This research examines how multilingual college students use AI writing tools and whether these tools support or hinder learning. The findings suggest that learning outcomes depend on how AI is used. When employed as a scaffold for feedback and reflection rather than a shortcut, AI can enhance writing development and critical thinking.

This research explores next-generation digital twins—virtual representations of real-world systems that support decision-making through simulation and AI. By combining decentralization, privacy-preserving architectures, explainable AI, and scenario analysis, the work aims to help individuals and organizations evaluate alternative futures, make informed decisions, and build more transparent intelligent systems.

As generative AI reshapes the advertising industry, this research shows creativity is not replaced but redistributed. Through interviews and immersive fieldwork, a four-stage framework—readiness, co-creativity, validation, and execution—reveals how humans and AI can collaborate to amplify creative potential rather than diminish it.

This research develops an onboard AI diagnostic assistant for space missions that can independently investigate life-critical anomalies. By learning how humans ask strategic diagnostic questions, the system combines language models and traditional AI to actively reason through unprecedented spacecraft failures when communication with Earth is delayed.

This research develops human–AI methods to prioritize massive data streams, especially in public health. By combining expert expectations with extreme value theory, it ranks events by contextual importance, reducing alert overload. Deployed nationally, the approach triaged thousands of outbreaks and data-quality issues, making big data interpretable, actionable, and life-saving decisions.