Synthia: Visually Interpreting and Synthesizing Feedback for Writing Revision

DOI

While recent advances in HCI and generative AI have improved authors' access to feedback on their work, the abundance of critiques can overwhelm writers and obscure actionable insights. We introduce Synthia, a system that visually scaffolds feedback-based writing revision with LLM-powered synthesis. Synthia helps authors strategize their revisions by breaking down large feedback collections into interactive visual bubbles that can be clustered, colored, and resized to reveal patterns and highlight valuable suggestions. Bidirectional highlighting links each feedback unit to its original context and relevant parts of the text. Writers can selectively combine feedback units to generate alternative drafts, enabling rapid, parallel exploration of revision possibilities. These interactions support feedback curation, interpretation, and experimentation throughout the revision process. A within-subjects study (N=12) showed that Synthia helped participants identify more helpful feedback, explore more diverse revisions, and revise with greater intentionality and transparency than a GPT-4-based writing interface.