Generative AI Literacy Training Improves Intelligence Analysts’ Discrimination of Real and AI-Generated Images

Kamali et al. (2026) evaluate a Generative AI Literacy training intervention designed to improve intelligence analysts' ability to distinguish real photographs from AI-generated images. In a controlled experiment, trained analysts significantly outperformed untrained controls on image discrimination tasks, with gains persisting on challenging edge cases. This is an important contribution to AI Literacy research because it demonstrates that detection skills are teachable even among domain experts, challenging the assumption that AI-generated content is fundamentally undetectable. The training draws attention to specific visual artifacts and generative model signatures, providing a template for broader Professional Training curricula. The study connects to Equity concerns around AI-generated misinformation, since disparities in detection ability can compound existing information-access inequalities. It also informs Generative AI regulation discussions by providing evidence that literacy interventions are a viable complement to technical watermarking approaches. The work extends AI Literacy Continuum Higher Education by showing that literacy training is relevant not just for students but for professionals across domains.

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Negar Kamali, Candice Rockell Gerstner, Jessica Hullman, Matthew Groh (2026). Generative AI Literacy Training Improves Intelligence Analysts’ Discrimination of Real and AI-Generated Images. arXiv:2606.28510. cs.HC / cs.AI / cs.CY.