945386 · Cited by 334
What Does Explainable AI Really Mean? A New Conceptualization of Perspectives
Doran, D., Schulz, S., & Besold, T. R. (2017) · arXiv · doi:10.48550/arxiv.1710.00794 · Cited by 333
Evaluating re-identification risks with respect to the HIPAA privacy rule
Benitez, K., & Malin, B. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.000026 · Cited by 298
50 Years of Test (Un)fairness: Lessons for Machine Learning
Hutchinson, B., & Mitchell, M. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287600 · Cited by 297
Towards a critical race methodology in algorithmic fairness
Hanna, A., Denton, R., Smart, A., & Smith-Loud, J. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372826 · Cited by 295 · cited 2×
“As feminist theory (D’ignazio & Klein, 2020), sociocultural theory (Ferrero & Barujel, 2019), and critical race theory (Hanna et al., 2020) begin to influence and improve data science, we need the ideas from these communities in AIED research as well.”
BackgroundReich, J. (2015) · Science · doi:10.1126/science.1261627 · Cited by 294
Discrimination through Optimization
Ali, M., Sapiezynski, P., Bogen, M., Korolova, A., Mislove, A., & Rieke, A. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359301 · Cited by 268 · cited 1×
“Some examples include gender and racial bias in ad delivery for housing and employment (Ali et al., 2019; Benner et al., 2019); gender bias in assigning credit limits (Knight, 2019; Telford, 2019); racial bias in sentencing decisions (Angwin et al., 2016), racial bias in identifying patients for…”
BackgroundProcedural Justice in Algorithmic Fairness
Lee, M. K., Jain, A., Jin, H., Ojha, S., & Kusbit, D. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359284 · Cited by 267
Garcia, M. (2016) · World Policy Journal · doi:10.1215/07402775-3813015 · Cited by 257 · cited 1×
“Examples of algorithmic bias cross contexts, from criminal justice (Angwin et al., 2016), to medicine (O’Reilly-Shah et al., 2020), to computer vision (Klare et al., 2012), to hiring (Garcia, 2016).”
BackgroundAdapting to When Students Game an Intelligent Tutoring System
Baker, R. S., Corbett, A. T., Koedinger, K. R., Evenson, S., Roll, I., Wagner, A. Z., Naim, M., Raspat, J., Baker, D. J., & Beck, J. E. (2006) · Lecture notes in computer science · doi:10.1007/11774303_39 · Cited by 245
Self-citationA Framework for Understanding Unintended Consequences of Machine Learning
Suresh, H., & Guttag, J. V. (2019) · Cited by 229
Evaluating the Fairness of Predictive Student Models Through Slicing Analysis
Gardner, J., Brooks, C. A., & Baker, R. S. (2019) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3303772.3303791 · Cited by 229
Self-citationAN EVALUATION OF SOME MODELS FOR CULTURE‐FAIR SELECTION
PETERSEN, N. S., & Novick, M. R. (1976) · Journal of Educational Measurement · doi:10.1111/j.1745-3984.1976.tb00178.x · Cited by 228
ANOTHER LOOK AT “CULTURAL FAIRNESS”1
DARLINGTON, R. B. (1971) · Journal of Educational Measurement · doi:10.1111/j.1745-3984.1971.tb00908.x · Cited by 199
Wolff, A., Zdráhal, Z., Nikolov, A., & Pantucek, M. (2013) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2460296.2460324 · Cited by 187
Population validity for educational data mining models: A case study in affect detection
Ocumpaugh, J., Baker, R., Gowda, S., Heffernan, N., & Heffernan, C. (2014) · British Journal of Educational Technology · doi:10.1111/bjet.12156 · Cited by 168
Self-citationAdversarial Filters of Dataset BiasesPreprint
Bras, R. J. L., Swayamdipta, S., Bhagavatula, C., Zellers, R., Peters, M. E., Sabharwal, A., & Choi, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.04108 · Cited by 165
How Drugs are Developed and Approved by the FDA: Current Process and Future Directions
Ciociola, A. A., Cohen, L. B., Kulkarni, P. M., Kefalas, C. H., Buchman, A. L., Burke, C. A., Cain, T., Connor, J., Ehrenpreis, E. D., Fang, J. C., et al. (2014) · The American Journal of Gastroenterology · doi:10.1038/ajg.2013.407 · Cited by 162
Algorithmic realism: expanding the boundaries of algorithmic thought
Green, B., & Viljoen, S. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372840 · Cited by 161
Comparison of Human and Machine Scoring of Essays: Differences by Gender, Ethnicity, and Country
Bridgeman, B., Trapani, C. S., & Attali, Y. (2012) · Applied Measurement in Education · doi:10.1080/08957347.2012.635502 · Cited by 160
Blodgett, S. L., & O'Connor, B. (2017) · arXiv · doi:10.48550/arxiv.1707.00061 · Cited by 153
Santelices, M. V., & Wilson, M. R. (2010) · Harvard Educational Review · doi:10.17763/haer.80.1.j94675w001329270 · Cited by 151
Data obfuscation: anonymity and desensitization of usable data sets
Bakken, D. E., Rarameswaran, R., Blough, D. M., Franz, A., & Palmer, T. J. (2004) · IEEE Security & Privacy · doi:10.1109/msp.2004.97 · Cited by 145
Critical analysis of the statistical and ethical implications of various definitions of test bias.
Hunter, J. E., & Schmidt, F. L. (1976) · Psychological Bulletin · doi:10.1037/0033-2909.83.6.1053 · Cited by 144
The cyclical ethical effects of using artificial intelligence in education
Dieterle, E., Dede, C., & Walker, M. (2024) · AI & SOCIETY · doi:10.1007/s00146-022-01497-w · Cited by 142
Tempelaar, D., Rienties, B., & Nguyen, Q. (2020) · PLOS ONE · doi:10.1371/journal.pone.0233977 · Cited by 137
Challenges for the Future of Educational Data Mining: The Baker Learning Analytics Prizes
Baker, R. S. (2019) · Journal of Educational Data Mining · doi:10.5281/zenodo.3554745 · Cited by 135
Self-citationToward fairness in AI for people with disabilities SBG@a research roadmap
Guo, A., Kamar, E., Vaughan, J. W., Wallach, H., & Morris, M. R. (2020) · ACM SIGACCESS Accessibility and Computing · doi:10.1145/3386296.3386298 · Cited by 128
Automated essay scoring: Psychometric guidelines and practices
Ramineni, C., & Williamson, D. M. (2013) · Assessing Writing · doi:10.1016/j.asw.2012.10.004 · Cited by 127
50 Years of Test (Un)fairness: Lessons for Machine LearningPreprint
Hutchinson, B., & Mitchell, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.10104 · Cited by 118
Boosting Tree-Assisted Multitask Deep Learning for Small Scientific Datasets
Jian, J., Wang, R., Wang, M., Gao, K., Nguyen, D. D., & Wei, G. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b01184 · Cited by 118
Woolf, B. P., Arroyo, I., Müldner, K., Burleson, W., Cooper, D. G., Dolan, R., & Christopherson, R. M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13388-6_37 · Cited by 117
Discrimination through Optimization: How Facebook's Ad Delivery Can Lead to Biased OutcomesPreprint
Ali, M., Sapieżyński, P., Bogen, M., Korolova, A., Mislove, A. E., & Rieke, A. (2019) · arXiv (Cornell University) · Cited by 116
Bias in Machine Learning -- What is it Good for?Preprint
Hellström, T., Dignum, V., & Bensch, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.00686 · Cited by 107
Stratified Sampling Using Cluster Analysis
Tipton, E. (2013) · Evaluation Review · doi:10.1177/0193841x13516324 · Cited by 106
A dynamic mixture model to detect student motivation and proficiency
Johns, J., & Woolf, B. P. (2006) · Cited by 104
Gender differences in the use and benefit of advanced learning technologies for mathematics.
Arroyo, I., Burleson, W., Tai, M., Müldner, K., & Woolf, B. P. (2013) · Journal of Educational Psychology · doi:10.1037/a0032748 · Cited by 102
Should College Dropout Prediction Models Include Protected Attributes?
Yu, R., Lee, H., & Kizilcec, R. F. (2021) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3430895.3460139 · Cited by 101
On the Applicability of Machine Learning Fairness Notions
Makhlouf, K., Zhioua, S., & Catuscia Palamidessi, A. (2021) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3468507.3468511 · Cited by 96
GRADE: Machine‐Learning Support for Graduate Admissions
Waters, A., & Miikkulainen, R. P. (2014) · AI Magazine · doi:10.1609/aimag.v35i1.2504 · Cited by 94
Race Counts: Racial and Ethnic Data on the U.S. Census and the Implications for Tracking Inequality
Strmic‐Pawl, H. V., Jackson, B. A., & Garner, S. (2017) · Sociology of Race and Ethnicity · doi:10.1177/2332649217742869 · Cited by 93
Cole, N. S., & Zieky, M. J. (2001) · Journal of Educational Measurement · doi:10.1111/j.1745-3984.2001.tb01132.x · Cited by 89
How Mastery Learning Works at Scale
RITTER, S., Yudelson, M. V., Fancsali, S. E., & Berman, S. R. (2016) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/2876034.2876039 · Cited by 88
The false promise of risk assessments: epistemic reform and the limits of fairness
Green, B. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372869 · Cited by 80
Discrimination through optimization: How Facebook's ad delivery can lead to skewed outcomes
Ali, M., Sapiezynski, P., Bogen, M., Korolova, A., Mislove, A., & Rieke, A. (2019) · arXiv · doi:10.48550/arxiv.1904.02095 · Cited by 72
Yu, R., Li, Q., Fischer, C., Doroudi, S., & Xu, D. (2020) · Educational Data Mining · Cited by 71
Towards Understanding How to Assess Help-Seeking Behavior Across Cultures
Ogan, A., Walker, E., Baker, R. S., Rodrigo, M. M. T., Soriano, J. C. A., & Jiménez-Castro, M. (2014) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-014-0034-8 · Cited by 68
Self-citationThe Effects of Culturally Congruent Educational Technologies on Student Achievement
Finkelstein, S., Yarzebinski, E., Vaughn, C., Ogan, A., & Cassell, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-39112-5_50 · Cited by 68
The many dimensions of algorithmic fairness in educational applications
Loukina, A., Madnani, N., & Zechner, K. (2019) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/w19-4401 · Cited by 64
Equity Beyond Bias in Language Technologies for Education
Mayfield, E., Madaio, M., Prabhumoye, S., Gerritsen, D., McLaughlin, B., Dixon-Román, E., & Black, A. W. (2019) · Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/w19-4446 · Cited by 62
O'Reilly-Shah, V. N., Gentry, K. R., Walters, A. M., Zivot, J., Anderson, C. T., & Tighe, P. J. (2020) · British Journal of Anaesthesia · doi:10.1016/j.bja.2020.07.040 · Cited by 62
Monitoring the performance of human and automated scores for spoken responses
Wang, Z., Zechner, K., & Sun, Y. X. (2016) · Language Testing · doi:10.1177/0265532216679451 · Cited by 61
Fairer but Not Fair Enough On the Equitability of Knowledge Tracing
Doroudi, S., & Brunskill, E. (2019) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3303772.3303838 · Cited by 60
Algorithmic bias: should students pay the price?
Smith, H. (2020) · AI & SOCIETY · doi:10.1007/s00146-020-01054-3 · Cited by 52
Evaluation of Fairness Trade-offs in Predicting Student SuccessPreprint
Lee, H., & Kizilcec, R. F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00088 · Cited by 49
Who's Learning? Using Demographics in EDM Research
Paquette, L., Ocumpaugh, J. L., Li, Z., Andres, A., & Baker, R. S. (2020) · Journal of Educational Data Mining · doi:10.5281/zenodo.4143611 · Cited by 48
Self-citationStamper, J. C., & Pardos, Z. A. (2016) · Journal of Learning Analytics · doi:10.18608/jla.2016.32.16 · Cited by 47
Algorithms, Platforms, and Ethnic Bias: An Integrative EssayPreprint
Selena, S., & Kenney, M. F. (2018) · SSRN Electronic Journal · Cited by 47
Towards Fair Educational Data Mining: A Case Study on Detecting At-Risk Students.
Hu, Q., & Rangwala, H. (2020) · Educational Data Mining · Cited by 43
Fairness in Learning Analytics: Student At-risk Prediction in Virtual Learning Environments
Riazy, S., Simbeck, K., & Schreck, V. (2020) · International Conference on Computer Supported Education · doi:10.5220/0009324100150025 · Cited by 38
Diverse Big Data and Randomized Field Experiments in MOOCs
Kizilcec, R. F., & Brooks, C. H. (2017) · Society for Learning Analytics Research (SoLAR) eBooks · doi:10.18608/hla17.018 · Cited by 33
Insight and Action Analytics: Three Case Studies to Consider.
Milliron, M. D., Malcolm, L., & Kil, D. (2014) · Research & Practice in Assessment · Cited by 31
Assistments dataset from multiple randomized controlled experiments
Selent, D., Patikorn, T., & Heffernan, N. (2016) · Proceedings of the Third (2016) ACM Conference on Learning @ Scale · doi:10.1145/2876034.2893409 · Cited by 29
Ramineni, C., & Williamson, D. M. (2018) · ETS Research Report Series · doi:10.1002/ets2.12192 · Cited by 29
Eliminating Racial Profiling in School Discipline: Cultures in Conflict
Bireda, M. R. (2002) · Medical Entomology and Zoology · Cited by 23
(2017) · Educational Data Mining · Cited by 22
Self-citationMORF: A Framework for Predictive Modeling and Replication At Scale With Privacy-Restricted MOOC Data
Gardner, J., Brooks, C. A., Andres, J. M. L., & Baker, R. S. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8621874 · Cited by 22
Self-citationCulture in Computer-Based Learning Systems: Challenges and OpportunitiesPreprint
Baker, R. S., Ogan, A., Walker, E., & Madaio, M. (2020) · EdArXiv (OSF Preprints) · doi:10.35542/osf.io/ad39g · Cited by 21
Self-citationWu, R., Xu, G., Chen, E., Liu, Q., & Ng, W. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3041021.3054156 · Cited by 19
Xia, M., Asano, Y., Williams, J. J., Qu, H., & Ma, X. (2020) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3386527.3405924 · Cited by 19
Assessing Post-hoc Explainability of the BKT Algorithm
Zhou, T., Sheng, H., & Howley, I. K. (2020) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3375627.3375856 · Cited by 17
Samei, B., Olney, A. M., Kelly, S., Nystrand, M., D’Mello, S. K., Blanchard, N., & Graesser, A. C. (2015) · Educational Data Mining · Cited by 14
Efficient Feature Embeddings for Student Classification with Variational Auto-Encoders.
Klingler, S., Wampfler, R., Käser, T., Solenthaler, B., & Groß, M. H. (2017) · Educational Data Mining · Cited by 14
Assessing the Fairness of Graduation Predictions.
Anderson, H. A., Boodhwani, A., & Baker, R. S. (2019) · Educational Data Mining · Cited by 14
Self-citationFAIRVIS: Visual Analytics for Discovering Intersectional Bias in Machine Learning
Cabrera, Á. A., Epperson, W., Hohman, F., Kahng, M., Morgenstern, J., & Chau, D. H. (2019) · 2019 IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast47406.2019.8986948 · Cited by 14
Degree of error in Bayesian knowledge tracing estimates from differences in sample sizes
Slater, S., & Baker, R. S. (2018) · Behaviormetrika · doi:10.1007/s41237-018-0072-x · Cited by 13
Self-citationRacist in the Machine: The Disturbing Implications of Algorithmic Bias
Garcia, M. (2017) · Project Muse (Johns Hopkins University) · Cited by 13
Use of Automated Scoring in Spoken Language Assessments for Test Takers With Speech Impairments
Loukina, A., & Buzick, H. M. (2017) · ETS Research Report Series · doi:10.1002/ets2.12170 · Cited by 12
Culturally adapted mathematics education with ActiveMath
Erica Melis, C., Giorgi Goguadze, A., Libbrecht, P., & Ullrich, C. (2009) · AI & Society · doi:10.1007/s00146-009-0215-4 · Cited by 10
Role of Socio-cultural Differences in Labeling Students’ Affective States
Okur, E., Aslan, S., Alyüz, N., Esme, A. A., & Baker, R. S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93843-1_27 · Cited by 10
Self-citationMachine-Learned School Dropout Early Warning at Scale.
Christie, S. T., Jarratt, D. C., Olson, L. A., & Taijala, T. T. (2019) · Educational Data Mining · Cited by 8
Accurate Measurement of Lexical Sophistication with Reference to ESL Learner Data.
Naismith, B., Han, N.-R., Juffs, A., Hill, B., & Zheng, D. (2018) · Educational Data Mining · Cited by 7
Ferrero, F., & Barujel, A. G. (2019) · Latin American Conference on Learning Technologies (LACLO) · doi:10.1109/laclo49268.2019.00038 · Cited by 7
Misrepresentations in Unfair Treatment by Santelices and Wilson
Dorans, N. J. (2010) · Harvard Educational Review · doi:10.17763/haer.80.3.l253473353686748 · Cited by 6
Modeling and Optimizing Computer-Assisted Mathematics Learning in Children
Jacober, T. K. (2014) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-010265296 · Cited by 4
Baker, R. S., Berning, A., & Gowda, S. M. (2020) · doi:10.35542/osf.io/cetxj · Cited by 3
Self-citationFeinleib, D. (2014) · Apress eBooks · doi:10.1007/978-1-4842-0040-7_13 · Cited by 1
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
Explainable Artificial Intelligence in education
Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100074 · Cited by 863
Longo, L., Brcic, M., Cabitza, F., Choi, J., Confalonieri, R., Ser, J. D., Guidotti, R., Hayashi, Y., Herrera, F., Holzinger, A., et al. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102301 · Cited by 626
AI‐driven adaptive learning for sustainable educational transformation
Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2025) · Sustainable Development · doi:10.1002/sd.3221 · Cited by 581
Holt‐Lunstad, J. (2024) · World Psychiatry · doi:10.1002/wps.21224 · Cited by 541
Memarian, B., & Doleck, T. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100152 · Cited by 416
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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