Emerging technologies in education: Potential and challenges of personalization through AI and Blockchain
Article Sidebar
Main Article Content
Abstract
This article examines how learning analytics, artificial intelligence (AI), and blockchain technology are transforming the personalization of education. By exploring recent literature, it identifies the contributions and challenges of these technologies in enhancing educational pathways. The analysis suggests that the integration of these technologies offers unique opportunities for the personalization of learning, while raising important questions about security, privacy, and equity. The convergence of AI, learning analytics, and blockchain technology promises a revolution in the way education is delivered and received, allowing for precise adaptation to each learner's profile. However, this technological integration requires deep reflection on ethical and regulatory frameworks to ensure that the personalization of education benefits everyone, without compromising data security or exacerbating inequalities. The article advocates for close collaboration between technological developers, educators, and policymakers to address these challenges and fully exploit the potential of these emerging technologies in education.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
Alammary, A., Alhazmi, S., Almasri, M., et Gillani, S. (2019). Blockchain-based applications in education: A systematic review. Applied Sciences, 9(12), 2400. https://doi.org/10.3390/app9122400 DOI: https://doi.org/10.3390/app9122400
Anne, A. et El Bahlouli, Y. (2023a). Les technologies des registres distribués et de la chaîne de blocs en éducation. The Conversation. https://tinyurl.com/mu6r765r
Anne, A. et El Bahlouli, Y. (2023b). La Technologie des registres distribués (TRD) : Usages et perspectives dans le secteur de l’éducation. Médiations et médiatisations, (14). https://doi.org/10.52358/mm.vi14.307 DOI: https://doi.org/10.52358/mm.vi14.307
Arndt, T., et Guercio, A. (2020). Blockchain-based transcripts for mobile higher-education. International Journal of Information and Education Technology, 10(2). https://doi.org/10.18178/ijiet.2020.10.2.1344 DOI: https://doi.org/10.18178/ijiet.2020.10.2.1344
Ausubel, D. P. (1968). Educational Psychology: A Cognitive View. Holt, Rinehart and Winston.
Baker, R. S., et Siemens, G. (2014). Educational data mining and learning analytics. Dans R. Keith Sawyer (dir.), Cambridge Handbook of the Learning Sciences (p. 253 – 272). Cambridge University Press. DOI: https://doi.org/10.1017/CBO9781139519526.016
Bandura, A. (1977). Social Learning Theory. Prentice Hall.
Bhaskar, P., Tiwari, C. K., et Joshi, A. (2021). Blockchain in education management: present and future applications. Interactive Technology and Smart Education, 18(1), 1-17. https://doi.org/10.1108/ITSE-07-2020-0102 DOI: https://doi.org/10.1108/ITSE-07-2020-0102
Bidarra, J., et Mamede, H. (2019). Artificial Intelligence et Blockchain in Online Education. Dans G. Ubachs (dir.), The Envisioning Report for Empowering Universities (3rd ed., p. 27-29). EADTU. https://tinyurl.com/bde6zduj
Bruner, J. S. (1960). The Process of Education. Harvard University Press. DOI: https://doi.org/10.4159/9780674028999
Bryson, J. J. (2019). The Past Decade and Future of AI’s Impact on Society. Towards a New Enlightenment? A Transcendent Decade. BBVA. https://www.joannajbryson.org/publications/the-past-decade-and-future-of-ais-impact-on-society
Creswell, J. W., et Creswell, J. D. (2017). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4e éd.). Sage Publications.
Crompton, H., et Burke, D. (2023). Artificial intelligence in Higher Education: the state of the field. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00392-8 DOI: https://doi.org/10.1186/s41239-023-00392-8
Devedžić, V. (2004). Web Intelligence and Artificial Intelligence in Education. Journal of Educational Technology & Society, 7(4), 29‑39. https://www.jstor.org/stable/jeductechsoci.7.4.29
Dickler, R. (2021). Learning with and from Artificial Intelligence-Driven Analytics. Society for Learning Analytics Research (SoLAR). https://www.solaresearch.org/2021/11/learning-with-and-from-artificial-intelligence-driven-analytics/
Dillenbourg, P. (2000). Collaborative learning: Cognitive and Computational Approaches. Advances in Learning and Instruction Series. Computers & Education. https://doi.org/10.1016/S0360-1315(00)00011-7 DOI: https://doi.org/10.1016/S0360-1315(00)00011-7
Drachsler, H., et Greller, W. (2016). Privacy and analytics: It's a DELICATE issue a checklist for trusted learning analytics. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, 89–98. https://doi.org/10.1145/2883851.2883893 DOI: https://doi.org/10.1145/2883851.2883893
Engeström, Y. (1987). Learning by Expanding: An Activity-Theoretical Approach to Developmental Research. Orienta-Konsultit. https://lchc.ucsd.edu/mca/Paper/Engestrom/Learning-by-Expanding.pdf
Gašević, D., Dawson, S., et Siemens, G. (2014). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71. https://doi.org/10.1007/s11528-014-0822-x DOI: https://doi.org/10.1007/s11528-014-0822-x
Greene Nolan, H., et Vang, M. C. (2023). Automated Essay Scoring in Middle School Writing: Understanding Key Predictors of Students’ Growth and Comparing Artificial Intelligence- and Teacher-Generated Scores and Feedback [Report]. Digital Promise. https://eric.ed.gov/?id=ED629956 DOI: https://doi.org/10.51388/20.500.12265/187
Guan, X., Yan, H., Wang, Z., Gao, P., et Ding, B. (2023). Research on Teaching Reform of Artificial Intelligence Course Based on CDIO. SHS Web of Conferences, 152, Article 03005. https://doi.org/10.1051/shsconf/202315203005 DOI: https://doi.org/10.1051/shsconf/202315203005
Holmes, W., Bialik, M., et Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
HolonIQ. (2023). Artificial Intelligence in Education. 2023 Survey Insights. https://www.holoniq.com/notes/artificial-intelligence-in-education-2023-survey-insights
Inamorato Dos Santos, A. (dir.), Grech, A., et Camilleri, A. F. (2017). Blockchain in Education. Joint Research Centre. Science for Policy Report. European Commission. https://doi.org/10.2760/60649
Jacob, S., Souissi, S. et Duplantis, L. (2023). Intelligence artificielle et transformation de l'évaluation de programme. Chaire de recherche sur l’administration publique à l’ère numérique, Université Laval. https://tinyurl.com/22c6sbxr
Kabudi, T., Pappas, I. O., et Olsen, D. H. (2021). AI-enabled Adaptive Learning Systems: A systematic mapping of the literature. Computers & Education: Artificial Intelligence, 2, article 100017. https://doi.org/10.1016/j.caeai.2021.100017 DOI: https://doi.org/10.1016/j.caeai.2021.100017
Kelly, G. (1955). The Psychology of Personal Constructs. W.W. Norton & Company.
Lai, C.-L. (2021). Exploring University Students’ Preferences for AI-Assisted Learning Environment: A Drawing Analysis with Activity Theory Framework. Educational Technology & Society, 24(4), 1‑15. https://www.jstor.org/stable/48629241
Luckin, R., Holmes, W., Griffiths, M., et Forcier, L. B. (2016). Intelligence Unleashed: An argument for AI in Education. Pearson Education. https://tinyurl.com/5n88z5tu
Nagel, D. (2023). Learning Analytics and the Future of Change in the Classroom. Campus Technology. https://tinyurl.com/bdfwuynp
Papert, S. (1980). Mindstorms: Children, Computers, and Powerful Ideas. Basic Books.
Pedró, F., Subosa, M., Rivas, A. et Valverde, P. (2019). Artificial intelligence in education: challenges and opportunities for sustainable development.UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000366994
Piaget, J. (1952). The Origins of Intelligence in Children. International Universities Press, Inc. DOI: https://doi.org/10.1037/11494-000
Saastamoinen, K., Rissanen, A., et Mutanen, A. (2023). Intelligent Learning in Studying and Planning Courses—New Opportunities and Challenges for Officers. International Baltic Symposium on Science and Technology Education. Scientia Socialis Ltd. https://eric.ed.gov/?id=ED629134 DOI: https://doi.org/10.33225/BalticSTE/2023.203
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400. https://doi.org/10.1177/0002764213498851 DOI: https://doi.org/10.1177/0002764213498851
Slade, S., et Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. https://doi.org/10.1177/0002764213479366 DOI: https://doi.org/10.1177/0002764213479366
Torraco, R. J. (2016). Writing Integrative Literature Reviews: Using the Past and Present to Explore the Future. Human Resource Development Review, 15(4), 404–428. https://doi.org/10.1177/1534484316671606 DOI: https://doi.org/10.1177/1534484316671606
Truby, J. (2018). Decarbonizing Bitcoin: Law and Policy Choices for Reducing the Energy Consumption of Blockchain Technologies and Digital Currencies. Energy Research & Social Science, 44, 399-410. https://doi.org/10.1016/j.erss.2018.06.009 DOI: https://doi.org/10.1016/j.erss.2018.06.009
University of Southern California. (2021). Organizing your social sciences research paper. 6. The Methodology. USC Libraries. https://libguides.usc.edu/writingguide/methodology
University System of Georgia. (2024). University System of Georgia Working with Georgia State University’s National Institute for Student Success. https://tinyurl.com/bdcwp828
Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes. Harvard University Press.
Whittemore, R., et Knafl, K. (2005). The integrative review: updated methodology. Journal of Advanced Nursing, 52(5), 546–553. https://doi.org/10.1111/j.1365-2648.2005.03621.x DOI: https://doi.org/10.1111/j.1365-2648.2005.03621.x
Zook, M., Barocas, S., Boyd, D., Crawford, K., Keller, E., Gangadharan, S. P., Goodman, A., Hollander, R., Koenig, B. A., Metcalf, J., Narayanan, A., Nelson, A., et Pasquale, F. (2017). Ten simple rules for responsible big data research. PLoS Computational Biology, 13(3). https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005399 DOI: https://doi.org/10.1371/journal.pcbi.1005399