Emerging AI and the Need for a Novel Evaluation Framework in Undergraduate Computer Science Education

Generative artificial intelligence (AI) is reshaping undergraduate computer science (CS) education, and instructors, assessment designers, and computing education researchers need clear guidance on what that means for evaluation. Current systems emphasize automated grading and functional correctness, leaving higher-order outcomes such as analytical reasoning, design justification, ethical reflection, and authorship verification under-assessed. This study synthesizes literature on automated assessment, AI disruption, and assignment design and argues why a novel evaluation framework is needed for undergraduate CS evaluation in the United States. Evidence is organized into four claims: validity gaps in correctness-centric grading, equity concerns introduced by AI-mediated work, pedagogical limitations in assessing higher-order outcomes, and an integration gap between assignment frameworks and AI-aware evaluation. Together these claims justify a shift toward AI-resilient evaluation that preserves academic integrity and supports the development of knowledge, skills, and abilities required for success in research and professional CS roles. These skills include problem analysis, solution design, code quality, ethical judgment, communication, and collaboration. This work is intended for instructors, assessment designers, and computing education researchers seeking practical, near-term adjustments that protect integrity and deepen learning in AI-rich classrooms.