The Ethics of Artificial Intelligence in Education: Practices, Challenges, and Debates
Open accessAbstract
The transition of Artificial Intelligence (AI) from a lab-based science to live human contexts brings into sharp focus many historic, socio-cultural biases, inequalities, and moral dilemmas. Many questions that have been raised regarding the broader ethics of AI are also relevant for AI in Education (AIED). AIED raises further specific challenges related to the impact of its technologies on users, how such technologies might be used to reinforce or alter the way that we learn and teach, and what we, as a society and individuals, value as outcomes of education. This chapter discusses key ethical dimensions of AI and contextualises them within AIED design and engineering practices to draw connections between the AIED systems we build, the questions about human learning and development we ask, the ethics of the pedagogies we use, and the considerations of values that we promote in and through AIED within a wider socio-technical system.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Crucial AI ethics considerations are equally underexamined in AIEd.
“Crucial AI ethics considerations are equally underexamined in AIEd [60, 103].”
From AI Sensing and Intervention in Higher Education: Student Perceptions of Learning Impacts, Affective Responses, and Ethical Priorities (2026) · cites this paper as [60]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
(2018) · Cited by 3,510 · see citing works
Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
Lebovits, H. (2018) · Public Integrity · doi:10.1080/10999922.2018.1511671 · Cited by 3,318 · see citing works
The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring
BLOOM, B. S. (1984) · Educational Researcher · doi:10.3102/0013189x013006004 · Cited by 2,734 · cited 1× · see citing works
“This approach is deeply ingrained in traditional educational practices focused on school-based subject-domain teaching, which was further boosted by Bloom’s influential “2-sigma effect” studies (Bloom, 1984).”
VanLEHN, K. (2011) · Educational Psychologist · doi:10.1080/00461520.2011.611369 · Cited by 2,323 · see citing works
The Ethics of AI Ethics: An Evaluation of Guidelines
Hagendorff, T. (2020) · Minds and Machines · doi:10.1007/s11023-020-09517-8 · Cited by 1,911 · see citing works
A Unified Framework of Five Principles for AI in Society
Floridi, L., & Cowls, J. (2019) · Harvard Data Science Review · doi:10.1162/99608f92.8cd550d1 · Cited by 1,631 · cited 2× · see citing works
“…framework, data, models, and education constitute the foundational level; while at the overlaps is a second level: the ethics of data in AI (e.g., Floridi and Cowls, 2019), the ethics of data in education (e.g., Ferguson et al. 2016), and the ethics of models and algorithms applied in…”
BackgroundEthics of AI in Education: Towards a Community-Wide Framework
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., et al. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00239-1 · Cited by 1,437 · see citing works
Self-citationLanguage (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 1× · see citing works
“Bias in AI has long been the subject of research, revealing overlapping definitions and different points at which it may enter the AI development and deployment pipeline (Crawford, 2017a; Blodgett, Barocas, Daumé & Wallach 2020; Baker, Ocumpaugh & Andres, 2021).”
Background(2019) · Open Research Online (The Open University) · Cited by 1,004 · see citing works
Self-citationBaker, R. S., & Hawn, A. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00285-9 · Cited by 993 · see citing works
AI and education: guidance for policy-makers
UNESCO (2021) · doi:10.54675/pcsp7350 · Cited by 929 · 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
(2019) · Cited by 771 · see citing works
Algorithmic Bias in Autonomous Systems
Danks, D., & London, A. J. (2017) · Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/654 · Cited by 472 · see citing works
Guha, M. (2000) · Reference Reviews · doi:10.1108/rr.2000.14.4.8.171 · Cited by 302 · see citing works
On the (im)possibility of fairness
Friedler, S. A., Scheidegger, C., & Venkatasubramanian, S. (2016) · arXiv · doi:10.48550/arxiv.1609.07236 · Cited by 247 · see citing works
Research Evidence on the Use of Learning Analytics: Implications for Education Policy
Ferguson, R., Andrew Brasher, A., Clow, D., Adam, C., Hillaire, G., Mittelmeier, J., Rienties, B., Ullmann, T., & Riina, V. (2016) · Research Explorer (The University of Manchester) · doi:10.2791/955210 · Cited by 179 · see citing works
Susan Bull, A., & Kay, J. (2016) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0090-8 · Cited by 165 · see citing works
Carter, L. D., Liu, D., & Cantrell, C. (2020) · AIS Transactions on Human-Computer Interaction · doi:10.17705/1thci.00138 · Cited by 154 · see citing works
Opportunity for all? Technology and learning in lower-income families
Rideout, V. J., & Katz, V. S. (2016) · Cited by 138 · see citing works
The Wisdom of Nature: An Evolutionary Heuristic for Human Enhancement
Bostrom, N., & Sandberg, A. (2009) · doi:10.1093/oso/9780199299720.003.0019 · Cited by 135 · see citing works
Digital Technology and the Futures of Education: Towards ‘Non-Stupid’ Optimism
Facer, K., & Selwyn, N. (2021) · Bristol Research (University of Bristol) · Cited by 129 · see citing works
Chinese Number Words, Culture, and Mathematics Learning
Ng, S. S. N., & Rao, N. (2010) · Review of Educational Research · doi:10.3102/0034654310364764 · Cited by 128 · 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
Pedagogical agents for learning by teaching: Teachable agents
Blair, K. P. (2007) · Cited by 91 · see citing works
Kulynych, B., Overdorf, R., Troncoso, C., & Gürses, S. F. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3372853 · Cited by 79 · 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 UnLocke Team, Wilkinson, H. R., Smid, C. R., Morris, S., Farran, E. K., Dumontheil, I., Mayer, S., Tolmie, A. K., Bell, D., Porayska‐Pomsta, K., et al. (2019) · Journal of Cognitive Enhancement · doi:10.1007/s41465-019-00161-4 · Cited by 63 · see citing works
Self-citationWilliamson, B., Eynon, R., Knox, J., & Davies, H. (2023) · Edward Elgar Publishing eBooks · doi:10.4337/9781800375413.00037 · Cited by 57 · see citing works
Teaching Machines: The History of Personalized Learning, 2021.
Tursunkulova, I. (2022) · Canadian Journal of Learning and Technology · doi:10.21432/cjlt28248 · Cited by 52 · see citing works
AI in Education needs interpretable machine learning: Lessons from Open Learner ModellingPreprint
Conati, C., Porayska‐Pomsta, K., & Mavrikis, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.00154 · Cited by 50 · see citing works
Self-citationPorayska‐Pomsta, K., & Rajendran, G. (2019) · Perspectives on rethinking and reforming education · doi:10.1007/978-981-13-8161-4_3 · Cited by 50 · see citing works
Self-citationDesigning for “challenge” in a large‐scale adaptive literacy game for primary school children
Benton, L., Mavrikis, M., Vasalou, A., Joye, N., Sumner, E., Herbert, E., Révész, A., Symvonis, A., & Raftopoulou, C. N. (2021) · British Journal of Educational Technology · doi:10.1111/bjet.13146 · Cited by 39 · see citing works
Discrimination in the Age of AlgorithmsPreprint
Kleinberg, J. M., Ludwig, J., Mullainathan, S., & Sunstein, C. R. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3329669 · Cited by 38 · see citing works
Guldberg, K., Parsons, S., Porayska‐Pomsta, K., & Keay‐Bright, W. (2017) · British Educational Research Journal · doi:10.1002/berj.3275 · Cited by 36 · see citing works
Self-citationResponses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135)Preprint
Wu, X., & Zhang, X. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.04135 · Cited by 36 · see citing works
Designing Appropriate Learning Technologies for School vs Home Settings in Tanzanian Rural Villages
Uchidiuno, J., Yarzebinski, E., Madaio, M., Maheshwari, N., Koedinger, K., & Ogan, A. (2018) · Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies · doi:10.1145/3209811.3209881 · Cited by 34 · see citing works
Beyond "Fairness:" Structural (In)justice Lenses on AI for EducationPreprint
Madaio, M., Blodgett, S. L., Mayfield, E., & Dixon‐Román, E. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.08847 · Cited by 34 · see citing works
Understanding Potential Sources of Harm throughout the Machine Learning Life Cycle
Suresh, H., & Guttag, J. V. (2021) · doi:10.21428/2c646de5.c16a07bb · Cited by 32 · see citing works
Building an Intelligent, Authorable Serious Game for Autistic Children and Their Carers
Porayska‐Pomsta, K., Anderson, K., Bernardini, S., Guldberg, K., Smith, T. J., Kossivaki, L., Hodgins, S., & Lowe, I. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-03161-3_34 · Cited by 30 · see citing works
Self-citationEvaluation 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
The Ethics of Artificial Intelligence in Education: Practices, Challenges, and Debates
Rose, J. (2024) · The Serials Librarian · doi:10.1080/0361526x.2024.2427948 · Cited by 23 · see citing works
Gauthier, A., Porayska‐Pomsta, K., Dumontheil, I., Mayer, S., & Denis Mareschal, B. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3485168 · Cited by 20 · see citing works
Self-citationBuilding Autonomous Social Partners for Autistic Children
Bernardini, S., Porayska‐Pomsta, K., Smith, T. J., & Avramides, K. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33197-8_5 · Cited by 14 · see citing works
Self-citationTreviranus, J. (2022) · Routledge eBooks · doi:10.4324/9780429329067-3 · Cited by 14 · see citing works
Tang, Y., & Liu, Y. (2009) · Progress in brain research · doi:10.1016/s0079-6123(09)17810-x · Cited by 12 · see citing works
Rashedi, R. N., Bonnet, K. R., Schulte, R. J., Schlundt, D., Swanson, A. R., Kinsman, A., Bardett, N., Warren, Z. E., Juárez, P., Biswas, G., et al. (2020) · Proceedings. · Cited by 2 · see citing works
The UnLocke Project Team, Gauthier, A., Porayska‐Pomsta, K., & Denis Mareschal, B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-52240-7_19 · Cited by 1 · see citing works
Self-citationAn implementation of a self-improving teaching program
O’Shea, T. (1979) · Birkhäuser Basel eBooks · doi:10.1007/978-3-0348-6553-1_4 · Cited by 1 · see citing works
Flagged first, then by citations
3 of the 53 references couldn’t be matched to a known paper.
Research works printed but not linked: 24
- Barocas, S., Crawford, K., Shapiro, A. and Wallach, H. (2017). The Problem With Bias: Allocative Versus Representational Harms in Machine Learning. In SIGCIS Conference. http://meetings.sigcis.org/uploads/6/3/6/8/6368912/program.pdf (accessed 1 September 2021).
- Blikstein, P. (2018). Time to Make Hard Choices for AI in Education. Keynote at the 19 th International Conference on Artificial Intelligence in Education, London 2018.
- Bogdan, K., Overdorf, R., Troncoso, C., and Gürses, S. (2018). POTs: Protective Optimization Technologies. arXiv.org. https://arxiv.org/abs/1806.02711. · arXiv
- Bolukbasi, T, Chang, K.-W., Zou, J.Y., Saligrama, V. and Kalai, A. T. (2016). Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon and R. Garnett (eds.), Advances in Neural Information Processing Systems 29, pages 4349-4357, Curran Associates, Inc.
- Cramer, H., Vaughan, W. J., & Holstein, K. (2019). Challenges of incorporating algorithmic 'fairness' into practice. Tutorial at the ACM Conference on Fairness, Accountability, and Transparency (FAT* 2019). (accessed 1 September 2021).
- du Boulay, B. (2022). Artificial Intelligence in Education: A brief discussion about ethics. In W. Holmes and K. Porayska-Pomsta (Eds.) The Ethics of Artificial Intelligence in Education: Practices, Challenges, and Debates. Routledge.
- Floridi, L. (2013). The Ethics of Information, Oxford, Oxford University Press.
- Grawemayer, B., Mavrikis, M., Holmes, W., Gutierrez-Santos, S., Wiedmann, M., Rummel, N. (2017). Affective Learning: Improving engagement and enhancing learning with affect- aware feedback, User modelling and User-Adapted Interaction, 27(1), 119-158.
- Heaven, W D. (2020). Predictive Policing Algorithms are Racist. They need to be dismantled. MIT Technology Review, July 17, 2020 (accessed 1 September 2021).
- Holstein, K. and Doroudi, S. (2022). In W. Holmes and K. Porayska-Pomsta (Eds.) The Ethics of Artificial Intelligence in Education: Practices, Challenges, and Debates. Routledge.
- Holstein, K., Wortman Vaughan, J., Daumé III, H., Dudik, M., & Wallach, H. (2019). Improving fairness in machine learning systems: What do industry practitioners need? In Proceedings of the 2019 CHI conference on human factors in computing systems (pp. 1- 16) (accessed 1 September 2021).
- IEEE General Principles (2017) https://standards.ieee.org/content/dam/ieee- standards/standards/web/documents/other/ead1e_general_principles.pdf
- Jobin, A., Ienca, M., & Vayena, E. (2019). Artificial Intelligence: The global landscape of ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2Kant, 1785. · DOI
- Kelly, K. (2010). What Technology Wants, Penguin Books.
- Kurzweil, R. (2014). The singularity is near. In Ethics and emerging technologies (pp. 393-406). Palgrave Macmillan, London.
- Madaio, M., Yarzebinski, E., Zinszer, B., Kamath, V., Akpe, H., Seri, A.B., Tanoh, F., Hannon-Cropp, J., Cassell, J., Jasinska, K., Ogam, A. (2020). Motivations and Barriers for Family Engagement with Home Literacy Technology in Rural Communities. CHI Conference on Human Factors in Computing Systems, 2020, ACM.
- Narayanan, A. (2018). 21 Fairness Definitions and their Politics. Tutorial at the FACCT Conference, 2018. Video available at https://www.youtube.com/watch?v=jIXIuYdnyyk (accessed 1 September 2021).
- Noble, S. (2018). Algorithms of Oppression. New York, NY: NYU Press.
- Porayska-Pomsta, K., Alcorn, A M., Avramides, K., Beale, S., Bernardini, S., Foster, M-E., Frauenberger, C., Good, J., Guldberg, K., Keay-Bright, W., Kossyvaki, L., Lemon, O., Mademtzi, M., Menzies, R., Pain, H., Rajendran, G., Waller, A., Sam Wass, Smith, T.J. (2018). Blending human and artificial intelligence to support autistic children of social communication skills, TOCHI, 25(6), 1-35, ACM
- Rawls, J. (1958). Justice as Fairness, The Philosophical Review, 67(2).
- Ross, W. D. (1930). The Right and the Good. Clarendon Press. The Royal Society.
- Saxena, M., Pillai, R. K., Mostow, J. (2018). Relating Children's Automatically Detected Facial Expressions to Their Behaviour in RoboTutor. In 32 nd AAAI Conference on Artificial Intelligence.
- Sweeney, Latanya. May 1, 2013. Discrimination in Online Ad Delivery. ACM 56(5), 44-54.
- The Royal Society Workshop Report (2019): Digital technology and human transformation, November 2019 DES6259, https://royalsociety.org/- /media/policy/Publications/2020/human-transformation-summary.pdf
Cited by
Sorted by citations
Practical and ethical challenges of large language models in education: A systematic scoping review
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez‐Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13370 · Cited by 935 · see citing works
“There are ongoing debates regarding AI ethics in education with a mixture of focuses on algorithmic and human ethics among educational data mining and AI in education communities (Holmes and Porayska-Pomsta, 2022).”
BackgroundExplainable 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
Inherent Tradeoffs in the Fair Determination of Risk Scores
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603197 · Cited by 347 · see citing works
Bulathwela, S., Pérez-Ortiz, M., Holloway, C., Cukurova, M., & Shawe-Taylor, J. (2024) · Sustainability · doi:10.3390/su16020781 · Cited by 269 · see citing works
Zhang, J., & Zhang, Z. (2024) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12988 · Cited by 263 · see citing works
Artificial intelligence for low income countries
Khan, M. S., Umer, H., & Faruqe, F. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03947-w · Cited by 210 · see citing works
Proactive and reactive engagement of artificial intelligence methods for education: a review
Mallik, S., & Gangopadhyay, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1151391 · Cited by 139 · see citing works
The optimistic future of Artificial Intelligence in higher education
Escotet, M. Á. (2024) · PROSPECTS · doi:10.1007/s11125-023-09642-z · Cited by 107 · see citing works
Kurian, N. C. (2024) · Learning Media and Technology · doi:10.1080/17439884.2024.2367052 · Cited by 95 · see citing works
Shaping integrity: why generative artificial intelligence does not have to undermine education
Tan, M. J. T., & Maravilla, N. M. A. T. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1471224 · Cited by 78 · see citing works
Woodruff, K. A., Hutson, J., & Arnone, K. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.1002741 · Cited by 76 · see citing works
Tuomi, I. (2022) · European Journal of Education · doi:10.1111/ejed.12531 · Cited by 66 · see citing works
Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603203 · Cited by 67 · 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
Alam, A., & Mohanty, A. (2022) · Communications in Computer and Information Science · doi:10.1007/978-3-031-23233-6_2 · Cited by 62 · see citing works
From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer
Tripathi, S., Tabari, A., Mansur, A., Dabbara, H., Bridge, C. P., & Daye, D. (2024) · Diagnostics · doi:10.3390/diagnostics14020174 · Cited by 64 · see citing works
Baker, B., Mills, K. A., McDonald, P., & Wang, L. (2023) · Teachers College Record The Voice of Scholarship in Education · doi:10.1177/01614681231191291 · Cited by 61 · see citing works
Furey, H., & Martin, F. (2019) · AI Matters · doi:10.1145/3299758.3299764 · Cited by 60 · see citing works
Lan, G., Feng, X., Du, S., Song, F., & Xiao, Q. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13427-6 · Cited by 71 · see citing works
Mulaudzi, L. V., & Hamilton, J. (2025) · Journal of Academic Ethics · doi:10.1007/s10805-025-09615-1 · Cited by 56 · see citing works
Sevnarayan, K., & Potter, M.-A. (2024) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2024.7.1.41 · Cited by 64 · see citing works
Lin, M. P.-C., Liu, A. L., Poitras, E., Chang, M., & Chang, D. (2024) · Sustainability · doi:10.3390/su16208992 · Cited by 47 · see citing works
Shi, L. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13684-5 · Cited by 46 · see citing works
Wu, H., Li, D., & Mo, X. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13312-8 · Cited by 52 · see citing works
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603207 · Cited by 49 · see citing works
Where does AI-driven Education, in the Chinese Context and Beyond, go next?
Yuan, L. Y. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00341-6 · Cited by 47 · see citing works
Generative AI in Education: Technical Foundations, Applications, and Challenges
Rashid, S. F., Duong‐Trung, N., & Niels Pinkwart, C. (2024) · Artificial intelligence · doi:10.5772/intechopen.1005402 · Cited by 53 · see citing works
Küçükuncular, A., & Ertugan, A. (2025) · Sustainability · doi:10.3390/su17167405 · Cited by 36 · see citing works
Explainable Artificial Intelligence in Education: A Comprehensive Review
Chaushi, B. A., Selimi, B., Chaushi, A., & Apostolova, M. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44067-0_3 · Cited by 39 · see citing works
Overview of Current AI Technologies in Education
Hubballi, R. B., Selvakumar, P., Seenivasan, R., Aradhya, S. G. B., Dinesh, N., & Seelam, P. K. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-8744-3.ch001 · Cited by 33 · see citing works
Human‐Centred Learning Analytics: 2019–24
Shum, S. B., Martínez‐Maldonado, R., Dimitriadis, Y. A., & Santos, P. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13442 · Cited by 32 · see citing works
Hacia un marco ético de la inteligencia artificial en la educación
Rodríguez, A. M. A. (2024) · Teoría de la Educación Revista Interuniversitaria · doi:10.14201/teri.31821 · Cited by 31 · see citing works
Lelescu, A., Sava, S., Grosseck, G., & Maliţa, L. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-06253-1 · Cited by 37 · see citing works
Augmented Education in the Global Age
Araya, D., & Marber, P. (2023) · Routledge eBooks · doi:10.4324/9781003230762 · Cited by 31 · see citing works
A Manifesto for a Pro-Actively Responsible AI in Education
Porayska‐Pomsta, K. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00346-1 · Cited by 30 · see citing works
Self-citationThe Role of ChatGPT in Education: Applications, Challenges: Insights From a Systematic Review
Dimeli, M., & Κώστας, Α. (2025) · Journal of Information Technology Education Research · doi:10.28945/5422 · Cited by 50 · see citing works
Towards a Postdigital Social Contract for Higher Education in the Age of Artificial Intelligence
Hayes, S., Jandrić, P., & Green, B. J. (2024) · Postdigital Science and Education · doi:10.1007/s42438-024-00459-3 · Cited by 24 · see citing works
Novoa-Echaurren, Á., Pavez, I., & Anabalón, M. E. (2025) · Education Sciences · doi:10.3390/educsci15060643 · Cited by 40 · see citing works
Unpacking the ethics of using AI in primary and secondary education: a systematic literature review
Wieczorek, M., Hosseini, M., & Gordijn, B. (2025) · AI and Ethics · doi:10.1007/s43681-025-00770-0 · Cited by 34 · see citing works
New Vistas on Responsible Learning Analytics
Pargman, T. C., McGrath, C., Viberg, O., & Knight, S. (2023) · Journal of Learning Analytics · doi:10.18608/jla.2023.7781 · Cited by 24 · see citing works
Ethical Use of Generative Artificial Intelligence Among Ecuadorian University Students
Buele, J., García, Á. R. S., Sabando-García, B. J., & Yánez-Rueda, H. (2025) · Sustainability · doi:10.3390/su17104435 · Cited by 21 · see citing works
Responsible AI in Education: Understanding Teachers’ Priorities and Contextual Challenges
Yin, Y., Karumbaiah, S., & Acquaye, S. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732176 · Cited by 21 · see citing works
Co-producing AIED Ethics Under Lockdown: an Empirical Study of Deliberative Democracy in Action
Swist, T., Shum, S. B., & Gulson, K. Ν. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00380-z · Cited by 20 · see citing works
Jaldemark, J., Lundin, J., Säljö, R., Edwards, J., Gegenfurtner, A., Holmes, W., Järvelä, S., de Laat, M. F., Lindberg, Y., Littlejohn, A. H., et al. (2025) · Postdigital Science and Education · doi:10.1007/s42438-025-00602-8 · Cited by 34 · see citing works
Self-citationThe Externalities of Exploration and How Data Diversity Helps Exploitation
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603208 · Cited by 20 · see citing works
Teaching with the algorithm: anticipations of AI in teacher education
Stenberg, C.-J., Åkerfeldt, A., Stenliden, L., & McGrath, C. (2025) · European Journal of Teacher Education · doi:10.1080/02619768.2025.2540793 · Cited by 21 · see citing works
AIED: From Cognitive Simulations to Learning Engineering, with Humans in the Middle
McNamara, D. S. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00349-y · Cited by 18 · see citing works
Selection Problems in the Presence of Implicit Bias
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603209 · Cited by 21 · see citing works
Designing a model of sustainable education based on artificial intelligence in higher education
Bagherimajd, K., & Khajedad, K. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100439 · Cited by 32 · see citing works
Exploring AI Technology in Grammar Performance Testing for Children with Learning Disabilities
Katsarou, D. V., Mantsos, E., Papadopoulou, S. L., Sofologi, M., Efthymiou, E., Vasileiou, I., Megari, K., Theodoratou, M., & Kougioumtzis, G. Α. (2025) · Education Sciences · doi:10.3390/educsci15030351 · Cited by 19 · see citing works
Farooq, U., Malik, S. A., & Syed, A. S. (2024) · ShodhKosh Journal of Visual and Performing Arts · doi:10.29121/shodhkosh.v5.i3.2024.3239 · Cited by 20 · see citing works
Capano, G., He, A. J., & McMinn, S. (2025) · Journal of Asian Public Policy · doi:10.1080/17516234.2025.2450571 · Cited by 16 · see citing works
Serdenia, J. R. C., Dumagay, A. H., Balasa, K. A., Capacio, E. A., & Lauzon, L. D. S. (2025) · LatIA · doi:10.62486/latia2025313 · Cited by 16 · see citing works
The Future Trajectory of the AIED Community: Defining the ‘Knowledge Tradition’ in Critical Times
Eynon, R. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00354-1 · Cited by 14 · see citing works
Mrabet, J., & Studholme, R. (2023) · International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) · doi:10.1109/iccike58312.2023.10131713 · Cited by 13 · see citing works
Feltáró szakirodalmi áttekintés a mesterséges intelligencia oktatási használatáról
Horváth, L. A. (2023) · Pannon Digitális Pedagógia (E-Tanulás – Távoktatás – Oktatás-informatika) · doi:10.56665/padipe.2023.1.1 · Cited by 17 · see citing works
Broadening horizons of the possible in education
Beghetto, R. A. (2023) · Possibility Studies & Society · doi:10.1177/27538699231182014 · Cited by 12 · see citing works
The Impact of Digital Safety Competence on Cognitive Competence, AI Self-Efficacy, and Character
Ma, C. M. S., Shek, D. T. L., Fan, I. Y. H., Zhu, X., & Hu, X. (2025) · Applied Sciences · doi:10.3390/app15105440 · Cited by 16 · see citing works
AI and ChatGPT in Higher Education: Greek Students’ Perceived Practices, Benefits, and Challenges
Κώστας, Α., Paraschou, V., Spanos, D., Tzortzoglou, F., & Sofos, A. (2025) · Education Sciences · doi:10.3390/educsci15050605 · Cited by 12 · see citing works
Artificial Intelligence Educational Pedagogy Development
Matthew, U. O., Kazaure, J. S., Ndukwu, C. C., Ebong, G. N., Nwanakwaugwu, A. C., & Nwamouh, U. C. (2024) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-2314-4.ch003 · Cited by 12 · see citing works
Foundation Models for Education: Promises and Prospects
Xu, T., Tong, R. J., Liang, J., Fan, X., Li, H., & Wen, Q. (2024) · IEEE Intelligent Systems · doi:10.1109/mis.2024.3398191 · Cited by 11 · see citing works
AI’s impact on science education: a study of ant and bee mindsets in UAE science classrooms
Alarabi, K., Alabidi, S., Tairab, H. H., Pagani, L. S., AlSadrani, B., & Khurma, O. A. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1577285 · Cited by 13 · see citing works
Wang, J., & Chuanbo, Z. (2025) · SAGE Open · doi:10.1177/21582440251342464 · Cited by 13 · see citing works
Uso de la inteligencia artificial en la dirección de centros educativos
Jiménez-Tuza, S. B. (2025) · Revista Científica Zambos. · doi:10.69484/rcz/v4/n1/86 · Cited by 10 · see citing works
Generative Artificial Intelligence (AI) on Sustainable Development Goal 4 for Tertiary Education
Matthew, U. O., Oyekunle, D. O. T., Akpan, E. E., Oladipupo, M. A., Chukwuebuka, E. S., Adekunle, T. S., Waliu, A. O., & Onumaku, V. C. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2418-9.ch010 · Cited by 10 · see citing works
Advancing pancreatic cancer management: the role of artificial intelligence in diagnosis and therapy
Ozsahin, D. U., Usanase, N., & Özşahin, İ. (2025) · Beni-Suef University Journal of Basic and Applied Sciences · doi:10.1186/s43088-025-00610-4 · Cited by 10 · see citing works
Ethical Guidelines for the thoughtful Implementation of AI in Higher Education
Peddi, S., & Manoharan, G. (2025) · EthAIca · doi:10.56294/ai2026409 · Cited by 13 · see citing works
Charting the futures of artificial intelligence in education
Tuomi, I., Holmes, W., & Miller, R. (2022) · European Journal of Education · doi:10.1111/ejed.12530 · Cited by 10 · see citing works
Self-citationNavigating the Grey Area: Students' Ethical Dilemmas in Using AI Tools for Coding Assignments
Mutanga, M. B., Lecheko, M., & Revesai, Z. (2024) · IJIE (Indonesian Journal of Informatics Education) · doi:10.20961/ijie.v8i1.90385 · Cited by 10 · see citing works
Pérez-Jorge, D., González-Afonso, M. C., Santos-Álvarez, A. G., Plasencia-Carballo, Z., & Perdomo-López, C. D. L. Á. (2025) · Information · doi:10.3390/info16070540 · Cited by 9 · see citing works
Generative AI in Terms of Its Ethical Problems for Both Teachers and Learners
Le-Nguyen, H.-T., & Tran, T. T. (2023) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-0074-9.ch006 · Cited by 12 · see citing works
Lee, Y.-L., & Lui, N. P. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1447015 · Cited by 11 · see citing works
Ortega, M., Castillo, A. M. M., Luzuriaga, E. E. L. L. L., Altamirano, H. C. Z., & Nazareno, M. M. C. (2025) · Arandu-UTIC. · doi:10.69639/arandu.v12i1.694 · Cited by 9 · see citing works
Ethical considerations and data privacy in artificial intelligence
Gaisie, E., Owusu-Boateng, O., Yidana, I., & Ghansah, B. (2025) · doi:10.70593/978-93-49307-53-7_5 · Cited by 9 · see citing works
Navigating Ethical Dilemmas in AI-Enhanced Education
Li, J., & Huang, Q. (2025) · International Journal of Knowledge Management · doi:10.4018/ijkm.382384 · Cited by 8 · see citing works
Towards A Postdigital Social Contract for Higher Education in the Age of Artificial Intelligence
Hayes, S., Jandrić, P., & Green, B. (2025) · Postdigital science and education · doi:10.1007/978-3-031-99247-6_6 · Cited by 8 · see citing works
Pedagogy, Cognition, Human Rights, and Social Justice
du Boulay, B. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00355-0 · Cited by 7 · see citing works
Digital Assessment in Higher Education: Sustainable Trends and Emerging Frontiers in the AI Era
Lelescu, A., & Kabiraj, S. (2024) · Lecture notes in educational technology · doi:10.1007/978-981-97-6136-4_2 · Cited by 6 · see citing works
Adaptation of Artificial Intelligence Literacy Scale into Turkish: A Sample of Pre-Service Teachers
Uğraş, H., Doğan, M., & Uğraş, M. (2024) · e-Kafkas Eğitim Araştırmaları Dergisi · doi:10.30900/kafkasegt.1429630 · Cited by 9 · see citing works
Chilean Teachers’ Knowledge of and Experience with Artificial Intelligence as a Pedagogical Tool
Alé-Silva, J., Ávalos, B., & Araya, R. (2025) · Education Sciences · doi:10.3390/educsci15101268 · Cited by 8 · see citing works
Exploring pre-service teachers’ perspectives on integrating artificial intelligence in education
Panagou, D., Stylos, G., & Kotsis, K. Τ. (2025) · Journal of Digital Educational Technology · doi:10.30935/jdet/17297 · Cited by 9 · see citing works
Yokuş, G. (2025) · Sustainability · doi:10.3390/su17219815 · Cited by 6 · see citing works
(2024) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets18093 · Cited by 6 · see citing works
Devanadera, A. C. (2025) · LatIA · doi:10.62486/latia2025249 · Cited by 10 · see citing works
Artificial Intelligence, Virtual, and Augmented Reality in Lifelong Learning
Abuzir, Y. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-1410-4.ch004 · Cited by 5 · see citing works
Developing high-quality, practical, and ethical automated L2 speaking assessments
de Jong, N. H., Raaijmakers, S. A., & Tigelaar, D. E. H. (2025) · System · doi:10.1016/j.system.2025.103796 · Cited by 9 · see citing works
Reshaping the Future of Learning Disabilities in Higher Education with AI
Patibandla, R. S. M. L., Rao, B. T., Rao, D. M., & Murty, M. R. (2024) · Applied Intelligence and Informatics. · doi:10.1007/978-981-97-0914-4_2 · Cited by 5 · see citing works
Murray, D., & Williams, K. (2023) · ASCILITE Publications · doi:10.14742/apubs.2023.662 · Cited by 3 · see citing works
Yin, Y., Karumbaiah, S., Lim, J., Acquaye, S., & Schwartz, S. (2025) · Computer-supported collaborative learning/The Computer-Supported Collaborative Learning Conference · doi:10.22318/cscl2025.817482 · Cited by 4 · see citing works
AI Smart-Enabled and Machine Learning Intensifies Smart Multimedia Platforms
Sreekanthaswamy, N., Chib, S., Maddel, M., Khande, R., Kalshetti, P., Selvakumar, P., & Manjunath, T. C. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-8915-7.ch002 · Cited by 4 · see citing works
Ethical AI for language assessment: Principles, considerations, and emerging tensions
Galaczi, E. D., & Pastorino-Campos, C. (2025) · Annual Review of Applied Linguistics · doi:10.1017/s0267190525100081 · Cited by 4 · see citing works
GPTs and the Choice Architecture of Pedagogies in Vocational Education
Scott, H., & Dwight, A. (2025) · Systems · doi:10.3390/systems13100872 · Cited by 4 · see citing works
Teaching in Today’s World: A Marxian Critique of AI in Education
Küçükuncular, A., & Ertugan, A. (2025) · Journal of Academic Ethics · doi:10.1007/s10805-025-09692-2 · Cited by 4 · see citing works
Beyond ChatGPT: a review of the use of AI tools in biological education
Cotton, P. A., & Cotton, D. (2026) · Journal of Biological Education · doi:10.1080/00219266.2026.2628797 · Cited by 4 · see citing works
DA FORMA ÉTICA À AÇÃO ÉTICA: RISCOS NA ADOÇÃO DA INTELIGÊNCIA ARTIFICIAL EM SALA DE AULA
Essenfelder, R., de Araújo, D. L., & César, T. C. (2023) · Editora BAGAI eBooks · doi:10.37008/978-65-5368-316-7.07.12.23.6 · Cited by 4 · see citing works
Handling the AI Employment Landscape
Satone, K. N., Ulhe, P., Deshmukh, A. S., & Mandurkar, L. (2025) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-8191-5.ch011 · Cited by 4 · see citing works
AI Tools for Education: The Development of a Free Asynchronous Course
Rodafinos, A. (2024) · Studies in educational leadership · doi:10.1007/978-3-031-51575-0_12 · Cited by 4 · see citing works
The Role of Artificial Intelligence in Higher Education: The Case of Armenia
Atoyan, V., Brosius, L., Movsisyan, N., Օհանյան, Ս., & Հովյան, Վ. (2025) · PS Political Science & Politics · doi:10.1017/s104909652400115x · Cited by 4 · see citing works
Zhang, J., & Jiang, Z. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-8881-5.ch012 · Cited by 4 · see citing works
Leveraging AI in Education: Benefits, Responsibilities, and Trends
Fitas, R., Ghosh, K., & Maity, S. (2025) · Signals and communication technology · doi:10.1007/978-3-031-96855-6_7 · Cited by 4 · see citing works
- Authors
- Holmes, W., & Porayska-Pomsta, K.
- Affiliations
- University College London (GB)
- ORCID
- Holmes, Wayne
- Venue
- Routledge eBooks
- Publisher
- Routledge
- Published
- 2022-08-11
- OpenAlex
- W4301626130
- Cited by
- 258
- References
- 53
- Citations by year
- 2026: 66 · 2025: 83 · 2024: 45 · 2023: 59 · 2022: 4 · 2019: 1
- FWCI
- 67.15
- Type
- book
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Routledge eBooks (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- 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 › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- artificial intelligence in education, AI ethics, educational technology design, educational inequality, pedagogy, educational values, socio-technical systems
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 ›
Are you an author, or do you see a problem with this page? Request a review