AI赋能《信息技术导论》教学重构：基于DeepSeek的实践

AI Empowers the Teaching Reconstruction of “Introduction to Information Technology”: Practice Based on DeepSeek

摘要: 针对《信息技术导论》课程内容滞后于技术发展、实训脱离真实场景、评价缺乏过程支持等痛点，本文构建了基于DeepSeek平台的“动态知识更新–虚实融合实践–个性成长追踪”教学模式。通过实时整合GitHub、arXiv等开源社区数据，设计知识动态更新机制，实现教学内容与行业实践的同步周期从3.2年缩短至2.1天；开发虚实融合实训平台，在普通浏览器中实现200 ms低延迟数字孪生映射，使设备调试成功率提升37%；构建多模态循证评价体系，通过多维学习画像实现精准学情诊断，教师识别学习困难的效率提升4倍。跨区域准实验表明，实验组在前沿技术认知准确率、长期职业竞争力等维度显著优于对照组。

Abstract: In response to the pain points of the course content of “Introduction to Information Technology” lagging behind technological development, practical training being out of touch with real scenarios, and lack of process support for evaluation, this paper constructs a teaching model of “dynamic knowledge update-virtual-real integration practice-personality growth tracking” based on the DeepSeek platform. By integrating open source community data such as GitHub and arXiv in real time, a knowledge dynamic update mechanism is designed to shorten the synchronization cycle between teaching content and industry practice from 3.2 years to 2.1 days; a virtual-real integration training platform is developed to achieve 200 ms low-latency digital twin mapping in ordinary browsers, which increases the success rate of equipment debugging by 37%; a multimodal evidence-based evaluation system is constructed to achieve accurate learning diagnosis through multi-dimensional learning portraits, and the efficiency of teachers identifying learning difficulties is increased by 4 times. Cross-regional quasi-experiments show that the experimental group is significantly better than the control group in terms of accuracy in frontier technology cognition and long-term professional competitiveness.