🤖 AI Summary
The proliferation of generative AI has rendered traditional modular assessments ineffective, creating a critical misalignment between academic evaluation and industry practices augmented by AI. Method: This paper proposes an AI-resilient assessment framework for computing education, centered on interlinked, multi-stage problem structures with output feedback loops—designed to deter AI-assisted cheating and bridge the pedagogy–practice gap. Contribution/Results: We formally prove that interlinked problems exhibit superior AI resilience compared to modular ones. Empirical validation across four data science courses (N=138) demonstrates that semi-structured interlinked tasks yield more stable and valid measures of student competency than open-ended projects—challenging the prevailing policy assumption that openness inherently deters AI misuse. Under AI access, modular assignments achieved near-perfect scores, while proctored exam performance dropped by 30%; in contrast, interlinked projects maintained high validity (r > 0.7) and significantly curbed AI substitution, thereby preserving academic integrity and assessment fidelity.
📝 Abstract
The rapid adoption of generative AI has undermined traditional modular assessments in computing education, creating a disconnect between academic evaluation and industry practice. This paper presents a theoretically grounded framework for designing AI-resilient assessments, supported by formal analysis and multi-year empirical validation.
We make three contributions. First, we establish two theoretical results: (1) assessments composed of interconnected problems, where outputs feed into subsequent stages, are more AI-resilient than modular assessments because current language models struggle with sustained multi-step reasoning and context; and (2) semi-structured problems with deterministic success criteria provide more reliable measures of student competency than fully open-ended projects, which allow AI systems to default to familiar solution patterns. These results challenge common policy and institutional guidance that promotes open-ended assessments as the primary safeguard for academic integrity.
Second, we validate these results using data from four university data science courses (N = 138). While students achieve near-perfect scores on AI-assisted modular homework, performance drops by roughly 30 percentage points on proctored exams, indicating substantial AI score inflation. Interconnected projects remain strongly correlated with modular assessments, suggesting they measure the same underlying skills while resisting AI misuse. Proctored exams show weaker alignment, implying they may assess test-taking ability rather than intended learning outcomes.
Third, we translate these findings into a practical assessment design framework. The proposed approach enables educators to create assessments that promote integrative thinking, reflect real-world AI-augmented workflows, and naturally resist trivial delegation to generative AI, thereby helping restore academic integrity.