发表机构
NAPS; MIT（NAPS; 麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对GenAI对传统评估的挑战，提出动态证据收集生态系统框架，通过多源过程证据收集与AI层支撑强化学术诚信，呈现机构采用的实施场景。
AI 中文摘要
生成式人工智能（GenAI）可生成高质量的论文、代码与设计制品，对依赖单点提交和仅以成果评分的传统评估的有效性构成挑战。本文提出名为“动态证据收集生态系统”的设计框架，将评估转向随时间推移的学生学习的连续、真实、多源证据。该框架通过迭代制品、设计日志、活动轮次、自我反思及同伴协作收集过程证据，由支持学习分析、形成性反馈与透明度的AI层提供支撑。该方法基于AI丰富环境下的近期评估重构研究，符合当代评估真实性观点。本文基于以下假设：当学术诚信被视为评估设计而非AI检测问题时，其会得到强化。相关工具存在使用限制与风险，可能带来学术处罚。本文呈现了一个支持机构采用的实施场景。
英文摘要
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.
CommentsISET 2026
