The Role of Artificial Intelligence (AI) in Personalizing Online Learning
DOI:
https://doi.org/10.47941/jodl.1689Keywords:
Artificial Intelligence (AI), Online Learning, Personalization, Education, Ethical ConsiderationsAbstract
Purpose: The objective of this study was to examine the role of Artificial Intelligence (AI) in personalizing online learning.
Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive's time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.
Findings: The findings revealed that there exists a contextual and methodological gap relating to the role of Artificial Intelligence (AI) in personalizing online learning. Preliminary empirical review revealed the transformative potential of AI in personalizing online learning, aligning with established learning theories and offering practical applications such as adaptive content delivery and data-driven decision-making. However, the responsible and ethical use of AI remains paramount, requiring privacy safeguards and ongoing collaboration among stakeholders. This research underscores AI's capacity to make online education more engaging and effective while emphasizing the need for ongoing exploration and responsible implementation to shape the future of learning.
Unique Contribution to Theory, Practice and Policy: The Cognitive Load Theory (CLT), the Constructivist Learning Theory and Self-Determination Theory (SDT) may be used to anchor future studies on personalizing online learning. The study made the following recommendations: Incorporating artificial intelligence (AI) effectively into online learning requires institutions to integrate AI-powered personalization tools, continually monitor and improve AI systems, prioritize ethical considerations and transparency, offer professional development for educators, support research and evaluation efforts, focus on customization and scalability, and establish regular feedback mechanisms from all stakeholders. These measures collectively ensure that AI enhances online learning experiences by providing tailored content and recommendations while maintaining data privacy, ethical standards, and educator involvement, ultimately benefiting learners and educators alike.
Downloads
References
Aljohani, N. R., & Davis, H. C. (2019). A framework for personalizing online learning using big data and AI. IEEE Access, 7, 149493-149503.
Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK '12), 267-270.
Asabere, N., Boateng, R., & Nyarko, F. (2017). Personalized e-learning system in Ghana. International Journal of Advanced Research in Computer Science, 8(5), 556-560.
Chen, G., Davis, D., Lin, J., Hauff, C., & Houben, G. J. (2016). Beyond the MOOC platform: Gaining insights about learners from the social web. In Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics (pp. 15:1-15:10).
Dawson, S., Macfadyen, L., & Lockyer, L. (2019). Learning analytics at the intersection of student, teacher, and designer needs. Distance Education, 40(3), 365-367. DOI: 10.1080/01587919.2019.1604933.
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum.
Hew, K. F., & Lo, C. K. (2019). Flipped classroom improves student learning in health professions education: A meta-analysis. Medical Education, 53(1), 24-35. DOI: 10.1111/medu.13816.
Higher Education Statistics Agency (HESA). (2022). Higher Education Student Statistics: UK, 2020/21. Retrieved from https://www.hesa.ac.uk/news/21-01-2022/sb253-higher-education-student-statistics/uk.
Hirose, A., Uchida, Y., Saito, Y., & Hirokawa, S. (2019). AI-Driven Personalized Learning for Educational Data Analytics in Super Smart School. In 2019 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1628-1634).
Huang, Y. M., & Chiu, P. S. (2015). The effectiveness of a meaningful learning-based evaluation model for context-aware mobile learning. British Journal of Educational Technology, 46(2), 437-447.
Ibañez, M. B., Delgado-Kloos, C., & Sarasola, J. L. (2018). A smart conversation agent for formal education. IEEE Transactions on Learning Technologies, 11(3), 396-407. DOI: 10.1109/TLT.2017.2776693
Kizilcec, R. F., Piech, C., & Schneider, E. (2017). Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. In Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale (L@S '17), 151-160.
Klašnja-Milićević, A., Vesin, B., Ivanović, M., & Budimac, Z. (2017). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 108, 201-220.
KlÃmová, B., & Poulová, P. (2019). Intelligent personalisation of online courses using machine learning methods. In Proceedings of the 2019 Federated Conference on Computer Science and Information Systems (FedCSIS) (pp. 55-62). DOI: 10.15439/2019F44
Li, L., Zheng, Q., Zhao, L., Zuo, D., & Zhang, B. (2017). An educational resource recommendation system based on deep learning. In 2017 International Conference on Education, Culture, and Society (pp. 7-12). DOI: 10.1109/ICECS.2017.13
Liu, D., Chen, S., Liang, C., & Tsai, C. C. (2019). A review of artificial intelligence applications in online peer assessment for supporting teacher education: Issues and challenges. Educational Technology & Society, 22(4), 1-14.
Madaan, A., Ramamurthy, K. N., & Stoll, M. (2018). A framework for ethical considerations in the implementation of AI in education. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 741-747). DOI: 10.1109/IROS.2018.8593981
Means, B., Bakia, M., & Murphy, R. (2014). Learning online: What research tells us about whether, when, and how. Routledge.
Office for Students. (2020). Insight brief: Adaptive learning. Retrieved from https://www.officeforstudents.org.uk/media/0c76c744-6f62-485b-990b-02ce8e0d53e5/insight-brief-adaptive-learning.pdf.
Oliveira, F. A., Rodrigues, P., Reis, J., & Peres, R. (2020). Emotion-aware personalized e-learning: A model combining physiological signals and content recommendations. Computers & Education, 151, 103858. DOI: 10.1016/j.compedu.2020.103858
Owusu-Fordjour, C., Koomson, C. K., & Hanson, D. (2016). Developing online learning materials for mathematics education in Sub-Saharan Africa: A study of the National Open University of Nigeria. Journal of Learning for Development, 3(1), 63-76.
Romero-Hall, E., Watson, G., & Papelis, Y. (2018). Using artificial intelligence to enhance an online simulation environment for teacher education: A design case in special education assessment practices. International Journal of Designs for Learning, 9(2), 16-28.
Statista. (2022). Number of users of online language learning platforms in Japan from 2017 to 2025. https://www.statista.com/statistics/1166802/japan-online-language-learning-platform-users/
Sweller, J. (1988). Cognitive load during problem-solving: Effects on learning. Cognitive Science, 12(2), 257-285.
Tanaka, A., Kishimoto, A., & Nakamura, J. (2020). Development of an AI chatbot to enhance student engagement in online courses. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (pp. 365-369).
Vasilev, V., Aleksieva-Petrova, A., Nikolova, N., & Nikolov, R. (2021). Integration of chatbots and artificial intelligence in online education: A systematic literature review. Computers in Human Behavior, 118, 106682. DOI: 10.1016/j.chb.2021.106682
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Wang, X., Ye, J., Li, Z., & Xue, G. (2016). Course recommendation in MOOCs: A review. Journal of Educational Technology & Society, 19(2), 146-160. DOI: 10.1109/ICML.2015.366
World Bank. (2019). Facing forward: Schooling for Learning in Africa. Retrieved from https://documents.worldbank.org/en/publication/documents-reports/documentdetail/106634549364164688/facing-forward-schooling-for-learning-in-africa
Xue, G., Li, L., & Wang, Q. (2019). A survey of reinforcement learning in recommendation system. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 2539-2547). DOI: 10.1145/3357384.3357884
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Vince Willis
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.