Events
CLIP Colloquium: When AI Adapts, Teaches, and Trains: Open Challenges in Human–AI Interaction
Event Start Date: Wednesday, February 11, 2026 - 11:00 am
Event End Date: Wednesday, February 11, 2026 - 12:00 pm
Location: IRB 5105
Contact: Wei Ai
Join us for an insightful CLIP Colloquium featuring Eugenia Rho, where we will explore the critical challenges and paradoxes of human-AI interaction in adaptation, teaching, and training.
Abstract: AI language technologies promise to adapt to us, support our work, and help us grow. Yet these promises also raise fundamental challenges in how such systems are designed and evaluated. In this talk I will discuss three dimensions of this challenge through ongoing studies in my lab. The first is a paradox in LLM personalization. When autistic users disclose their identity to language models, the same mechanism meant to make AI more responsive can trigger stereotype-driven advice. We find that disclosure often steers models toward risk-averse recommendations, creating what we call the safety-opportunity paradox. The second is a tension in learning with AI. In our pair programming study, we compare human-AI (HAI) pairs with human-human-AI (HHAI) triads. When participants worked alone with AI, they relied heavily on AI-generated code. But when working with a human peer alongside AI, they became far more selective, using almost no AI-generated code in their final coding solutions. These contrasting experiences between HAI and HHAI conditions situate our findings within the long-standing HCI discussion on augmentation versus automation. The third involves open questions in AI-mediated communication training. Systems like CommCoach use conversational role-play to help managers practice difficult workplace interactions. But critical questions remain about whether AI can accurately recognize user intent in sensitive contexts, whether people will sustain practice over time, and whether better communication skills actually transfer to real workplace conversations. Taken together, these studies show that the deeper challenge is not whether AI performs well but whether it supports human agency and growth.
Eugenia Rho
Bio: I am an Assistant Professor of Computer Science at Virginia Tech, where I study how AI mediates human communication and how people adapt to it. My research develops computational frameworks and interactive systems for language generation that are responsive to social dynamics, human reasoning, and real-world outcomes. I lead the Society + AI & Language (SAIL) Lab.
My work is motivated by a long-standing interest in how language shapes thought, interaction, and decision-making. Drawing on methods from natural language processing, human–computer interaction, and statistics, I model the social consequences of language, design AI systems that support human interaction, and ground model reasoning in diverse human contexts. Together, these directions contribute to a foundation for AI that seeks to understand not only what language means, but what it does.
My research has been featured in NPR, CNN, Forbes, PBS, Newsweek, Scripps News, and Scientific American. Prior to joining Virginia Tech, I was a postdoctoral researcher in the Stanford NLP Group, working with Dan Jurafsky. I received my Ph.D. from the University of California, Irvine, and my B.A. from Columbia University.
Speaker(s): Eugenia Rho