Artificial intelligence (AI) and large language models (LLMs) are transforming educational technology by enabling conversational tutoring, personalized explanations, and inquiry-driven learning. However, most AI-based learning systems rely on continuous internet connectivity and cloud-based computation, limiting their use in bandwidth-constrained environments. This paper presents an offline-first large language model architecture designed for AI-assisted learning in low-connectivity settings. The system performs all inference locally using quantized language models and incorporates hardware-aware model selection to enable deployment on low-specification CPU-only devices. By removing dependence on cloud infrastructure, the system provides curriculum-aligned explanations and structured academic support through natural-language interaction. To support learners at different educational stages, the system includes adaptive response levels that generate explanations at varying levels of complexity: Simple English, Lower Secondary, Upper Secondary, and Technical. This allows explanations to be adjusted to student ability, improving clarity and understanding of academic concepts. The system was deployed in selected secondary and tertiary institutions under limited-connectivity conditions and evaluated across technical performance, usability, perceived response quality, and educational impact. Results show stable operation on legacy hardware, acceptable response times, and positive user perceptions regarding support for self-directed learning. These findings demonstrate the feasibility of offline large language model deployment for AI-assisted education in low-connectivity environments.

翻译：人工智能（AI）与大语言模型（LLM）正通过实现对话式辅导、个性化解释和探究驱动学习，变革教育技术。然而，大多数基于AI的学习系统依赖持续的互联网连接和基于云计算的计算，这限制了其在带宽受限环境中的使用。本文提出了一种为低连接环境下的AI辅助学习而设计的离线优先大语言模型架构。该系统使用量化语言模型在本地执行所有推理，并结合硬件感知的模型选择，以实现在仅配备CPU的低规格设备上的部署。通过消除对云基础设施的依赖，该系统通过自然语言交互提供与课程内容一致的解释和结构化的学术支持。为支持不同教育阶段的学习者，该系统包含自适应响应级别，可生成不同复杂程度的解释：简单英语、初中、高中和技术级别。这使得解释能够根据学生能力进行调整，从而提高对学术概念的清晰度和理解。该系统在选定的中学和高等教育机构的有限连接条件下部署，并在技术性能、可用性、感知响应质量和教育影响方面进行了评估。结果表明，系统在老旧硬件上运行稳定，响应时间可接受，并且在支持自主学习方面获得了积极的用户反馈。这些发现证明了在低连接环境下部署离线大语言模型以支持AI辅助教育的可行性。