编码与AI：从工业实践的反思到未来计算机科学与软件工程教育
Coding With AI: From a Reflection on Industrial Practices to Future Computer Science and Software Engineering Education
AI总结：
本文探讨了AI在工业实践中对软件开发的影响，分析了LLM工具带来的生产力提升与风险，并提出教育应转向问题解决和项目式学习以适应变化。
摘要：
近年来，大语言模型（LLMs）的进展在软件开发中引入了新的范式，包括氛围编码、AI辅助编码和代理编码，从根本上改变了软件的设计、实现和维护方式。先前的研究主要在个人层面或教育环境中考察AI基于的编码，留下了工业从业者视角的探索不足。本文通过研究LLM编码工具在专业实践中的使用情况、相关担忧和风险以及由此产生的开发工作流程转变，特别关注其对计算教育的影响。我们对2024年底至2025年间发布的57个精心挑选的YouTube视频进行了定性分析，捕捉从业者分享的反思和经验。经过筛选和质量评估过程后，选定的资料被用来比较基于LLM的编程和传统编程，识别新兴风险，并描述演变的工作流程。我们的发现揭示了基于AI的编程实践的定义，显著的生产力提升和进入门槛的降低。从业者还报告了开发瓶颈向代码审查转移，以及对代码质量、可维护性、安全漏洞、伦理问题、基础问题解决技能的侵蚀和初级工程师准备不足的担忧。基于这些见解，我们讨论了对计算机科学和软件工程教育的影响，并主张课程向问题解决、架构思维、代码审查和早期项目式学习转变，以整合LLM工具。本研究为基于AI的编码提供了工业视角，并为使教育实践与快速演变的专业现实相适应提供了指导。
英文摘要：
Recent advances in large language models (LLMs) have introduced new paradigms in software development, including vibe coding, AI-assisted coding, and agentic coding, fundamentally reshaping how software is designed, implemented, and maintained. Prior research has primarily examined AI-based coding at the individual level or in educational settings, leaving industrial practitioners' perspectives underexplored. This paper addresses this gap by investigating how LLM coding tools are used in professional practice, the associated concerns and risks, and the resulting transformations in development workflows, with particular attention to implications for computing education. We conducted a qualitative analysis of 57 curated YouTube videos published between late 2024 and 2025, capturing reflections and experiences shared by practitioners. Following a filtering and quality assessment process, the selected sources were analyzed to compare LLM-based and traditional programming, identify emerging risks, and characterize evolving workflows. Our findings reveal definitions of AI-based coding practices, notable productivity gains, and lowered barriers to entry. Practitioners also report a shift in development bottlenecks toward code review and concerns regarding code quality, maintainability, security vulnerabilities, ethical issues, erosion of foundational problem-solving skills, and insufficient preparation of entry-level engineers. Building on these insights, we discuss implications for computer science and software engineering education and argue for curricular shifts toward problem-solving, architectural thinking, code review, and early project-based learning that integrates LLM tools. This study offers an industry-grounded perspective on AI-based coding and provides guidance for aligning educational practices with rapidly evolving professional realities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
