Humans Navigating Complexity with AI Helpers скачать в хорошем качестве

Humans Navigating Complexity with AI Helpers

Abstract Modern particle accelerator control rooms are like busy air traffic control centers, where skilled operators juggle dozens of interconnected tasks under tight deadlines, increasing with AI assistance. This talk explores a study of how novices and experts pick and relates these tasks, revealing key differences in their thinking patterns that AI tools must respect. Using data from real operation logs analyzed as networks, it shows novices stick to “safe” task groups, while experts have more precise mental models of the entire complex system for better results. These insights guide the design of trustworthy AI helpers that complement human expertise rather than replace it. Attendees will learn practical ways to make high-stakes tech environments safer and more efficient through human-centered AI. Speaker Bio Dr. Wan-Lin Hu is a human factors researcher specializing in human–machine interaction within complex automated systems in high-stakes environments. She has led SLAC studies uncovering expert decision-making patterns in control room operations. Dr. Hu earned her Ph.D. in Mechanical Engineering with a focus on human-centered design and human trust in machines. Her recent focus includes developing human-centered AI tools and STEM training programs.