DIS 2026 Under Review ‣ Artistic Support Tools: Expanding the Space of Creativity Support Research Through Artistic Practice ‣ Anonymous Authors ‣ Pictorial
CHI 2026 ‣ Through a Live Elections Dashboard, Darkly: Managing Expectations and Trust in Progressive Vote Counting During the 2024 U.S. Election ‣ Mandi Cai, Grace Wang, Chloe R. Mortenson, Fumeng Yang, Erik C. Nisbet, Matthew Kay ‣Preprint ‣ Illustrated Project Page
ACM TOCE ‣AI Unplugged: Exploring Pathways from Physical Simulation to Conceptualization of AI Reasoning Processes ‣ Hasti Darabipourshiraz, Lily Murakami Ng, Grace Wang, Sophie Rollins, Duri Long ‣ Paper ‣ Illustrated Project Page
AREA 2025 ‣ Prompts Matter: Comparing ML/GAI Approaches for Generating Inductive Qualitative Coding Results ‣ John Chen, Alexandros Lotsos, Lexie Zhao, Grace Wang, Uri Wilensky, Bruce Sherin, Michael Horn ‣ Paper
CSCL 2025 ‣ Processes Matter: How ML/GAI Approaches Could Support Open Qualitative Coding of Online Discourse Datasets ‣ John Chen, Alexandros Lotsos, Grace Wang, Lexie Zhao, Bruce Sherin, Uri Wilensky, Michael Horn ‣ Paper
Research Projects
OSADE
OSADE is a mentorship application that supports coaching of self-regulatory skills, ranging from metacognitive to emotional regulation, in Problem-Based Learning classrooms.
Knowledge Net
Knowledge Net is a collaborative, tangible tabletop museum exhibit where users construct characters by building semantic networks, aimed at teaching middle schoolers about knowledge representations in semantic networks.
Kernel Quest
A hands-on, interactive activity that teaches middle school students how CNNs use kernels to detect visual features. Students simulate a CNN by sliding the a plastic “kernel” between polarizer film sheets, revealing features of a cartoon monster to guess the monster's identity.
LIVE-ELEX
A live election result tracker designed for and tested during the 2024 Presidential election that fosters voter trust in vote counting procedures and debunk misconceptions about trends indicative of malfeasance.
Qualitative Coding with LLMs
LLM-Qual is a theory-informed computational method to systematically evaluate inductive coding results by computationally transforming each coder's inductive coding results into a Code Space. We propose and operationalize 4 computational metrics for assessing Code Spaces: Coverage, Density, Novelty, and Divergence.