人工智能赋能职教本科《仪器分析》课程教学改革研究——基于云端OpenClaw的智能作业批改系统设计与实践

Research on Teaching Reform of AI Empowered Vocational Education Undergraduate Course Instrument Analysis—Design and Practice of Intelligent Homework Correction System Based on Cloud OpenClaw

摘要: 针对职教本科《仪器分析》课程教学中主观题作业批改耗时费力、学情分析难以深入等现实困境，本文依托深圳职业技术大学《仪器分析》课程建设基础，系统构建了基于云端OpenClaw的智能作业批改与学情分析系统。本系统以豆包大模型为支撑，通过任务拆分的技能组合方式，实现了主观题自动批改、分步给分、作答思路研判、共性学情汇总与诊断报告生成的全流程自动化处理。实践表明，该系统能够在一到两小时内完成70人教学班的作业批改，显著提升批改效率、保证评分一致性并降低人工成本。同时，本系统通过数据脱敏处理和人工内容审查等机制确保数据安全与输出质量，为职教本科理工类课程的AI赋能教学改革提供了系统化、可复制的实践路径。

Abstract: In response to the practical difficulties of subjective homework correction in the teaching of the undergraduate course Instrument Analysis in vocational education, such as time-consuming and laborious work, and difficulty in in-depth analysis of learning situations, this article relies on the construction foundation of the Instrument Analysis course at Shenzhen Vocational and Technical University to systematically construct an intelligent homework correction and learning situation analysis system based on cloud OpenClaw. This system is supported by the Big Bean Bun Model and achieves the full process automation of subjective question automatic correction, step-by-step scoring, analysis of answer ideas, summary of common learning situations, and generation of diagnostic reports through the skill combination of task splitting. Practice has shown that the system can complete homework grading for a 70-person teaching class within one to two hours, significantly improving grading efficiency, ensuring grading consistency, and reducing labor costs. At the same time, this system ensures data security and output quality through mechanisms such as data anonymization and manual content review, providing a systematic and replicable practical path for AI empowered teaching reform in vocational education undergraduate science and engineering courses.