Machine Learning for Human Learners: Opportunities, Issues, Tensions and Threats
Open accessAbstract
Abstract Machine learning systems are infiltrating our lives and are beginning to become important in our education systems. This article, developed from a synthesis and analysis of previous research, examines the implications of recent developments in machine learning for human learners and learning. In this article we first compare deep learning in computers and humans to examine their similarities and differences. Deep learning is identified as a sub-set of machine learning, which is itself a component of artificial intelligence. Deep learning often depends on backwards propagation in weighted neural networks, so is non-deterministic—the system adapts and changes through practical experience or training. This adaptive behaviour predicates the need for explainability and accountability in such systems. Accountability is the reverse of explainability. Explainability flows through the system from inputs to output (decision) whereas accountability flows backwards, from a decision to the person taking responsibility for it. Both explainability and accountability should be incorporated in machine learning system design from the outset to meet social, ethical and legislative requirements. For students to be able to understand the nature of the systems that may be supporting their own learning as well as to act as responsible citizens in contemplating the ethical issues that machine learning raises, they need to understand key aspects of machine learning systems and have opportunities to adapt and create such systems. Therefore, some changes are needed to school curricula. The article concludes with recommendations about machine learning for teachers, students, policymakers, developers and researchers.
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Webb et al. demonstrated the effectiveness of targeted training sessions in enhancing confidence and competence in integrating GenAI tools into curricula.
“For instance, Webb, Fluck [71] demonstrated the effectiveness of targeted training sessions in enhancing confidence and competence in integrating GenAI tools into curricula.”
From Generative AI in Higher Education: Balancing Innovation and Integrity (2025) · cites this paper as Webb, Fluck [71]
The Discussion and conclusion (page unavailable) recommends teacher professional development in machine learning and says teachers will need to integrate examples into curricula. It does not report targeted training sessions, measure gains in confidence or competence, or address integrating generative AI tools specifically. A recommendation is not a demonstration of effectiveness. (LLM, conf=0.83)
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,655 · see citing works
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561 · see citing works
Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018 · see citing works
ON QUALITATIVE DIFFERENCES IN LEARNING: I—OUTCOME AND PROCESS*
Marton, F., & Säljö, R. (1976) · British Journal of Educational Psychology · doi:10.1111/j.2044-8279.1976.tb02980.x · Cited by 4,748 · see citing works
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2761740 · Cited by 4,215 · cited 1× · see citing works
“Deep learning is now widely used in speech recognition and computer vision and is expected to make a large contribution in many other fields in the near future (Sze et al. 2017).”
BackgroundMachine Learning in Agriculture: A Review
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018) · Sensors · doi:10.3390/s18082674 · Cited by 3,441 · see citing works
Kaplan, A., & Haenlein, M. (2019) · Business Horizons · doi:10.1016/j.bushor.2018.08.004 · Cited by 3,140 · see citing works
Towards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121 · see citing works
Continual lifelong learning with neural networks: A review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.01.012 · Cited by 3,117 · see citing works
Deep learning and its applications to machine health monitoring
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.05.050 · Cited by 2,894 · see citing works
Machine Learning Interpretability: A Survey on Methods and Metrics
Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019) · Electronics · doi:10.3390/electronics8080832 · Cited by 1,842 · see citing works
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., et al. (2019) · Nature · doi:10.1038/s41586-019-1138-y · Cited by 1,364 · see citing works
A survey on deep learning for big data
Zhang, Q., Yang, L. T., Chen, Z., & Li, P. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.10.006 · Cited by 1,226 · see citing works
Deep learning for healthcare applications based on physiological signals: A review
Faust, O., Hagiwara, Y., Hong, T. J., Lih, O. S., & Acharya, U. R. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.005 · Cited by 1,042 · see citing works
Envisioning AI for K-12: What Should Every Child Know about AI?
Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019795 · Cited by 918 · see citing works
A Pragmatic Definition of the Concept of Theoretical Saturation
Low, J. (2019) · Sociological Focus · doi:10.1080/00380237.2018.1544514 · Cited by 445 · see citing works
Classroom assessment and pedagogy
Black, P., & Wiliam, D. (2018) · Assessment in Education: Principles, Policy & Practice · doi:10.1080/0969594x.2018.1441807 · Cited by 423 · see citing works
Cheng, L., & Yu, T. (2019) · International Journal of Energy Research · doi:10.1002/er.4333 · Cited by 380 · see citing works
Engagement detection in online learning: a review
Dewan, M. A. A., Murshed, M., & Lin, F. O. (2019) · Smart Learning Environments · doi:10.1186/s40561-018-0080-z · Cited by 353 · see citing works
Inclusive AI literacy for kids around the world
Druga, S., Vu, S. T., Likhith, E., & Qiu, T. (2019) · Proceedings of FabLearn 2019 · doi:10.1145/3311890.3311904 · Cited by 318 · see citing works
From Machine Learning to Explainable AI
Holzinger, A. (2018) · 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA) · doi:10.1109/disa.2018.8490530 · Cited by 306 · see citing works
Asikainen, H., & Gijbels, D. (2017) · Educational Psychology Review · doi:10.1007/s10648-017-9406-6 · Cited by 265 · see citing works
Simon, H. A. (1983) · Springer eBooks · doi:10.1007/978-3-662-12405-5_2 · Cited by 261 · see citing works
Botero, G. A. G., Questier, F., & Zhu, C. (2018) · Computer Assisted Language Learning · doi:10.1080/09588221.2018.1485707 · Cited by 235 · see citing works
Mehta, J. D. (2019) · Harvard University Press eBooks · doi:10.4159/9780674239951 · Cited by 235 · see citing works
Second Handbook of Information Technology in Primary and Secondary Education
(2018) · Springer international handbooks of education · doi:10.1007/978-3-319-71054-9 · Cited by 166 · see citing works
Accelerated Move for AI Education in China
Yang, X. (2019) · ECNU Review of Education · doi:10.1177/2096531119878590 · Cited by 153 · see citing works
Classroom Assessment to Support Teaching and Learning
Shepard, L. A. (2019) · The Annals of the American Academy of Political and Social Science · doi:10.1177/0002716219843818 · Cited by 135 · see citing works
Entwistle, N. J. (2005) · The Curriculum Journal · doi:10.1080/0958517042000336818 · Cited by 115 · see citing works
Ontology-based Recommender System in Higher Education
Obeid, C., Lahoud, C., Khoury, H. E., et al. (2018) · doi:10.1145/3184558.3191533 · Cited by 101 · see citing works
Encyclopedia of Education and Information Technologies
Lemay, D. J., Doleck, T., & Bazelais, P. (2020) · Springer eBooks · doi:10.1007/978-3-030-10576-1 · Cited by 96 · see citing works
Predicting Secondary School Students' Performance Utilizing a Semi-supervised Learning Approach
Livieris, I. E., Drakopoulou, K., Tampakas, V., Mikropoulos, T. A., & Pintelas, P. (2018) · Journal of Educational Computing Research · doi:10.1177/0735633117752614 · Cited by 91 · see citing works
Utilizing crowdsourcing and machine learning in education: Literature review
Al-Enezi, H., & Faisal, M. H. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10102-w · Cited by 89 · see citing works
A critique of the deep and surface approaches to learning model
Howie, P. C., & Bagnall, R. G. (2012) · Teaching in Higher Education · doi:10.1080/13562517.2012.733689 · Cited by 88 · see citing works
Competence for Students’ Future: Curriculum Change and Policy Redesign in China
Tao, W. (2019) · ECNU Review of Education · doi:10.1177/2096531119850905 · Cited by 74 · see citing works
Exploring Echo-Systems: How Algorithms Shape Immersive Media Environments
Cohen, J. N. (2018) · Journal of Media Literacy Education · doi:10.23860/jmle-2018-10-2-8 · Cited by 68 · see citing works
Detecting student emotions in computer-enabled classrooms
Bosch, N., D’Mello, S. K., Baker, R. S., Ocumpaugh, J. L., Shute, V. J., Ventura, M., Wang, L., & Zhao, W. (2016) · International Joint Conference on Artificial Intelligence · Cited by 65 · see citing works
Galliers, R. D., & Huang, J. (2011) · European Journal of Information Systems · doi:10.1057/ejis.2011.44 · Cited by 51 · see citing works
Artificial Intelligence and Inclusive Education: Speculative Futures and Emerging Practices
(2019) · Perspectives on Rethinking and Reforming Education · doi:10.1007/978-981-13-8161-4 · Cited by 51 · 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
Porayska‐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
An Integrative Framework for Artificial Intelligence Education
Pat Langley, B. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019670 · Cited by 44 · see citing works
Yu, Y., & Chen, Y. (2018) · OALib · doi:10.4236/oalib.1104898 · Cited by 38 · see citing works
Hayashi, Y. (2019) · Frontiers in Robotics and AI · doi:10.3389/frobt.2019.00024 · Cited by 37 · see citing works
In Search of Deeper Learning: The Quest to Remake the American High School
Fine, S., & Mehta, J. D. (2019) · Cited by 37 · see citing works
It’s not Magic After All – Machine Learning in Snap! using Reinforcement Learning
Jatzlau, S., Michaeli, T., Seegerer, S., & Romeike, R. (2019) · IEEE Blocks and Beyond Workshop (B&B) · doi:10.1109/bb48857.2019.8941208 · Cited by 35 · see citing works
Touretzky, D. S., Martin, F., Seehorn, D., Breazeal, C., & Posner, T. (2019) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3287324.3287525 · Cited by 34 · see citing works
Brief Review of Educational Applications Using Data Mining and Machine Learning
Nájera, A. B. U., & de la Calleja, J. (2017) · Revista Electrónica de Investigación Educativa · doi:10.24320/redie.2017.19.4.1305 · Cited by 33 · see citing works
(2017) · Springer Publishing Company eBooks · doi:10.1891/9780826140531.0128 · Cited by 25 · see citing works
Machine Learning: The State of the Art
Wang, J., & Tao, Q. (2008) · IEEE Intelligent Systems · doi:10.1109/mis.2008.107 · Cited by 23 · see citing works
“Applying machine learning to improve curriculum design,”
Ball, R., Duhadway, L., Feuz, K., Jensen, J., Rague, B., & Weidman, D. (2019) · Proceedings of the 50th ACM Technical Symposium on Computer Science Education · doi:10.1145/3287324.3287430 · Cited by 21 · see citing works
Recommending peers for learning: Matching on dissimilarity in interpretations to provoke breakdown
Rajagopal, K., Bruggen, J. M. V., & Sloep, P. (2015) · British Journal of Educational Technology · doi:10.1111/bjet.12366 · Cited by 14 · see citing works
Assessment as, for, and of Twenty-First Century Learning Using Information Technology: An Overview
Webb, M., & Ifenthaler, D. (2018) · Springer international handbooks of education · doi:10.1007/978-3-319-71054-9_37 · Cited by 14 · see citing works
Self-citationTensions in specifying computing curricula for K-12: Towards a principled approach for objectives
Webb, M., Bell, T., Davis, N. E., Katz, Y. J., Fluck, A. E., Sysło, M. M., Kalaš, I., Cox, M., Angeli, C., Malyn‐Smith, J., et al. (2018) · it - Information Technology · doi:10.1515/itit-2017-0017 · Cited by 13 · see citing works
Self-citationAdolescents’ Self-regulation During Job Interviews Through an AI Coaching Environment
Porayska‐Pomsta, K., & Chryssafidou, E. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93846-2_52 · Cited by 12 · see citing works
What Can You Do with Educational Technology that is Getting More Human?
Kloos, C. D., Alario‐Hoyos, C., Muñoz‐Merino, P. J., et al. (2019) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon.2019.8725188 · Cited by 5 · see citing works
Dumitrescu, R. M. (1970) · Journal of Comparative Research in Antropology & Sociology · Cited by 5 · see citing works
Mehta, R., Henriksen, D., & Rosenberg, J. M. (2019) · Educational leadership · Cited by 5 · see citing works
Case Study of Baiyun and Caohai Lakes Implies How to Implement Wetland Restoration/Creation
Xiao, L., Zhang, Q., Wei, G., et al. (2018) · OALib · doi:10.4236/oalib.1104896 · Cited by 4 · see citing works
Webb, M., Fluck, A. E., Deschênes, M., et al. (2019) · Figshare · Cited by 4 · see citing works
Self-citationMagenheim, J., & Schulte, C. (2020) · Encyclopedia of Education and Information Technologies · doi:10.1007/978-3-030-10576-1_253 · Cited by 4 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 5
- GDPR. (2018). Regulation (EU) 2016/679 of the European Parliament and of the Council: General Data Protection Regulation. Official Journal of the European Union, L 119/1. https ://eur-lex.europ a.eu/legal -conte nt/EN/TXT/PDF/?uri=CELEX :32016 R0679 .
- Fullan, M., Gardner, M., & Drummy, M. (2019). Going deeper. Education Leadership, 76(8), 64-68.
- Hao, K. (2020). Born in China, taught by AI. MIT Technology Review, 123(1), 24-29.
- In P. Fisser, & M. Phillips (Eds.), Learners and learning contexts: New alignments for the digital age Report of EDUsummIT 2019, Québec City, Canada, September 29th-October 2nd, 2019 (pp. 127-136)
- created professional development materials and led workshops across Canada as part of the CanCode pro- gram launched by Innovation, Science and Economic Development Canada.
Cited by
Sorted by citations
A Review of Artificial Intelligence (AI) in Education from 2010 to 2020
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021) · Complexity · doi:10.1155/2021/8812542 · Cited by 1,364 · see citing works
“[78] Discussing how time and temporality are used and inflected with the introduction of AI in education policy contexts 60 Williams [79] Analyzing implications of artificial intelligence, data analytics, and blockchain technology for the academy”
BackgroundEvaluating the quality of machine learning explanations: a survey on methods and metrics
Zhou, J., Gandomi, A. H., Chen, F., & Holzinger, A. (2021) · Electronics · doi:10.3390/electronics10050593 · Cited by 606 · see citing works
Generative AI in Higher Education: Balancing Innovation and Integrity
Francis, N. J., Jones, S., & Smith, D. P. (2025) · British Journal of Biomedical Science · doi:10.3389/bjbs.2024.14048 · Cited by 295 · see citing works
“For instance, Webb, Fluck [71] demonstrated the effectiveness of targeted training sessions in enhancing con fi dence and competence in integrating GenAI tools into curricula.”
Cain, W. (2024) · TechTrends · doi:10.1007/s11528-023-00896-0 · Cited by 232 · 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
- Authors
- Webb, M. E., Fluck, A., Magenheim, J., Malyn-Smith, J., Waters, J., Deschênes, M., & Zagami, J.
- Affiliations
- King's College London (GB)
- University of Tasmania (AU)
- Paderborn University (DE)
- Université Laval (CA)
- Griffith University (AU)
- Corresponding author
- Webb, Mary E.
- Fluck, Andrew
- Magenheim, Johannes
- Zagami, Jason
- Venue
- Educational Technology Research and Development
- Published
- 2020-11-19
- Volume
- 69
- Issue
- 4
- Pages
- 2109-2130
- ISSN
- 1042-1629 (ISSN-L)
- 1556-6501
- OpenAlex
- W3103420222
- Cited by
- 116
- References
- 63
- Citations by year
- 2026: 25 · 2025: 21 · 2024: 21 · 2023: 26 · 2022: 17 · 2021: 6
- FWCI
- 11.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Educational Technology Research and Development (publishedVersion, cc-by)
- Research Portal (King's College London) (publishedVersion, cc-by)
- UTAS Research Repository (acceptedVersion)
- Figshare (submittedVersion, cc-by)
- Griffith Research Online (submittedVersion, cc-by)
- Funders
- King's College London
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Information Systems › Digital Education and Society
- Keywords
- machine learning in education, deep learning, explainability, accountability, artificial intelligence in education, machine learning ethics, school curriculum, teacher professional development, backward propagation
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