提出控制依赖度框架，避免开发者过度或过少依赖AI工具
Towards an Appropriate Level of Reliance on AI: A Preliminary Reliance-Control Framework for AI in Software Engineering
- 通过访谈22位开发者，构建可衡量依赖程度的控制框架
- 识别出过度依赖与依赖不足两种风险状态
- 适合关注AI伦理、开发效率与教育实践的研究者
软件开发者与人工智能（如大语言模型）的互动方式，直接影响其使用效果。过度依赖可能削弱批判性思维能力；依赖不足则错失生产率与质量提升机会。基于对22位开发者的访谈，本文提出一个初步的依赖控制框架，以控制水平作为识别过度依赖和依赖不足的指标，并建议未来研究应进一步探索当前及新兴大语言模型工具所支持的不同控制层级。该工作为人工智能依赖问题的学术讨论提供基础，有助于开发者、教育者和政策制定者推动负责任且高效的AI工具使用。
原文摘要 · Abstract (English)
How software developers interact with Artificial Intelligence (AI)-powered tools, including Large Language Models (LLMs), plays a vital role in how these AI-powered tools impact them. While overreliance on AI may lead to long-term negative consequences (e.g., atrophy of critical thinking skills); underreliance might deprive software developers of potential gains in productivity and quality. Based on twenty-two interviews with software developers on using LLMs for software development, we propose a preliminary reliance-control framework where the level of control can be used as a way to identify AI overreliance and underreliance. We also use it to recommend future research to further explore the different control levels supported by the current and emergent LLM-driven tools. Our paper contributes to the emerging discourse on AI overreliance and provides an understanding of the appropriate degree of reliance as essential to developers making the most of these powerful technologies. Our findings can help practitioners, educators, and policymakers promote responsible and effective use of AI tools.
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