用轻量模型重构大气物理课程，实现精准教学与动态资源联动。
Small Language Models Reshape Higher Education: Courses, Textbooks, and Teaching
- 用微型语言模型构建百万级语料库与图像库，支持高精度检索。
- 课程打破学科边界，教材转为动态数字资源库，学习路径变问答驱动。
- 适合高校教育数字化转型者、跨学科教学设计者参考。
尽管大语言模型在科学与教育领域带来新范式，但其在高等教育中的应用受限于易出错及高算力需求，难以满足对知识准确可靠性的严苛要求。相比之下，小型语言模型（MiniLMs）凭借轻量化与精准检索优势，在专业教育中展现出独特价值。本研究以《大气物理学》为例，从130余种国际权威地球与环境科学期刊中收集超55万篇全文PDF，从中提取超过1亿条高质量句级语料和逾300万张高分辨率学术图像，构建专用语料库与图像库。基于MiniLMs，将这些资源转化为高维向量库，实现高效精准检索与教学内容利用。据此系统重构了《大气物理学》课程体系：打造跨学科前沿教学框架，融合大气科学、空间科学、水文学与遥感；教材由静态文本升级为动态数字资源库；教学方法采用问题导向路径，推动从被动接受到主动认知的转变。该方案为‘AI赋能教育’提供了可落地的新路径。
原文摘要 · Abstract (English)
While large language models (LLMs) have introduced novel paradigms in science and education, their adoption in higher education is constrained by inherent limitations. These include a tendency to produce inaccuracies and high computational requirements, which compromise the strict demands for accurate and reliable knowledge essential in higher education. Small language models (MiniLMs), by contrast, offer distinct advantages in professional education due to their lightweight nature and precise retrieval capabilities. This research takes "Atmospheric Physics" as an example. We established a specialized corpus and image repository by gathering over 550,000 full-text PDFs from over 130 international well-respected journals in Earth and environmental science. From this collection, we extracted over 100 million high-quality sentence-level corpus and more than 3 million high-resolution academic images. Using MiniLMs, these resources were organized into a high-dimensional vector library for precise retrieval and efficient utilization of extensive educational content. Consequently, we systematically redesigned the courses, textbooks, and teaching strategies for "Atmospheric Physics" based on MiniLMs. The course is designed as a "interdisciplinary-frontier" system, breaking down traditional boundaries between atmospheric science, space science, hydrology, and remote sensing. Teaching materials are transformed from static, lagging text formats into a dynamic digital resource library powered by MiniLM. For teaching methods, we have designed a question-based learning pathway. This paradigm promotes a shift from passive knowledge transfer to active cognitive development. Consequently, this MiniLM-driven "Atmospheric Physics" course demonstrates a specific avenue for "AI for education".
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