Toward AI-Resilient Assessment in Computer Science Courses in an AI-Native World
AI-native course assessments in senior computer science courses and related fields should grade students by \emph{AI-resilient skill}: the ability to achieve outcomes beyond a strong AI baseline. Such assessments should allow students to use AI freely, while reducing the extent to which greater private AI budget or more intensive AI use, by itself, becomes a grading advantage. This paper proposes a minimal formal framework for this goal. The framework specifies a real task, an executable evaluator, a declared AI-native Pareto frontier, and a grading rule based on Pareto surplus. The central claim is simple: Pareto surplus provides a measurable, protocol-relative certificate that a submitt...
| Authors | Anshumali Shrivastava |
|---|---|
| Primary category | cs.CY |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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