Speaker: Tiffany Li

Title: Towards The Effective and Responsible Use of Imperfect NLP-generated Learning Content For STEM Personalized Learning

Abstract:

Artificial intelligence (AI) has been a building block to provide adaptive instruction and learning support at scale since the 1970s. In the past decades, researchers have mainly relied on knowledge-based AI to construct a domain model for learner-facing interactive personalized instruction systems, i.e., encoding task domain knowledge in symbols, logic, and rules. More recently, the use of data-driven AI to extract domain knowledge from data has gained attention due to its potential to better represent domains with open or changing worlds, reduce expert-authoring costs, and broaden the scope of user interaction. However, the learning content created based on a data-driven AI domain model is much more likely to contain inaccurate or incomplete information. This has raised concerns about its use, especially since it has become more accessible after the public launch of ChatGPT and similar tools. Should we deploy a system that uses imperfect AI-generated learning content, given its potential harm? If so, how should we design and deploy it effectively and responsibly? 

To provide insights into these questions, I systematically investigated how adult learners perceive, interact with, and get impacted by imperfect AI-generated content in STEM learning. My dissertation focuses on two types of learning content created with Natural Language Processing (NLP) techniques: (1) formative correctness feedback for short-answer questions and (2) natural language responses to learner-initiated interactive help-seeking. Using a socio-technical lens and a mixed-methods approach, I contributed actionable recommendations on learner support, system design, and system deployment to help diverse learners gain the most from imperfect AI-generated learning content. The dissertation further demonstrates the need to use caution when deploying such imperfect content and provides guidelines for conducting impact assessments before deployment.

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 at top HCI conferences (CHI, CSCW) and prestigious educational research 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.