用动态证据生态取代单一作业，让评估更真实可信。
Dynamic Evidence Collection Ecosystem for Assessment Integrity and Authentic Competence
- 通过迭代作品、设计日志等多源过程数据收集学习证据。
- 支持实时反馈与透明评估，提升学术诚信与真实性。
- 适合教育机构改革评估体系，应对AI生成挑战。
生成式人工智能可产出高质量论文、代码和设计作品，威胁依赖单次提交和结果评分的传统评估有效性。本文提出「动态证据收集生态系统」设计框架，将评估转向持续、真实、多源的学习证据积累。通过迭代作品、设计日志、活动记录、自我反思与同伴协作，结合AI驱动的学习分析、形成性反馈与透明机制，实现对学生学习过程的全面捕捉。该框架基于富人工智能情境下的评估重构研究，契合当前对评估真实性的认知。本文主张学术诚信应作为评估设计问题而非仅靠AI检测解决，强调其在制度化实施中的可行性与潜在风险。工具使用存在局限与学术惩罚风险，但提供了支持机构采纳的实施场景。
原文摘要 · Abstract (English)
Generative Artificial Intelligence (GenAI) can produce high-quality essays, code, and design artefacts, challenging the validity of conventional assessments that rely on single-point submissions and product-only grading. This paper proposes a design framework called "Dynamic Evidence Collection Ecosystem" that shifts assessment toward continuous, authentic, multi-source evidence of student learning over time. The framework collects process evidence through iterative artefacts, design logs, activity rounds, self-reflection, and peer collaboration, supported by an AI-enabled layer for learning analytics, formative feedback, and transparency. The approach is grounded in recent assessment-redesign scholarship in AI-rich contexts and aligned with contemporary views of authenticity in assessment. This paper builds on the hypothesis that academic integrity is strengthened when it is treated as an assessment design rather than as an AI detection problem. The tools have limitations and risks of use that carry academic penalties. This paper presents an implementation scenario to support institutional adoption.
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