用三重视角解析AI编程，助科研人员高效写代码。
The why, what, and how of AI-based coding in scientific research
- 从'为何用AI'出发，剖析大模型在编程中的角色
- 归纳六类代码辅助类型，覆盖研发全流程
- 提出五步实战流程，适合科研与教学场景
编程是跨学科研究不可或缺的能力，但学习难、耗时长。生成式AI，尤其是大语言模型（LLMs），有望将编程转化为自然对话，但最佳实践和有效工作流仍在探索中。本文从三个关键视角解析基于AI的编程：大模型在编码中的本质与作用（为什么）、它们提供的六种编码辅助类型（什么），以及一个五步工作流及其实际实施策略（如何）。此外，还讨论了当前局限与未来展望。该框架为研究人员提供了可操作的洞察，助力其有效利用AI提升编码效率与教育质量，加速科学进步。
原文摘要 · Abstract (English)
Computer programming (coding) is indispensable for researchers across disciplines, yet it remains challenging to learn and time-consuming to carry out. Generative AI, particularly large language models (LLMs), has the potential to transform coding into intuitive conversations, but best practices and effective workflows are only emerging. We dissect AI-based coding through three key lenses: the nature and role of LLMs in coding (why), six types of coding assistance they provide (what), and a five-step workflow in action with practical implementation strategies (how). Additionally, we address the limitations and future outlook of AI in coding. By offering actionable insights, this framework helps to guide researchers in effectively leveraging AI to enhance coding practices and education, accelerating scientific progress.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。