Information Systems Faculty Publications
Document Type
Article
Publication Date
11-2025
Abstract
Adaptive learning, a personalized educational approach, has appeared as a substitute paradigm to conventional teaching methodologies. Opposed to instruction-based learning, adaptive learning prepares learning content in a way that corresponds to individual learner needs, increasing engagement and knowledge retention. The present study has been conducted to review the literature to evaluate the influence of adaptive learning systems on long-term knowledge retention as compared to their traditional counterparts. Real-time feedback, spaced repetition, and scaffolded content can reduce cognitive load and enhance the learning experience, as they are considered highly effective tools. Several studies have shown that retention improves through the use of adaptive systems, as they help fill information gaps and encourage active learning, especially in STEM fields. Despite the benefits of using adaptive systems in relevant areas, some challenges remain, including limited access in low-resource settings, underrepresentation in non-STEM areas, and difficulties integrating with traditional teaching methods. The present research suggests that future studies should concentrate on longitudinal studies, hybrid models, and equitable access to adaptive technologies. Adaptive learning will revolutionize the learning sector in various situations by addressing these challenges.
Recommended Citation
Altalhi, A., Bonke, C. and Alblowi, K., 2025. Adaptive vs. Traditional Learning: Long-Term Knowledge Retention-A Literature Review. Qubahan Academic Journal, 5(4), pp.322-331. https://doi.org/10.48161/qaj.v5n4a2165
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Publication Title
Qubahan Academic Journal
DOI
10.48161/qaj.v5n4a2165
Comments
Copyright (c) 2025 Qubahan Academic Journal
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.