Building an AI-Assisted Work Research Interface
Aruna Ranganathan, Professor
Business, Haas School
Applications for Fall 2026 are closed for this project.
This project examines how the design of AI-enabled tools shapes people’s work processes and experiences. For an upcoming experiment, we are developing a browser-based workspace in which participants will review task materials, interact with an integrated generative-AI assistant, move through stages of work, and turn in workplace-style deliverables.
Role: You will focus on building and maintaining this research interface. The work will include implementing and refining front-end components such as task panels, AI chat, dashboards, and workflow checkpoints; connecting the interface to backend services, an LLM API, and a database; and capturing system events such as edits, version history, task transitions, timestamps, and AI exchanges. You will also test and debug the platform, help deploy it for data collection, monitor its technical performance, and document the code so that it can be maintained reliably.
What you’ll gain
- Hands-on experience building a full-stack web application for behavioral research
- Practical experience integrating generative-AI models into a user interface
- Experience with event logging, application testing, deployment, and privacy-conscious research infrastructure
Qualifications: The ideal candidate will preferably be a sophomore or above with demonstrated experience building web applications. Applicants should be comfortable working with a modern web-development stack and have experience connecting user-facing interfaces to APIs and data storage. Familiarity with Git and systematic testing and debugging is important. Experience integrating LLM APIs, managing application state and event logs, or deploying web applications is especially desirable. We value careful, reliable work, clear communication, and willingness to learn.
Students from any major are welcome to apply, although those in computer science, data science, information science, engineering, or related fields are especially encouraged.
Day-to-day supervisor for this project: Maggie Ye, Staff Researcher
Hours: 9-11 hrs
Off-Campus Research Site: Remote
Related website: https://haas.berkeley.edu/faculty/aruna-ranganathan/
Social Sciences