Adaptive Learning Mechanisms for Learning Management Systems: A Scoping Review and Practical Considerations

2025-12-20

AI Summary

Traditional Learning Management Systems (LMS) commonly adopt a “one-size-fits-all” architecture, limiting personalization and adaptability to individual learner needs. To address this, we conducted a large-scale systematic review (2003–2023), covering Scopus, Web of Science, and grey literature, and identified 61 relevant studies via snowball sampling. Our eight-dimensional comparative coding analysis empirically validated three persistent deficiencies: (1) underutilization of existing educational data, (2) monolithic data processing approaches, and (3) lack of end-user configurability. Building on these findings, we propose two core contributions: (1) a system-agnostic conceptual model and supporting architecture for adaptive learning mechanisms, and (2) a visualization-based authoring tool enabling low-technical-literacy educators to design and deploy custom adaptation logic. These innovations offer a practical methodology and technical pathway to mitigate vendor lock-in and empower teachers with actionable, real-time pedagogical intervention capabilities.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Deep Neural Architectures and Foundation Models
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Background: Traditional Learning Management Systems (LMS) usually offer a one-size-fits-all solution that cannot be customized to meet specific learner needs. To address this issue, adaptive learning mechanisms are integrated either by LMS-specific approaches into individual LMSs or by system-independent mechanisms into various existing LMSs to increase reusability. Objective: We conducted a systematic review of the literature addressing the following research questions. How are adaptive learning mechanisms integrated into LMSs system-independently? How are they provided, how are they specified, and on which database do they operate? A priori, we proposed three hypotheses. First, the focused adaptive learning mechanisms, rarely consider existing data. Second, they usually support a limited number of data processing mechanisms. Third, the users intended to provide them, are rarely given the ability to adapt how they work. Furthermore, to investigate the differences between system-independent and LMS-specific approaches, we also included the latter. Design: We used Scopus, Web of Science and Google Scholar for gray literature to identify 3370 papers published between 2003 and 2023 for screening, and conducted a snowball search. Results: We identified 61 relevant approaches and extracted eight variables for them through in-depth reading. The results support the proposed hypotheses. Conclusion: Based on the challenges raised by the proposed hypotheses with regard to the relevant user groups, we defined two future research directions - developing a conceptual model for the system-independent specification of adaptive learning mechanisms and a corresponding architecture for the provision, and supporting the authoring of these mechanisms by users with low technical expertise.

Problem

• Examining how adaptive learning mechanisms are integrated into LMSs independently of specific systems
• Addressing the limited use of existing data and processing mechanisms in current adaptive learning approaches
• Exploring ways to enable non-technical users to author and customize adaptive learning mechanisms

Innovation

• System-independent adaptive mechanisms for LMS reusability
• Addressing limited data processing and user adaptability
• Proposing conceptual models and architectures for user authoring