证书仍然证明什么？人工智能介导教育的认知管理
What Does the Credential Still Certify? Cognitive Stewardship for AI-Mediated Education
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中文总结 AI 辅助
研究探讨生成式AI对教育评估前提的改变，开发认知管理框架，审核30所大学的AI评估指南，发现公共政策分类AI使用较好但解释证书有效性不足，强调大学需制定使认证逻辑可见的政策。
中文摘要 AI 辅助
生成式人工智能正在改变教育评估的一个基本前提：提交的作品能够可靠地证明证书所宣称认证的人类能力。挑战不仅在于学生是否使用人工智能，还在于当一些认知工作委托给系统时，关于学习仍能推断出什么。本文开发了认知管理，这是一个用于人工智能介导评估的框架，它将学习主张、委托边界、证据标准和保障措施联系起来。然后，我们审核了30所大学经过验证的公共生成式人工智能评估指南。使用预先指定的评分码本——一个书面的、基于来源的评分标准——四个开放权重的语言模型作为结构化编码员应用该评分标准，分数平均以减少对任何单个模型偏差的依赖。审核表明，公共政策在对人工智能使用进行分类方面比在解释哪些证据和保护措施能保持证书有效性方面做得更好。边界比证据标准更明显；保障措施不均衡；当人工智能的使用类似于最终输出替代而不是反馈、访问、验证或专业工作流程时，指导最为清晰。结论是，许可类别是必要的但并不充分。大学需要制定使认证逻辑可见的政策：学习者可以委托什么，他们仍必须展示什么，以及机构将如何保护公平证据而不仅仅是监控人工智能的使用。
英文摘要
Generative AI is changing a basic premise of educational assessment: that submitted work can reliably evidence the human capacities a credential claims to certify. The challenge is not simply whether students use AI, but what remains inferable about learning when some cognitive work has been delegated to a system. This paper develops cognitive stewardship, a framework for AI-mediated assessment that links the learning claim, delegation boundary, evidence standard, and safeguards. We then audit verified public generative AI assessment guidance from 30 universities. Using a pre-specified scoring codebook--a written, source-grounded rubric--four open-weight LLM models applied the rubric as structured coders, with scores averaged to reduce dependence on any single model's bias. The audit shows that public policies are becoming better at classifying AI use than at explaining what evidence and protections preserve credential validity. Boundaries are more visible than evidence standards; safeguards are uneven; and guidance is clearest when AI use resembles final-output substitution rather than feedback, access, verification, or professional workflow. The takeaway is that permission categories are necessary but insufficient. Universities need policies that make the certification logic visible: what learners may delegate, what they must still demonstrate, and how institutions will protect fair evidence rather than merely monitor AI use.
