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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AI systems may replicate social and cultural inequalities embedded in training data, influencing assessment outcomes and learning opportunities.
“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
ChatGPT's apparent fluency can encourage students to skip ethical consideration or critical examination, particularly when its biases remain hidden.
“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
Algorithmic harms are often amplified in educational systems that rely heavily on predictive modelling for assessments, admissions, or instructional personalisation.
“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
Automation may subtly displace critical judgment, especially in repetitive tasks or environments with ambiguous accountability structures.
“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
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.
“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
When GenAI use is not accompanied by ethical framing, structured experimentation, and critical reflection, GenAI tools may encourage automation bias, epistemic shallowness, and dependency.
“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
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.
“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
Researchers have highlighted the lack of perspective of people with disabilities in AI fairness.
“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
Research on the use of AI in education and disability is still scarce.
“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
The opinions and experiences of students with disabilities are essential because they are often overlooked.
“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
Educational uses of generative AI pose several other challenges, one being the perpetuation of biases and discrimination, potentially reinforcing racial or gender-based stereotypes during personalized learning, automated scoring, and admission processes.
“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]
The proliferation of AI in education raises pertinent ethical concerns, including data privacy, algorithmic biases, and the digital divide.
“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
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 29,585 · see citing works
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 · see citing works
8 Decolonizing Methodologies: Research and Indigenous Peoples
Smith, L. T. (2024) · Multilingual Matters eBooks · doi:10.21832/9781800418868-012 · Cited by 9,765 · see citing works
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× · see citing works
“…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 · see citing works
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 · see citing works
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 · see citing works
Equality of Opportunity in Supervised Learning
Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
D'Ignazio, C., & Klein, L. F. (2020) · doi:10.7551/mitpress/11805.001.0001 · Cited by 1,738 · see citing works
Lee, M. K. (2018) · Big Data & Society · doi:10.1177/2053951718756684 · Cited by 1,338 · see citing works
Friedman, B., & Nissenbaum, H. (1996) · ACM Transactions on Information Systems · doi:10.1145/230538.230561 · Cited by 1,203 · cited 1× · see citing works
“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 · see citing works
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× · see citing works
“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 · see citing works
Inherent 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× · see citing works
“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 · see citing works
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 · see citing works
Discrimination in online ad delivery
Sweeney, L. (2013) · Communications of the ACM · doi:10.1145/2447976.2447990 · Cited by 783 · see citing works
Improving 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 · see citing works
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× · see citing works
“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 · see citing works
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 · see citing works
Fairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646 · see citing works
Fairness in Machine Learning: A Survey
Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646 · see citing works
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× · see citing works
“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 · see citing works
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× · see citing works
“, 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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Algorithmic fairness in education
Kizilcec, R. F., & Lee, H. (2022) · Routledge eBooks · doi:10.4324/9780429329067-10 · Cited by 302 · see citing works
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 · see citing works
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 · see citing works
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× · see citing works
“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 · see citing works
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× · see citing works
“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…”
BackgroundGarcia, M. (2016) · World Policy Journal · doi:10.1215/07402775-3813015 · Cited by 257 · cited 1× · see citing works
“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 · see citing works
Self-citationProcedural 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 236 · see citing works
A Framework for Understanding Unintended Consequences of Machine Learning
Suresh, H., & Guttag, J. V. (2019) · Cited by 229 · see citing works
AN 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 · see citing works
ANOTHER LOOK AT “CULTURAL FAIRNESS”<sup>1</sup>
DARLINGTON, R. B. (1971) · Journal of Educational Measurement · doi:10.1111/j.1745-3984.1971.tb00908.x · Cited by 199 · see citing works
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 193 · see citing works
Self-citationWolff, A., Zdráhal, Z., Nikolov, A., & Pantucek, M. (2013) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2460296.2460324 · Cited by 187 · see citing works
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 · see citing works
Self-citationHow 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 · see citing works
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 · see citing works
Adversarial 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 155 · see citing works
Blodgett, S. L., & O'Connor, B. (2017) · arXiv · doi:10.48550/arxiv.1707.00061 · Cited by 153 · see citing works
Santelices, M. V., & Wilson, M. R. (2010) · Harvard Educational Review · doi:10.17763/haer.80.1.j94675w001329270 · Cited by 146 · see citing works
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 · see citing works
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 144 · see citing works
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 144 · see citing works
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 · see citing works
Tempelaar, D., Rienties, B., & Nguyen, Q. (2020) · PLOS ONE · doi:10.1371/journal.pone.0233977 · Cited by 137 · see citing works
Toward 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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 112 · see citing works
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 106 · see citing works
Self-citationA dynamic mixture model to detect student motivation and proficiency
Johns, J., & Woolf, B. P. (2006) · Cited by 104 · see citing works
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 · see citing works
GRADE: Machine‐Learning Support for Graduate Admissions
Waters, A., & Miikkulainen, R. P. (2014) · AI Magazine · doi:10.1609/aimag.v35i1.2504 · Cited by 94 · see citing works
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 · see citing works
Stratified Sampling Using Cluster Analysis
Tipton, E. (2013) · Evaluation Review · doi:10.1177/0193841x13516324 · Cited by 91 · see citing works
Cole, N. S., & Zieky, M. J. (2001) · Journal of Educational Measurement · doi:10.1111/j.1745-3984.2001.tb01132.x · Cited by 89 · see citing works
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 84 · see citing works
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 83 · see citing works
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 · see citing works
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 78 · see citing works
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 · see citing works
Yu, R., Li, Q., Fischer, C., Doroudi, S., & Xu, D. (2020) · Educational Data Mining · Cited by 71 · see citing works
The 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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 60 · see citing works
Self-citationFairer 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 · see citing works
Algorithmic bias: should students pay the price?
Smith, H. (2020) · AI & SOCIETY · doi:10.1007/s00146-020-01054-3 · Cited by 52 · see citing works
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 48 · see citing works
Algorithms, Platforms, and Ethnic Bias: An Integrative EssayPreprint
Selena, S., & Kenney, M. F. (2018) · SSRN Electronic Journal · Cited by 47 · see citing works
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 46 · see citing works
Self-citationStamper, J. C., & Pardos, Z. A. (2016) · Journal of Learning Analytics · doi:10.18608/jla.2016.32.16 · Cited by 45 · see citing works
Towards Fair Educational Data Mining: A Case Study on Detecting At-Risk Students.
Hu, Q., & Rangwala, H. (2020) · Educational Data Mining · Cited by 43 · see citing works
ASSISTments Dataset from Multiple Randomized Controlled Experiments
Selent, D., Patikorn, T., & Heffernan, N. T. (2016) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/2876034.2893409 · Cited by 39 · see citing works
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 · see citing works
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 · see citing works
Insight and Action Analytics: Three Case Studies to Consider.
Milliron, M. D., Malcolm, L., & Kil, D. (2014) · Research & Practice in Assessment · Cited by 31 · see citing works
Ramineni, C., & Williamson, D. M. (2018) · ETS Research Report Series · doi:10.1002/ets2.12192 · Cited by 29 · see citing works
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 24 · see citing works
Eliminating Racial Profiling in School Discipline: Cultures in Conflict
Bireda, M. R. (2002) · Medical Entomology and Zoology · Cited by 23 · see citing works
(2017) · Educational Data Mining · Cited by 22 · see citing works
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 · see citing works
Self-citationXia, 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 · see citing works
Culture in Computer-Based Learning Systems: Challenges and OpportunitiesPreprint
Baker, R. S., Ogan, A., Walker, E., & Madaio, M. (2020) · doi:10.35542/osf.io/ad39g · Cited by 19 · see citing works
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 18 · see citing works
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 · see citing works
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 · see citing works
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 14 · see citing works
Self-citationAssessing the Fairness of Graduation Predictions.
Anderson, H. A., Boodhwani, A., & Baker, R. S. (2019) · Educational Data Mining · Cited by 14 · see citing works
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 · see citing works
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 13 · see citing works
Racist in the Machine: The Disturbing Implications of Algorithmic Bias
Garcia, M. (2017) · Project Muse (Johns Hopkins University) · Cited by 13 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Ferrero, F., & Barujel, A. G. (2019) · Latin American Conference on Learning Technologies (LACLO) · doi:10.1109/laclo49268.2019.00038 · Cited by 7 · see citing works
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 · see citing works
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 · see citing works
Baker, R. S., Berning, A., & Gowda, S. M. (2020) · doi:10.35542/osf.io/cetxj · Cited by 3 · see citing works
Self-citationFeinleib, D. (2014) · Apress eBooks · doi:10.1007/978-1-4842-0040-7_13 · Cited by 1 · see citing works
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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 · see citing works
Longo, L., Brčić, 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 648 · see citing works
<scp>AI</scp>‐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 · see citing works
Holt‐Lunstad, J. (2024) · World Psychiatry · doi:10.1002/wps.21224 · Cited by 498 · see citing works
Memarian, B., & Doleck, T. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100152 · Cited by 416 · see citing works
Barrett, A., & Pack, A. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00427-0 · Cited by 391 · see citing works
The impact of generative artificial intelligence on socioeconomic inequalities and policy making
Capraro, V., Lentsch, A., Acemoglu, D., Akgun, S., Akhmedova, A., Bilancini, E., Bonnefon, J.-F., Brañas-Garza, P., Butera, L., Douglas, K. M., et al. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae191 · Cited by 371 · see citing works
REVOLUTIONIZING EDUCATION THROUGH AI: A COMPREHENSIVE REVIEW OF ENHANCING LEARNING EXPERIENCES
Onesi-Ozigagun, O., Ololade, Y. J., Eyo-Udo, N. L., & Ogundipe, D. O. (2024) · International Journal of Applied Research in Social Sciences · doi:10.51594/ijarss.v6i4.1011 · Cited by 366 · see citing works
Cukurova, M. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13514 · Cited by 302 · see citing works
The Ethics of Artificial Intelligence in Education: Practices, Challenges, and Debates
Holmes, W., & Porayska-Pomsta, K. (2022) · doi:10.4324/9780429329067 · Cited by 258 · see citing works
Artificial Intelligence in Education: Implications for Policymakers, Researchers, and Practitioners
Ifenthaler, D., Majumdar, R., Gorissen, P., Judge, M., Mishra, S., Raffaghelli, J., & Shimada, A. (2024) · Technology, Knowledge and Learning · doi:10.1007/s10758-024-09747-0 · Cited by 238 · see citing works
Artificial Intelligence and the Transformation of Higher Education Institutions: A Systems Approach
Katsamakas, E., Pavlov, O. V., & Saklad, R. (2024) · Sustainability · doi:10.3390/su16146118 · Cited by 195 · see citing works
ED-AI Lit: An Interdisciplinary Framework for AI Literacy in Education
Allen, L. K., & Kendeou, P. (2023) · Policy Insights from the Behavioral and Brain Sciences · doi:10.1177/23727322231220339 · Cited by 217 · see citing works
The ethical implications of using generative chatbots in higher education
Williams, R. T. (2024) · Frontiers in Education · doi:10.3389/feduc.2023.1331607 · Cited by 183 · see citing works
Digital equity and inclusion in education
Gottschalk, F., & Weise, C. (2023) · OECD education working papers · doi:10.1787/7cb15030-en · Cited by 229 · see citing works
Nguyen, A., Kremantzis, M., Essien, A., Petrounias, I., & Hosseini, S. (2024) · Journal of University Teaching and Learning Practice · doi:10.53761/caraaq92 · Cited by 146 · see citing works
Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework
Rana, M. M., Siddiqee, M. S., Sakib, M. N., & Ahamed, M. R. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e37569 · Cited by 143 · see citing works
Pack, A., & Maloney, J. S. (2023) · TESOL Quarterly · doi:10.1002/tesq.3253 · Cited by 140 · see citing works
The ethics of using AI in K-12 education: a systematic literature review
Gouseti, A., James, F., Fallin, L., & Burden, K. (2025) · Technology, Pedagogy and Education · doi:10.1080/1475939x.2024.2428601 · Cited by 141 · see citing works
Ethical and regulatory challenges of Generative AI in education: a systematic review
García-López, I. M., & Trujillo-Liñán, L. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1565938 · Cited by 144 · see citing works
“For example, while previous studies have explored issues such as algorithmic biases (Baker and Hawn, 2022; Smith, 2020) or automated feedback (Bauer et al., 2023), this analysis highlights the need for an interdisciplinary approach that combines legal governance, digital literacy and clear ethical…”
Artificial intelligence and education governance
Filgueiras, F. D. B. (2023) · Education Citizenship and Social Justice · doi:10.1177/17461979231160674 · Cited by 150 · see citing works
The life cycle of large language models in education: A framework for understanding sources of bias
Lee, J., Hicke, Y., Yu, R., Brooks, C., & Kizilcec, R. F. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13505 · Cited by 119 · see citing works
Opesemowo, O. A. G., & Adekomaya, V. (2024) · International Journal of Learning Teaching and Educational Research · doi:10.26803/ijlter.23.3.4 · Cited by 116 · see citing works
Incorporating <scp>AI</scp> and learning analytics to build trustworthy peer assessment systems
Darvishi, A., Khosravi, H., Sadiq, S., & Gašević, D. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13233 · Cited by 120 · see citing works
Propagation of societal gender inequality by internet search algorithms
Vlasceanu, M., & Amodio, D. M. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2204529119 · Cited by 109 · see citing works
Lavidas, K., Voulgari, I., Papadakis, S., Athanassopoulos, S., Anastasiou, A., Filippidi, A., Komis, V., & Karacapilidis, N. I. (2024) · Information · doi:10.3390/info15060314 · Cited by 165 · see citing works
González-Sendino, R., Serrano, E., & Bajo, J. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.02.023 · Cited by 111 · see citing works
Arizmendi, C. J., Bernacki, M. L., Raković, M., Plumley, R. D., Urban, C. J., Panter, A. T., Greene, J. A., & Gates, K. M. (2022) · Behavior Research Methods · doi:10.3758/s13428-022-01939-9 · Cited by 127 · see citing works
Using Artificial Intelligence in TESOL: Some Ethical and Pedagogical Considerations
Pack, A., & Maloney, J. S. (2024) · TESOL Quarterly · doi:10.1002/tesq.3320 · Cited by 122 · see citing works
Bell, A., Solano-Kamaiko, I., Nov, O., & Stoyanovich, J. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533090 · Cited by 103 · see citing works
Advancing algorithmic bias management capabilities in AI-driven marketing analytics research
Akter, S., Sultana, S., Mariani, M. M., Wamba, S. F., Spanaki, K., & Dwivedi, Y. K. (2023) · Industrial Marketing Management · doi:10.1016/j.indmarman.2023.08.013 · Cited by 116 · see citing works
A Scoping Survey of ChatGPT in Mathematics Education
Pepin, B., Buchholtz, N. F., & Salinas-Hernández, U. (2025) · Digital Experiences in Mathematics Education · doi:10.1007/s40751-025-00172-1 · Cited by 138 · see citing works
Roshanaei, M. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12605-2 · Cited by 84 · see citing works
Wang, C., Li, Z., & Bonk, C. J. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100247 · Cited by 117 · see citing works
Farrow, R. (2023) · Learning, Media and Technology · doi:10.1080/17439884.2023.2185630 · Cited by 81 · see citing works
Artificial Intelligence: An Untapped Opportunity for Equity and Access in STEM Education
Kohnke, S., & Zaugg, T. (2025) · Education Sciences · doi:10.3390/educsci15010068 · Cited by 77 · see citing works
Perspectives of Generative AI in Chemistry Education Within the TPACK Framework
Feldman-Maggor, Y., Blonder, R., & Alexandron, G. (2024) · Journal of Science Education and Technology · doi:10.1007/s10956-024-10147-3 · Cited by 100 · see citing works
Critical GenAI Literacy: Postdigital Configurations
Rapanta, C., Bhatt, I., Bozkurt, A., Chubb, L. A., Erb, C., Forsler, I., Gravett, K., Koole, M. L., Lintner, T., Örtegren, A., et al. (2025) · Postdigital Science and Education · doi:10.1007/s42438-025-00573-w · Cited by 87 · see citing works
Samuel, Y., Brennan‐Tonetta, M., Samuel, J., Kashyap, R. K., Kumar, V., Kaashyap, S. K., Chidipothu, N., Anand, I., & Jain, P. H. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1198180 · Cited by 69 · see citing works
Deho, O. B., Zhan, C., Li, J., Liu, J., Liu, L., & Le, T. D. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13217 · Cited by 69 · see citing works
Jiang, Y., Hao, J., Fauss, M., & Li, C. (2024) · Computers & Education · doi:10.1016/j.compedu.2024.105070 · Cited by 68 · see citing works
O’Dea, X., Tsz Kit Ng, D., O’Dea, M., & Shkuratskyy, V. (2026) · Policy Futures in Education · doi:10.1177/14782103241287401 · Cited by 69 · see citing works
A systematic literature review of data privacy and security research on smart tourism
Gong, Y., & Schroeder, A. (2022) · Tourism Management Perspectives · doi:10.1016/j.tmp.2022.101019 · Cited by 74 · see citing works
Ramnarain, U. D., Ogegbo, A. A., Penn, M., Ojetunde, S. M., & Mdlalose, N. (2024) · Journal of Science Education and Technology · doi:10.1007/s10956-024-10159-z · Cited by 92 · see citing works
Explainable AI In Education : Current Trends, Challenges, And Opportunities
Rachha, A., & Seyam, M. (2023) · SoutheastCon 2023 · doi:10.1109/southeastcon51012.2023.10115140 · Cited by 68 · see citing works
Viberg, O., Kizilcec, R. F., Wise, A. F., Jivet, I., & Nixon, N. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13507 · Cited by 85 · see citing works
Tuomi, I. (2022) · European Journal of Education · doi:10.1111/ejed.12531 · Cited by 66 · see citing works
Buele, J., & Llerena-Aguirre, L. A. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1603763 · Cited by 64 · see citing works
Sha, L., Rakovic, M., Das, A., Gasevic, D., & Chen, G. (2022) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2022.3196278 · Cited by 67 · see citing works
Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review
Pierrès, O., Christen, M., Schmitt, F., & Darvishy, A. (2024) · IEEE Access · doi:10.1109/access.2024.3365368 · Cited by 72 · see citing works
Artificial intelligence in multimodal learning analytics: a systematic literature review
Mohammadi, M., Tajik, E., Martinez-Maldonado, R., Sadiq, S., Tomaszewski, W., & Khosravi, H. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100426 · Cited by 85 · see citing works
Empathic pedagogical conversational agents: A systematic literature review
Ochoa, E. G. O., Arguedas, M., & Daradoumis, Τ. (2023) · British Journal of Educational Technology · doi:10.1111/bjet.13413 · Cited by 71 · see citing works
Wang, D., Bian, C., & Chen, G. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13466 · Cited by 70 · see citing works
Gender biases within Artificial Intelligence and ChatGPT: Evidence, Sources of Biases and Solutions
Ho, J. Q. H., Hartanto, A., Koh, A., & Majeed, N. M. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100145 · Cited by 74 · see citing works
Debiasing Education Algorithms
Idowu, J. A. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00389-4 · Cited by 84 · see citing works
Multimodality of AI for Education: Toward Artificial General Intelligence
Lee, G., Shi, L., Latif, E., Gao, Y., Bewersdorff, A., Nyaaba, M., Guo, S., Liu, Z., Mai, G., Liu, T., et al. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3574466 · Cited by 145 · see citing works
Allison, J., Hwang, G., Mayer, R. E., Πέλλας, Ν., Karnalim, O., de Freitas, S., Ng, O., Huang, Y., Hooshyar, D., Seidman, R. H., et al. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251359964 · Cited by 71 · see citing works
Methodological and Technological Advancements in E-Learning
Dritsas, E., & Τρίγκα, Μ. (2025) · Information · doi:10.3390/info16010056 · Cited by 93 · see citing works
León, C. E. Y., Lipuma, J. M., & Torres, X. O. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1619888 · Cited by 76 · see citing works
AI-Powered E-Learning for Lifelong Learners: Impact on Performance and Knowledge Application
Ahn, H. Y. (2024) · Sustainability · doi:10.3390/su16209066 · Cited by 56 · see citing works
El–Sappagh, S., Alonso, J. M., Abuhmed, T., Ali, F., & Bugarín, A. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10415-5 · Cited by 54 · see citing works
They shall be fair, transparent, and robust: auditing learning analytics systems
Simbeck, K. (2023) · AI and Ethics · doi:10.1007/s43681-023-00292-7 · Cited by 58 · see citing works
Using artificial intelligence in sustainability teaching and learning
Filho, W. L., Kim, E., Borsatto, J. M. L. S., & Marcolin, C. B. (2025) · Environmental Sciences Europe · doi:10.1186/s12302-025-01159-w · Cited by 49 · see citing works
Pierrès, O., Darvishy, A., & Christen, M. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13134-8 · Cited by 48 · see citing works
“Research on the use of AI in education and disability is still scarce (Baker & Hawn, 2021) and it is not clear how sharing disability status could impact students with impairments.”
InfluentialDelcker, J., Heil, J., & Ifenthaler, D. (2024) · Educational Technology Research and Development · doi:10.1007/s11423-024-10418-1 · Cited by 49 · see citing works
Using fair AI to predict students’ math learning outcomes in an online platform
Li, C., Xing, W., & Leite, W. L. (2022) · Interactive Learning Environments · doi:10.1080/10494820.2022.2115076 · Cited by 47 · see citing works
Bird, K., Castleman, B., & Song, Y. (2024) · Journal of Policy Analysis and Management · doi:10.1002/pam.22569 · Cited by 46 · see citing works
Ahmed, W. M., Azhari, A. A., Alfaraj, A., Alhamadani, A., Zhang, M., & Lu, C. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e28198 · Cited by 48 · see citing works
Generative AI and Its Educational Implications
Łodzikowski, K., Foltz, P. W., & Behrens, J. T. (2024) · Postdigital Science and Education · doi:10.1007/978-3-031-64487-0_2 · Cited by 45 · see citing works
Hybrid Models for Knowledge Tracing: A Systematic Literature Review
Zanellati, A., Mitri, D. D., Gabbrielli, M., & Levrini, O. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2023.3348690 · Cited by 50 · see citing works
Enhancing children’s understanding of algorithmic biases in and with text-to-image generative AI
Vartiainen, H., Kahila, J., Tedre, M., López‐Pernas, S., & Pope, N. (2024) · New Media & Society · doi:10.1177/14614448241252820 · Cited by 54 · see citing works
Rana, V., Verhoeven, B., & Sharma, M. (2025) · Journal of University Teaching and Learning Practice · doi:10.53761/tjse2f36 · Cited by 48 · see citing works
Moral Machines or Tyranny of the Majority? A Systematic Review on Predictive Bias in Education
Li, L., Sha, L., Li, Y., Raković, M., Rong, J., Joksimovíc, S., Selwyn, N., Gašević, D., & Chen, G. (2023) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3576050.3576119 · Cited by 43 · see citing works
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Harkins-Brown, A. R., Carling, L. Z., & Peloff, D. C. (2025) · Encyclopedia · doi:10.3390/encyclopedia5010011 · Cited by 41 · see citing works
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Mangal, M., & Pardos, Z. A. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13484 · Cited by 47 · see citing works
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Singh, B. P., & Joshi, A. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-2964-1.ch010 · Cited by 41 · see citing works
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Bao, L., Huang, D., & Lin, C. (2024) · Management Science · doi:10.1287/mnsc.2022.02787 · Cited by 39 · see citing works
Chen, Z. (2024) · Human Resource Development Review · doi:10.1177/15344843241273316 · Cited by 41 · see citing works
Renkema, M., & Tursunbayeva, A. (2024) · Futures · doi:10.1016/j.futures.2024.103453 · Cited by 46 · see citing works
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Ahmad, I., Sharma, S., Singh, R., Gehlot, A., Gupta, L. R., Thakur, A. K., Priyadarshi, N., & Twala, B. (2024) · Cogent Education · doi:10.1080/2331186x.2024.2330235 · Cited by 37 · see citing works
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Idowu, J. A., Koshiyama, A., & Treleaven, P. C. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100267 · Cited by 38 · see citing works
Lillywhite, B., & Wolbring, G. (2022) · Sustainability · doi:10.3390/su142013519 · Cited by 36 · see citing works
Al-Abri, A. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13484-x · Cited by 44 · see citing works
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Deho, O. B., Joksimovic, S., Li, J., Zhan, C., Liu, J., & Liu, L. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2022.3226474 · Cited by 35 · see citing works
Bozdağ, A. A. (2023) · Online Journal of Communication and Media Technologies · doi:10.30935/ojcmt/13414 · Cited by 40 · see citing works
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Khalil, M., Prinsloo, P., & Slade, S. (2023) · Journal of Learning Analytics · doi:10.18608/jla.2023.7983 · Cited by 35 · see citing works
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İlikhan, S. U., Özer, M., Tanberkan, H., & Bozkurt, V. (2024) · TURKISH JOURNAL OF MEDICAL SCIENCES · doi:10.55730/1300-0144.5814 · Cited by 38 · see citing works
Anderson, J. E., Nguyen, C. A., & Moreira, G. (2025) · The International Journal of Information and Learning Technology · doi:10.1108/ijilt-10-2024-0240 · Cited by 33 · see citing works
Hlosta, M., Herodotou, C., Papathoma, T., Gillespie, A., & Bergamin, P. B. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100108 · Cited by 32 · see citing works
Prinsloo, P., Khalil, M., & Slade, S. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13508 · Cited by 32 · see citing works
Why explainable AI may not be enough: predictions and mispredictions in decision making in education
Saqr, M., & López‐Pernas, S. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00343-4 · Cited by 32 · see citing works
Han, B., Nawaz, S., Buchanan, G., & McKay, D. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00456-4 · Cited by 32 · see citing works
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Lacmanović, S., & Škare, M. (2025) · Review of Accounting and Finance · doi:10.1108/raf-01-2025-0006 · Cited by 36 · see citing works
Arévalo-Cordovilla, F. E., & Peña, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-15388-9 · Cited by 32 · see citing works
Hao, X., Demir, E., & Eyers, D. R. (2025) · Sustainable Futures · doi:10.1016/j.sftr.2025.101152 · Cited by 32 · see citing works
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Ghosh, M. (2025) · Cogent Business & Management · doi:10.1080/23311975.2025.2551809 · Cited by 41 · see citing works
Warr, M., & Heath, M. K. (2025) · Journal of Teacher Education · doi:10.1177/00224871251325073 · Cited by 35 · see citing works
Fairness for machine learning software in education: A systematic mapping study
Pham, N., Ngoc, H. P., & Nguyen‐Duc, A. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112244 · Cited by 42 · see citing works
Harvey, E., Koenecke, A., & Kizilcec, R. F. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713210 · Cited by 37 · see citing works
- 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 · 2021: 4
- 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
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Information Systems › 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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