Small Language Models Reshape Higher Education: Courses, Textbooks, and Teaching
Jian Zhang, Jia Shao

TL;DR
This paper demonstrates how small language models can be used to redesign higher education courses, textbooks, and teaching methods by leveraging extensive scientific corpora for precise retrieval and active learning.
Contribution
It introduces a novel approach using MiniLMs to organize large educational resources and transform teaching strategies in atmospheric physics.
Findings
Enhanced course interactivity and interdisciplinarity
Improved retrieval accuracy of educational content
Shift from static textbooks to dynamic digital resources
Abstract
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…
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Taxonomy
TopicsComputational and Text Analysis Methods · Multimodal Machine Learning Applications · Topic Modeling
