Research
This page lists my published and accepted research work in reverse chronological order. I am deeply grateful to my supervisors for their guidance and to my collaborators for their support.
Enhancing AI Explainability for Non-technical Users with LLM-Driven Narrative Gamification
Yuzhe You, Helen Weixu Chen, and Jian Zhao
Abstract: Artificial intelligence (AI) is tightly integrated into modern technology, yet existing exploratory XAI visualizations are primarily designed for users with technical expertise. This leaves everyday users, who now also rely on AI systems for work and tasks, with limited resources to explore or understand AI. In this work, we explored the use of LLM-driven narrative gamification to enhance the learning and engagement of exploratory XAI visualizations. Specifically, we designed a design probe that enables non-experts to collect insights from an embedding projection by conversing directly with visualization elements similar to game NPCs. We conducted a preliminary comparative study to assess the effectiveness and usability of our design probe. Our study shows that while the tool enhances non-technical users’ AI knowledge and is perceived as beneficial, the impact of gamification alone on understanding remains inconclusive. Participant opinions on engagement are mixed: some find it enriching, while others see it as disruptive. (Accepted on Feb 22, 2025)
Self-Disclosure and Beyond: Takeaways from an Online and In-Person Computing Ethics Course
Helen Weixu Chen, Maura R. Grossman, and Daniel G. Brown
Abstract: We evaluate the amount and nature of self-disclosure in two versions of a 400-level computing ethics course focusing on discrimination and surveillance. The study involved 30 participants enrolled in two identical course offerings, taught by the same pair of instructors, but delivered in different formats: online versus in-person. Our analysis concentrated on the extent and contents of self-disclosure by both students and instructors. By using both quantitative and qualitative methods, we observed a higher prevalence of self-disclosure by both students and instructors in the online section. Notably, an analysis of demographic data revealed that minority group members were particularly active in self-disclosure in both formats. Overall, our findings suggest that an online setting may be more effective for delivering computing ethics courses where a primary goal is increasing open discussion and self-disclosure among participants. DOI Poster Demo
"Imagine a Dress": Exploring the case of task-specific prompt assistants for text-to-image AI tools
Helen Weixu Chen* and Lesley Istead*
Abstract: In this paper, we explore the impact of task-specific prompt assistants for text-to-image generative AI tools through a user study. Participants were asked to recreate a dress with SDXL using either a prompt assistant tailored to dress design, or no assistant at all. A detailed analysis of the results and feedback suggests that for this specific task, a tailored assistant improves result satisfaction and accuracy. This style of assistant helps users focus on the task by providing a detailed, visual, and organized approach to describing the object—enabling faster production times and more accurate descriptions with less ambiguity. DOI