Designing AI-resilient assessment in higher education: a four-pillar conceptual framework
Generative AI tools can produce polished academic text on demand, undermining the validity of assessments that treat written submissions as evidence of individual learning. Detection-based countermeasures have demonstrated variable accuracy and equity concerns. This paper does not report empirical outcomes or validation data. It proposes a framework for AI-resilient assessment that shifts evaluation from product quality to demonstrable reasoning, decision-making, and ownership of learning. The f
Record details
Published: 16 July 2026
Source: Frontiers in Artificial Intelligence
Category: Research
Topics: Bias & fairness · Children & education
Retrieved: 17 July 2026
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How to cite this record
ethics.ai (16 July 2026), “Designing AI-resilient assessment in higher education: a four-pillar conceptual framework,” evidence record 11066, https://ethics.ai/record/11066 (originally published by Frontiers in Artificial Intelligence).
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