New LLM tool automates real-time classification of user feedback

A new paper from researchers Jim Maddock, Rose Leitner, and Anna Wu (arXiv:2606.08050) presents a multi-year project that leverages large language models to automatically classify open-text user feedback in real time. The goal is to make UX data analysis more accessible by significantly reducing the time and expertise required to implement automated solutions. By doing so, the authors aim to democratize data analysis processes, allowing non-technical stakeholders to independently access, explore, and leverage user feedback without relying on specialized data teams.

The paper doesn't just highlight the technical achievement—it also candidly shares the organizational and technical constraints encountered throughout the project and the prototypes developed to address them. This includes challenges around scalability, privacy, and integration with existing UX workflows. The solution is positioned as a practical tool for UX practitioners who need to surface actionable themes from large volumes of free-text feedback quickly. While the paper focuses on human-computer interaction applications, the approach could generalize to any domain where open-text feedback needs real-time classification and analysis.