Abstract: What is AI’s role in providing equitable access to quality, personalized education? Quality education transforms life and empowers communities. Yet, education inequity remains one of the biggest challenges worldwide. Recent advancements in interactive AI systems have painted an exciting future for more accessible personalized education. In this talk, I will discuss two questions essential to achieving the effective and responsible use of AI in advancing personalized learning at scale.

 

Personalized learning benefits a wide range of learning contexts. Existing applications of AI personalized educational systems remain limited, particularly to well-structured learning contexts. To push the boundaries of AI personalized education, we need to ask: for personalized learning contexts that are yet hard to scale, can AI help, and if so, where and how? I will share my effort in answering this question in the contexts of spatial visualization sketching exercises and open-ended non-STEM courses.

 

Knowing that “AI can help” is insufficient – if these systems are not developed and deployed using a human-centered approach, they can fail to reach their full potential, cause harm, and even widen learning disparities. Therefore, it’s equally critical for us to consider: Should an AI system be deployed in a personalized learning context? How should it be designed and deployed? I will demonstrate the effectiveness of conducting algorithmic impact assessments on imperfect AI personalized learning systems in answering these questions.

Short Bio: Tiffany Wenting Li is a final-year Ph.D. candidate in Computer Science at the University of Illinois at Urbana-Champaign, co-advised by Karrie Karahalios and Hari Sundaram. She is committed to using computing to address the challenge of education inequality with her training as a researcher in human-computer interaction (HCI), human-centered AI, and education. She has published her work in both top HCI conferences (CHI, CSCW) and prestigious education venues (ICER, EDM, etc.). The significance of her work has earned her a Google Ph.D. Fellowship and funding from the Microsoft Research Accelerate Foundation Models Research Program.