Research on Challenges and Strategies of Students' Adaptive Learning within AI

Authors

  • Cheng Yu

DOI:

https://doi.org/10.54097/mhkpyq51

Keywords:

Adaptive learning, challenges and strategies, Artificial Intelligence (AI), personalization.

Abstract

It's acknowledged that an adaptive e-learning system can be efficient for users to conduct personalized learning in which learners could choose appropriate content and preferred models based on artificial intelligence (AI) recommendations. Nowadays, the rapid development of adaptive platforms, fueled by the evolution of machine learning and AI, incontestably brings innovative potential to the educational field. However, there are many unavoidable challenges followed by the swift process of integrating AI and other technologies into adaptive learning practices. Through the extensive review of previous research on adaptive e-learning and comprehensive analysis of their prominent outcomes, the paper focuses on exploring the challenges of adaptive learning within AI that students are facing and proposing possible solutions. It's found that adaptive learning is mainly experiencing the challenges involving how to avoid data bias and protect personal privacy, making continuous evaluations of users, lack of emotion monitoring and technological support for both students and teachers and the technology integration and inequity. To deal with the increasing problems, some practical strategies are presented, such as properly governing data for learning management, conducting continuous evaluation and prediction for users, and visualizing cognition, which can be supportive of students' professional development in adaptive learning.

Downloads

Download data is not yet available.

References

Shute V, Towle B. Adaptive e-learning. Educational Psychologist, 2003, 38 (2): 105 - 114.

Zhu Y. A knowledge graph and BiLSTM-CRF-enabled intelligent adaptive learning model and its potential application. Alexandria Engineering Journal, 2024, 91: 305 - 320.

Minn S. AI-assisted knowledge assessment techniques for adaptive learning environments. Computers and Education: Artificial Intelligence, 2022, 3: 100050.

Bailey A, Vaduganathan N, Henry T, et al. Making digital learning work: Success strategies from six leading universities and community colleges. Boston: Massachusetts: Boston Consulting Group, 2018.

Weber N. Adaptive learning: Understanding its progress and potential. Horizon Report: 2019 Higher Education Edition, 2019, 34 - 35.

Pelánek R. Adaptive Learning is Hard: Challenges, Nuances, and Trade-offs in Modeling. International Journal of Artificial Intelligence in Education, 2024, 1 - 26.

Cui X P, Xu J. Application, issues, and trends of adaptive learning technique: An Interview with Professor David Stein, Ohio State University. Open Education Research, 2019, 25 (5): 4 - 10.

David Y B, Segal A, Gal Y. Sequencing educational content in classrooms using Bayesian knowledge tracing. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, 2016, 354 - 363.

Murtaza M, Ahmed Y, Shamsi J A, et al. AI-based personalized e-learning systems: Issues, challenges, and solutions. IEEE Access, 2022, 10: 81323 - 81342.

Hubalovsky S, Hubalovska M, Musilek M. Assessment of the influence of adaptive E-learning on learning effectiveness of primary school pupils. Computers in Human Behavior, 2019, 92: 691 - 705.

Li F, He Y, Xue Q. Progress, challenges and countermeasures of adaptive learning. Educational Technology & Society, 2021, 24 (3): 238 - 255.

Fatahi S. An experimental study on an adaptive e-learning environment based on learner's personality and emotion. Education and Information Technologies, 2019, 24 (4): 2225 - 2241.

Sungkur R K, Antoaroo M A, Beeharry A. Eye tracking system for enhanced learning experiences. Education and Information Technologies, 2016, 21: 1785 - 1806.

Junokas M J, Lindgren R, Kang J, et al. Enhancing multimodal learning through personalized gesture recognition. Journal of Computer Assisted Learning, 2018, 34 (4): 350 - 357.

Mirata V, Hirt F, Bergamin P, et al. Challenges and contexts in establishing adaptive learning in higher education: findings from a Delphi study. International Journal of Educational Technology in Higher Education, 2020, 17: 1 - 25.

Tlili A, Essalmi F, Ayed L J B, et al. A smart educational game to model personality using learning analytics. 2017 IEEE 17th International conference on advanced learning technologies (ICALT). IEEE, 2017, 131 - 135.

Zou D, Xie H. Personalized word-learning based on technique feature analysis and learning analytics. Journal of Educational Technology & Society, 2018, 21 (2): 233 - 244.

Saba F, Shearer R L. Transactional Distance and Adaptive Learning: Planning for the Future of Higher Education, 2017.

Eldenfria A, Al-Samarraie H. Towards an online continuous adaptation mechanism (OCAM) for enhanced engagement: An EEG study. International Journal of Human-Computer Interaction, 2019, 35 (20): 1960 - 1974.

Lokare V T, Jadhav P M. An AI-based learning style prediction model for personalized and effective learning. Thinking Skills and Creativity, 2024, 51: 101421.

Suhaimi N S, Mountstephens J, Teo J. EEG-based emotion recognition: A state-of-the-art review of current trends and opportunities. Computational intelligence and neuroscience, 2020, 2020: 1-19.

Wang, L. P., Zhao, W., & Wei, J. H. Empirical research on open learner model in the adaptive learning system. Journal of Jilin University (Information Science Edition), 2019, 37 (5): 512 – 517.

Downloads

Published

28-09-2024

How to Cite

Yu, C. (2024). Research on Challenges and Strategies of Students’ Adaptive Learning within AI. Journal of Education, Humanities and Social Sciences, 38, 117-124. https://doi.org/10.54097/mhkpyq51