AI时代的评价：输出作为学习的证据
Evaluation in the Age of AI: Output as Evidence of Learning
AI总结：
本文探讨AI时代大学教育评价的伦理挑战，指出传统评价指标易被AI外包的问题，提出需转向重视过程的替代评价模式，以保留学生自主性与问责制。
AI中文摘要：
人工智能（AI），特别是大语言模型（LLMs）的快速采用，从根本上改变了高等教育中学习的展示与评价方式。曾经作为理解代理的任务——如撰写论文、解决习题或生成计算机代码——现在可由AI系统以极少人力 superficial 生成。这一范式转变提出了一个关键伦理问题：当传统能力指标可轻易外包时，应如何评价学习？本文从大学视角审视AI时代教育评价的伦理挑战。我们认为核心问题超出学术不诚实，延伸至评价实践与其旨在测量的学习成果之间更深的错位。依赖人为约束的评价体系可能测量合规性、获取渠道或隐藏行为，而非真正的理解、推理或判断。通过分析机构应对措施并呈现实证调查数据，我们强调需要替代评价模式，该模式重视过程而非结果，目标是建立符合伦理的评价策略，在自动化时代保留学生的自主性与问责制。
英文摘要：
The rapid adoption of artificial intelligence (AI), particularly large language models (LLMs), has fundamentally disrupted how learning is demonstrated and evaluated in higher education. Tasks that once served as proxies for understanding-such as writing essays, solving problem sets, or producing computer code-can now be generated superficially by AI systems with minimal human effort. This paradigm shift raises a critical ethical question: how should learning be evaluated when traditional indicators of competence are easily outsourced? This paper examines the ethical challenges of educational evaluation in the age of AI from a university-level perspective. We argue that the core problem extends beyond academic dishonesty to a deeper misalignment between assessment practices and the learning outcomes they are intended to measure. Evaluation regimes that rely on artificial constraints risk measuring compliance, access, or concealment rather than genuine understanding, reasoning, or judgment. By analyzing institutional responses and presenting empirical survey data, we highlight the need for alternative assessment models that emphasize process over product. The goal is to establish ethically informed assessment strategies that preserve student agency and accountability in an automated age.
