Generative AI produces fluent and persuasive output that can obscure factual errors, bias, and misalignment with academic expectations. This session introduces two complementary, classroom-tested strategies for teaching students how to evaluate AI-generated content with purpose rather than relying on surface-level correctness checks. Drawing on an AI Output Evaluation Worksheet and the Triple-R strategy (Read, Relevance, Represent), participants will explore practical methods for embedding accuracy, relevance, ethical reflection, and accountability into assignments so students critically assess AI output before incorporating it into their academic work.
