当人工智能模糊贡献边界时：作者身份校准的实证研究
When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration
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中文总结 AI 辅助
研究人工智能广泛应用下用户与系统交互产生新内容时的作者身份校准问题，通过CoAuthor数据集实证研究，发现依赖AI程度不同影响校准准确性，强调培养校准对促进AI负责任及有教育意义整合的重要性。
中文摘要 AI 辅助
人工智能（AI），尤其是生成式人工智能的广泛应用，引发了关于用户如何与这些系统交互以产生新内容的紧迫问题。本文引入了作者身份校准的概念，即用户在与人工智能交互时对其实际作者身份的认知。我们使用CoAuthor数据集，实证研究了作者身份校准如何因用户而异，以及它与人工智能使用频率的关系。结果显示出很大的变异性：严重依赖人工智能的用户往往会误判自己的作者身份，而使用人工智能频率较低的用户则表现出更准确的作者身份校准。这些发现表明，人工智能会模糊用户对自己作者身份的认知。在学习环境中，校准错误会影响元认知监测和学习策略，最终影响学习成果。因此，培养作者身份校准对于促进负责任且具有教育意义的人工智能整合至关重要。
英文摘要
The broad adoption of Artificial Intelligence (AI), especially Generative AI, raises pressing questions about how users interact with these systems to produce new content. In this paper, we introduce the concept of authorship calibration, defined as users awareness of their actual authorship when interacting with AI. Using the CoAuthor dataset, we empirically examine how authorship calibration varies across users and how it relates to their frequency of AI use. Our results reveal high variability: users relying heavily on AI tend to misjudge their authorship, whereas those using AI less frequently exhibit more accurate authorship calibration. These findings suggest that AI can obscure users perception of their own authorship. In learning contexts, miscalibration can affect metacognitive monitoring and learning strategies, ultimately impacting learning outcomes. Fostering authorship calibration then appears essential for promoting responsible and educationally meaningful AI integration.
发表机构
- Human-IST Institute, University of Fribourg（人类-IST研究所，弗里堡大学）
机构由 AI 辅助整理，请以论文原文为准。
