研究AI使用如何影响作者自我认知，发现频繁使用者易误判贡献度。
When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration
- 通过实证分析用户在使用AI时的作者身份感知偏差
- 高频使用者作者认知偏差显著，低频使用者更准确
- 对教育场景中元认知与学习策略有重要影响
生成式AI的广泛应用引发了关于用户如何借助AI创作新内容的紧迫问题。本文提出作者身份校准（authorship calibration）概念，指用户在使用AI时对其实际贡献程度的认知。基于CoAuthor数据集，我们实证考察了不同用户群体的作者身份校准差异及其与AI使用频率的关系。结果表明，依赖AI较频繁的用户存在明显作者身份误判，而使用较少者则表现出更高的校准准确性。这说明AI可能模糊用户对自己贡献的认知。在学习情境中，这种认知偏差会影响元认知监控和学习策略，进而损害学习成效。因此，培养作者身份校准能力对于实现负责任且具教育意义的AI整合至关重要。
原文摘要 · Abstract (English)
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.
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