Research on Smart Student Affairs Platform in Vocational Colleges via LoRA Fine-Tuning and Knowledge Base Augmentation: Architecture, Implementation, and Evaluation
Abstract
As China's higher vocational education transitions from scale expansion to high-quality internal development, institutions face structural contradictions, including high student-teacher ratios and the complex administrative burden on counselors. Traditional manual management modes struggle to meet the personalized service demands of “Gen Z” students. This study proposes a hybrid architecture for a next-generation smart student work platform, integrating Large Language Model (LLM) fine-tuning with Retrieval-Augmented Generation (RAG). We first analyze current administrative bottlenecks, then detail a “Client-Server-AI” (C-S-A) distributed architecture. The study focuses on the deep fusion mechanism of Parameter-Efficient Fine-Tuning (PEFT), specifically LoRA, with a vertical domain knowledge base. Furthermore, addressing mobile resource constraints, we introduce a “Code as Design” engineering paradigm, utilizing automated scripts to generate lightweight UI assets. Empirical results from the “Yunlv Zhidao” system demonstrate that this architecture improves policy Q&A faithfulness from 65% to 92%, significantly reduces transactional workloads, and achieves 24/7 service coverage, providing a replicable technical paradigm for digital governance in vocational education.
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