AI4Society Seminar - Diyi Yang - Designing AI for Society: Teaming, Teaching, Tailoring

Abstract: Recent advances in large language models (LLMs) have revolutionized how humans and AI systems work, learn and interact, creating new opportunities for collaboration while also raising new challenges. In this talk, we explore the evolving landscape of human–AI collaboration from three perspectives: teaming, teaching, and tailoring. The first part on teaming shows how collaboration matters by introducing Co-Gym, which supports and evaluates human–agent collaboration, and a national audit of worker preferences that highlights mismatches between what workers want and current technological capabilities. The second part on teaching presents CARE, a scalable training system that leverages large language models to upskill counselors through realistic roleplay and structured feedback. The tailoring part introduces GUM, a general user modeling architecture that infers and reasons about unstructured context from users’ computer use to enable proactive AI assistance. Overall, this talk highlights how to develop AI systems that are not just tools, but meaningful collaborators working alongside us, helping us grow, and adapting to who we are.

Bio: Diyi Yang is an assistant professor in the Computer Science Department at Stanford University, also affiliated with the Stanford NLP Group, Stanford HCI Group and Stanford Human Centered AI Institute. Her research focuses on human-centered natural language processing and human-AI interaction.  She is a recipient of  Microsoft Research Faculty Fellowship (2021),  NSF CAREER Award (2022), an ONR Young Investigator Award (2023), and a Sloan Research Fellowship (2024).  Her work has received multiple paper awards or nominations at top NLP and HCI conferences.