DesignQuizzer: A Community-Powered Conversational Agent for Learning Visual Design

Zhenhui Peng*, Qiaoyi Chen, Zhiyu Shen, Xiaojuan Ma, Antti Oulasvirta

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review


Online design communities, where members exchange free-form views on others' designs, offer a space for beginners to learn visual design. However, the content of these communities is often unorganized for learners, containing many redundancies and irrelevant comments. In this paper, we propose a computational approach for leveraging online design communities to run a conversational agent that assists informal learning of visual elements (e.g., color and space). Our method extracts critiques, suggestions, and rationales on visual elements from comments. We present DesignQuizzer, which asks questions about visual design in UI examples and provides structured comment summaries. Two user studies demonstrate the engagement and usefulness of DesignQuizzer compared with the baseline (reading We also showcase how effectively novices can apply what they learn with DesignQuizzer in a design critique task and a visual design task. We discuss how to use our approach with other communities and offer design considerations for community-powered learning support tools.

Original languageEnglish
Article number44
Number of pages40
JournalProceedings of the ACM on Human-Computer Interaction
Issue numberCSCW1
Publication statusPublished - 23 Apr 2024
MoE publication typeA1 Journal article-refereed


  • comment processing
  • informal learning
  • Online communities
  • visual design


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