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About

Winnie Han is at the forefront of building the foundational infrastructure for AI literacy education. Her research centers on the development of transparent, public-interest evidence registries that document how artificial intelligence is being integrated into learning environments worldwide. As a key contributor to the AI Assessment Board (AAB), Han has created a suite of structured datasets—including the *AAB AI Education Pilot Registry*, *Case Registry*, and *Evidence Registry*—that serve as critical resources for implementation research, standards development, and comparative analysis. These datasets, each accumulating early citations, provide a versioned and open framework for tracking global AI education pilots, policies, and community signals. By establishing a rigorous evidence preservation system, Han enables researchers and policymakers to move beyond anecdotal reporting toward data-driven decision-making in AI literacy. Her work is particularly notable for its emphasis on transparency and reproducibility, ensuring that pilot implementations and case studies are documented in a way that supports both current evaluation and long-term scholarly inquiry. Through these contributions, Han is shaping how the educational community systematically learns from AI integration efforts.