Adaptive Knowledge Retrieval System for Smart Education Platforms Based on Learner Behavior Modeling and AI Personalization
Keywords:
adaptive learning, knowledge retrieval, personalization, learner behavior modeling, smart education platforms, AI in educationAbstract
The proliferation of digital learning environments has generated vast repositories of educational content, yet the challenge of delivering the right knowledge resource to the right learner at the right moment remains largely unresolved. This paper presents a comprehensive system-level analysis of an adaptive knowledge retrieval framework designed for smart education platforms, integrating fine-grained learner behavior modeling with artificial intelligence (AI) driven personalization. Moving beyond static rule-based recommendation, the proposed approach constructs dynamic learner profiles from multimodal interaction traces, including clickstream sequences, time-on-task patterns, assessment performance, and self-regulation signals. A central architectural contribution is the fusion of behavior-derived cognitive state representations with context-aware knowledge retrieval mechanisms that operate over semantically indexed learning object repositories. The paper examines the structural trade-offs inherent in such a system, including the tension between global model generalization and local personalization accuracy, the computational cost of real-time inference versus offline precomputation, and the privacy implications of pervasive behavior sensing. Infrastructure considerations for cloud-native microservice deployment, model versioning, and continuous integration of learner feedback are discussed in depth. The work further addresses governance dimensions such as algorithmic fairness, accountability in automated pedagogical decisions, and the long-term sustainability of AI models under concept drift and shifting curricular landscapes. An evaluation framework is proposed that combines offline metrics, online A/B testing, and qualitative learner experience indicators, emphasizing robustness across diverse learner populations. By framing adaptive knowledge retrieval as a socio-technical system, this paper contributes a holistic perspective that balances technical performance with ethical and operational imperatives, offering a blueprint for next-generation smart education platforms.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
