Lightweight AI Agents Reconstructing the Virtual Teaching-Research Landscape in County and Rural Areas

Providing quality education in resource-constrained environments is a global challenge. Cloud computing and augmented reality, despite their effectiveness for immersive learning, are limited by their high bandwidth requirements and dependence on sophisticated hardware, which are often lacking in rural China. Responding to the national promotion of educational digitalization, it proposes a lightweight AI agent framework suitable for such environments. The framework integrates a lightweight BERT model via knowledge distillation and a multi-agent collaboration mechanism, forming a low-resource-dependency technical system. A one-semester randomized controlled experiment in 30 rural schools in Guangdong Province showed that, compared to the control group, the experimental group achieved a 91.6% increase in average teaching-research participation per teacher. Additionally, students in the experimental group showed a greater improvement of 7.5 points in their average subject scores, and teachers’ overall satisfaction increased by 39.1%. It also outlines construction concepts, existing problems with solutions, and future research prospects.