AI-Driven Personalized Learning and Its Ethical Implications for Educational Counseling
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Educational Counseling ,
Personalized Learning
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Abstract
In the contemporary digital landscape, AI-driven personalized learning has emerged as a transformative approach to enhancing educational experiences. This article examines the integration of artificial intelligence within educational settings, focusing specifically on its implications for educational counseling. By leveraging sophisticated data analytics, AI systems can customize learning materials and instructional strategies to align with the unique needs and preferences of individual learners. This tailored approach not only aims to improve academic outcomes but also fosters greater engagement and motivation among students. However, the deployment of AI in education raises significant ethical considerations. Issues such as data privacy, where sensitive student information may be compromised, and algorithmic bias, which can perpetuate inequities in learning opportunities. This research highlights both the potential benefits and inherent risks associated with AI-driven personalized learning. It advocates for a balanced perspective that emphasizes ethical practices in the implementation of technology in education. Recommendations for educational counselors include fostering digital literacy, advocating for transparent data practices, and actively participating in discussions about the ethical use of AI in educational contexts.
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References
Anand, R. (2023). An Image-based Deep Learning Approach for Personalized Outfit Selection. Proceedings of the 17th INDIACom; 2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023, Query date: 2025-02-12 22:43:05, 1050–1054.
Bang, H. J. (2023). Efficacy of an Adaptive Game-Based Math Learning App to Support Personalized Learning and Improve Early Elementary School Students’ Learning. Early Childhood Education Journal, 51(4), 717–732. https://doi.org/10.1007/s10643-022-01332-3
Bhaskaran, S. (2023). Enhanced personalized recommendation system for machine learning public datasets: Generalized modeling, simulation, significant results and analysis. International Journal of Information Technology (Singapore), 15(3), 1583–1595. https://doi.org/10.1007/s41870-023-01165-2
Chaipidech, P. (2022). A personalized learning system-supported professional training model for teachers’ TPACK development. Computers and Education: Artificial Intelligence, 3(Query date: 2025-02-12 22:43:05). https://doi.org/10.1016/j.caeai.2022.100064
Chang, Y. C. (2022). A Personalized Learning Service Compatible with Moodle E-Learning Management System. Applied Sciences (Switzerland), 12(7). https://doi.org/10.3390/app12073562
Cho, Y. J. (2023). Communication-Efficient and Model-Heterogeneous Personalized Federated Learning via Clustered Knowledge Transfer. IEEE Journal on Selected Topics in Signal Processing, 17(1), 234–247. https://doi.org/10.1109/JSTSP.2022.3231527
Dai, R. (2022). DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training. Proceedings of Machine Learning Research, 162(Query date: 2025-02-12 22:43:05), 4587–4604.
Daniels, J. (2022). A Multitask Learning Approach to Personalized Blood Glucose Prediction. IEEE Journal of Biomedical and Health Informatics, 26(1), 436–445. https://doi.org/10.1109/JBHI.2021.3100558
Ding, Z. (2023). DiffusionRig: Learning Personalized Priors for Facial Appearance Editing. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2023(Query date: 2025-02-12 22:43:05), 12736–12746. https://doi.org/10.1109/CVPR52729.2023.01225
Fariani, R. I. (2023). A Systematic Literature Review on Personalised Learning in the Higher Education Context. Technology, Knowledge and Learning, 28(2), 449–476. https://doi.org/10.1007/s10758-022-09628-4
Iyer, L. S. (2022). Advancing equity in digital classrooms: A personalized learning framework for higher education institutions. Socioeconomic Inclusion During an Era of Online Education, Query date: 2025-02-12 22:43:05, 225–245. https://doi.org/10.4018/978-1-6684-4364-4.ch011
Jacobson, N. C. (2022). Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments. Behaviour Research and Therapy, 149(Query date: 2025-02-12 22:43:05). https://doi.org/10.1016/j.brat.2021.104013
Jiang, B. (2022). Data-Driven Personalized Learning Path Planning Based on Cognitive Diagnostic Assessments in MOOCs. Applied Sciences (Switzerland), 12(8). https://doi.org/10.3390/app12083982
Kang, I. A. (2022). DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine. Applied Sciences (Switzerland), 12(6). https://doi.org/10.3390/app12063043
Kaswan, K. S. (2024). AI in personalized learning. Advances in Technological Innovations in Higher Education: Theory and Practices, Query date: 2025-02-12 22:43:05, 103–117. https://doi.org/10.1201/9781003376699-9
Langarica, S. (2023). A meta-learning approach to personalized blood glucose prediction in type 1 diabetes. Control Engineering Practice, 135(Query date: 2025-02-12 22:43:05). https://doi.org/10.1016/j.conengprac.2023.105498
Li, B. (2023). A personalized recommendation framework based on MOOC system integrating deep learning and big data. Computers and Electrical Engineering, 106(Query date: 2025-02-12 22:43:05). https://doi.org/10.1016/j.compeleceng.2022.108571
Li, C. C. (2022). Data-driven method to learning personalized individual semantics to support linguistic multi-attribute decision making. Omega (United Kingdom), 111(Query date: 2025-02-12 22:43:05). https://doi.org/10.1016/j.omega.2022.102642
Li, H. (2023). Computing personalized brain functional networks from fMRI using self-supervised deep learning. Medical Image Analysis, 85(Query date: 2025-02-12 22:43:05). https://doi.org/10.1016/j.media.2023.102756
Li, K. C. (2023). Artificial intelligence in personalised learning: A bibliometric analysis. Interactive Technology and Smart Education, 20(3), 422–445. https://doi.org/10.1108/ITSE-01-2023-0007
Lian, Z. (2023). Blockchain-Based Personalized Federated Learning for Internet of Medical Things. IEEE Transactions on Sustainable Computing, 8(4), 694–702. https://doi.org/10.1109/TSUSC.2023.3279111
Lim, L. (2023). Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning. Computers in Human Behavior, 139(Query date: 2025-02-12 22:43:05). https://doi.org/10.1016/j.chb.2022.107547
Liu, K. (2022). Development and Validation of a Personalized Model with Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records. JAMA Network Open, 5(7). https://doi.org/10.1001/jamanetworkopen.2022.19776
Liu, M. (2022). Deep reinforcement learning for personalized treatment recommendation. Statistics in Medicine, 41(20), 4034–4056. https://doi.org/10.1002/sim.9491
Liu, Q. (2022). A personalized deep learning denoising strategy for low-count PET images. Physics in Medicine and Biology, 67(14). https://doi.org/10.1088/1361-6560/ac783d
Lu, S. (2022). Decentralized Bilevel Optimization For Personalized Client Learning. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2022(Query date: 2025-02-12 22:43:05), 5543–5547. https://doi.org/10.1109/ICASSP43922.2022.9746612
Luu, L. (2022). Accurate Step Count with Generalized and Personalized Deep Learning on Accelerometer Data. Sensors, 22(11). https://doi.org/10.3390/s22113989
Ma, G. (2024). A Transfer Learning-Based Method for Personalized State of Health Estimation of Lithium-Ion Batteries. IEEE Transactions on Neural Networks and Learning Systems, 35(1), 759–769. https://doi.org/10.1109/TNNLS.2022.3176925
Mantouka, E. G. (2022). Deep Reinforcement Learning for Personalized Driving Recommendations to Mitigate Aggressiveness and Riskiness: Modeling and Impact Assessment. Transportation Research Part C: Emerging Technologies, 142(Query date: 2025-02-12 22:43:05). https://doi.org/10.1016/j.trc.2022.103770
Murtaza, M. (2022). AI-Based Personalized E-Learning Systems: Issues, Challenges, and Solutions. IEEE Access, 10(Query date: 2025-02-12 22:43:05), 81323–81342. https://doi.org/10.1109/ACCESS.2022.3193938
Musolin, M. H. (2024). Developing Personalised Islamic Learning in Digital Age: Pedagogical and Technological Integration for Open Learning Resources (OLR). Lecture Notes in Networks and Systems, 1004(Query date: 2025-02-12 22:43:05), 11–25. https://doi.org/10.1007/978-981-97-3305-7_2
Nazaretsky, T. (2022). Empowering Teachers with AI: Co-Designing a Learning Analytics Tool for Personalized Instruction in the Science Classroom. ACM International Conference Proceeding Series, Query date: 2025-02-12 22:43:05, 1–12. https://doi.org/10.1145/3506860.3506861
Okubo, F. (2023). Adaptive Learning Support System Based on Automatic Recommendation of Personalized Review Materials. IEEE Transactions on Learning Technologies, 16(1), 92–105. https://doi.org/10.1109/TLT.2022.3225206
Panjaburee, P. (2022). Acceptance of personalized e-learning systems: A case study of concept-effect relationship approach on science, technology, and mathematics courses. Journal of Computers in Education, 9(4), 681–705. https://doi.org/10.1007/s40692-021-00216-6
Prathiba, S. B. (2022). Cybertwin-Driven Federated Learning Based Personalized Service Provision for 6G-V2X. IEEE Transactions on Vehicular Technology, 71(5), 4632–4641. https://doi.org/10.1109/TVT.2021.3133291
Quesnel, F. (2022). Deep-learning-based partial pricing in a branch-and-price algorithm for personalized crew rostering. Computers and Operations Research, 138(Query date: 2025-02-12 22:43:05). https://doi.org/10.1016/j.cor.2021.105554
Raj, N. S. (2022). A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020. Journal of Computers in Education, 9(1), 113–148. https://doi.org/10.1007/s40692-021-00199-4
Salimi, Y. (2022). Deep Learning-based calculation of patient size and attenuation surrogates from localizer Image: Toward personalized chest CT protocol optimization. European Journal of Radiology, 157(Query date: 2025-02-12 22:43:05). https://doi.org/10.1016/j.ejrad.2022.110602
Sayed, W. S. (2023). AI-based adaptive personalized content presentation and exercises navigation for an effective and engaging E-learning platform. Multimedia Tools and Applications, 82(3), 3303–3333. https://doi.org/10.1007/s11042-022-13076-8
Shuvo, M. M. H. (2023). Deep Multitask Learning by Stacked Long Short-Term Memory for Predicting Personalized Blood Glucose Concentration. IEEE Journal of Biomedical and Health Informatics, 27(3), 1612–1623. https://doi.org/10.1109/JBHI.2022.3233486
Soboleva, E. V. (2022). Developing a Personalised Learning Model Based on Interactive Novels to Improve the Quality of Mathematics Education. Eurasia Journal of Mathematics, Science and Technology Education, 18(2). https://doi.org/10.29333/EJMSTE/11590
Tapalova, O. (2022). Artificial Intelligence in Education: AIEd for Personalised Learning Pathways. Electronic Journal of E-Learning, 20(5), 639–653. https://doi.org/10.34190/ejel.20.5.2597
Thai, K. P. (2022). Accelerating Early Math Learning with Research-Based Personalized Learning Games: A Cluster Randomized Controlled Trial. Journal of Research on Educational Effectiveness, 15(1), 28–51. https://doi.org/10.1080/19345747.2021.1969710
Vadapalli, S. (2022). Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine. Briefings in Bioinformatics, 23(5). https://doi.org/10.1093/bib/bbac191
Vashishth, T. K. (2024). AI-driven learning analytics for personalized feedback and assessment in higher education. Using Traditional Design Methods to Enhance AI-Driven Decision Making, Query date: 2025-02-12 22:43:05, 206–230. https://doi.org/10.4018/979-8-3693-0639-0.ch009
Wen, Z. (2023). Deep learning in digital pathology for personalized treatment plans of cancer patients. Seminars in Diagnostic Pathology, 40(2), 109–119. https://doi.org/10.1053/j.semdp.2023.02.003
Wong, B. T. m. (2023). An analysis of learning analytics in personalised learning. Journal of Computing in Higher Education, 35(3), 371–390. https://doi.org/10.1007/s12528-022-09324-3
Yang, A. C. M. (2022). Adaptive formative assessment system based on computerized adaptive testing and the learning memory cycle for personalized learning. Computers and Education: Artificial Intelligence, 3(Query date: 2025-02-12 22:43:05). https://doi.org/10.1016/j.caeai.2022.100104
Yang, X. (2023). Dynamic Personalized Federated Learning with Adaptive Differential Privacy. Advances in Neural Information Processing Systems, 36(Query date: 2025-02-12 22:43:05). https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85190101783&origin=inward
Yu, L. (2022). Energy-efficient personalized thermal comfort control in office buildings based on multi-agent deep reinforcement learning. Building and Environment, 223(Query date: 2025-02-12 22:43:05). https://doi.org/10.1016/j.buildenv.2022.109458
Zhang, Z. (2023). A personalized federated learning-based fault diagnosis method for data suffering from network attacks. Applied Intelligence, 53(19), 22834–22849. https://doi.org/10.1007/s10489-023-04753-8
Zheng, L. (2022). Effects of personalised feedback approach on knowledge building, emotions, co-regulated behavioural patterns and cognitive load in online collaborative learning. Assessment and Evaluation in Higher Education, 47(1), 109–125. https://doi.org/10.1080/02602938.2021.1883549
Zhong, L. (2023). A systematic review of personalized learning in higher education: Learning content structure, learning materials sequence, and learning readiness support. Interactive Learning Environments, 31(10), 7053–7073. https://doi.org/10.1080/10494820.2022.2061006
Zhong, M. (2022). Design of a Personalized Recommendation System for Learning Resources based on Collaborative Filtering. International Journal of Circuits, Systems and Signal Processing, 16(Query date: 2025-02-12 22:43:05), 122–131. https://doi.org/10.46300/9106.2022.16.16
Zhou, P. (2022). Are You Left Out? Are you left out? An efficient and fair federated learning for personalized profiles onwearable devices of inferior networking conditions. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 6(2). https://doi.org/10.1145/3534585
Zhou, X. (2022). 2D Federated Learning for Personalized Human Activity Recognition in Cyber-Physical-Social Systems. IEEE Transactions on Network Science and Engineering, 9(6), 3934–3944. https://doi.org/10.1109/TNSE.2022.3144699