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、个性化解释及探究式学习推动教育技术变革。然而，多数基于AI的学习系统依赖持续网络连接与云端计算，限制了其在带宽受限环境中的应用。本文提出一种专为低连接场景下AI辅助学习设计的离线优先大语言模型架构。该系统通过量化语言模型在本地完成全部推理任务，并采用硬件感知模型选择策略，使其能够在仅配备CPU的低规格设备上部署。通过消除对云基础设施的依赖，系统可通过自然语言交互提供与课程内容相符的解释及结构化学术支持。为适配不同教育阶段的学习者，系统包含自适应响应层级，可生成四种复杂程度的解释文本：简易英语、初中水平、高中水平及专业水平。这种分层设计使解释内容能够依据学生能力动态调整，提升学术概念的清晰度与理解度。该系统已在选定中学与高等院校的低连接环境中部署，并从技术性能、可用性、感知响应质量及教育影响四个维度进行评测。结果表明系统在老旧硬件上运行稳定，响应时间可接受，用户对自主学习的支持效果反馈积极。这些发现证明了在低连接环境中部署离线大语言模型实现AI辅助教育的可行性。