Uramaki LAB

Mentors: Student: Justin Timothy W

RUXAILAB currently saves the final state of usability studies but lacks insight into the evaluation process itself. This project introduces a unified, scalable logging and traceability system built into the shared frontend layer, backed by entirely secure Firestore Cloud Functions. It captures methodology-specific events (like evaluator answer revisions and participant task anomalies) to provide researchers with an interactive Log Explorer, quick filters, and anonymized exports. This system enables deep session traceability for IRB submissions and post-study behavioral analysis without increasing database read costs or affecting existing architectures.

Mentors: Student: pedrohenriquebl

This project focuses on implementing a lightweight real-time gaze cursor overlay that visually represents where the user is looking during a usability test. The goal is to provide an immediate, low-overhead visualization that supports live demos, moderation, and quick qualitative insights.

Mentors: Student: Montse Ponsa Capell

This project aims to develop an AI-powered tool for automated heuristic evaluation of digital interfaces, addressing the limitations of manual usability analysis, which is time-consuming and requires expert knowledge. The proposed solution combines rule-based evaluation using established usability principles, such as Nielsen’s heuristics, with artificial intelligence techniques to analyze interfaces, detect usability issues, and generate both quantitative scores and qualitative insights. This hybrid approach improves efficiency, consistency, and accessibility of usability evaluation. The project will be developed using the Scrum methodology, with iterative sprints and continuous feedback. As an open-source solution, it will support collaboration and future extensions by the community. Key deliverables include the system architecture and prototype, a heuristic evaluation engine, an AI-based analysis module, an automated report generation system, and complete documentation along with the final release.

Mentors: Student: M Mohith

RUXAILAB supports five study types but lacks focus group functionality. This project adds FOCUS_GROUP as a new study type under the Inquiry category, delivering structured session configuration with discussion guides, real-time multi-participant data capture via Firebase RTDB, manual thematic structuring, and NLP-assisted analysis through cost-tiered Python Cloud Functions. Core deliverables include the full focus group lifecycle - create, configure, run, capture, persist, and analyze - following the established Heuristic module architecture. Stretch goals include a LiveKit SFU integration for reliable video, sentiment-analysis-api integration for audio sessions, and a PDF report generator.

Mentors: Student: Tianqin Meng

This project implements a comprehensive GitHub-Discord integration platform. The system includes a Discord bot with OAuth GitHub account linking for automatic role assignment, a PR review automation system with AI-powered code analysis, and a data pipeline that processes and stores GitHub events for analytics. Built on Python with Flask and Discord.py, deployed via Google Cloud Run, and orchestrated through GitHub Actions workflows.