Context-Aware Educational Question Answering through Large Language Model-Based Learner Memory and Intent Recognition
Keywords:
large language model; educational question answering; learner memory; intent recognition; context awareness; fairness; deploymentAbstract
The integration of large language models into educational question answering promises to transform how learners access explanations, remediate misconceptions, and navigate complex subject matter. However, existing deployments often treat each query in isolation, disregarding the learner’s prior knowledge, evolving misconceptions, and pedagogical context. This paper presents a system-level investigation of context-aware educational question answering that combines large language model-based learner memory with fine-grained intent recognition. Rather than proposing a single algorithmic solution, we analyze the architectural, infrastructural, and governance dimensions of such systems as socio-technical infrastructures. We examine how persistent learner memory modules can capture knowledge states, error patterns, and learning trajectories, how intent recognition disambiguates under-specified queries in educational discourse, and how the interplay of these components shapes response quality, fairness, and scalability. The paper explores structural trade-offs among centralized memory stores, privacy-preserving edge architectures, and hybrid designs. It further addresses deployment challenges including latency constraints for real-time tutoring, the tension between model customization and generalizability, and the sustainability of large-scale inference. A thorough discussion of governance, bias mitigation, and policy compliance reveals how institutional and regulatory pressures reshape technical choices. Throughout, we emphasize the need to treat context-aware educational QA as a layered infrastructure where design decisions propagate across memory management, intent modeling, retrieval-augmented generation, and feedback loops. The analysis is grounded in cross-domain comparisons with adaptive tutoring systems, conversational AI, and information retrieval, offering forward-looking perspectives on robust, fair, and sustainable architectures for next-generation learning environments.
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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.
