Improving Human-AI Interactions in the Future of Work (Experimental Game Building)
Park Sinchaisri, Professor
Business, Haas School
Applications for Spring 2026 are closed for this project.
Our lab works on human-AI interfaces for applications in the future of work. The overarching goals are to (i) understand how humans learn and make decisions in complex environments, (ii) design interpretable algorithms that improve human decision-making, and (iii) apply our frameworks in real-world settings, taking into account interpretability/explainability, human biases, and when people may resist or not comply with algorithmic advice.
Current and ongoing project themes include (non-exhaustive):
1. Impact of algorithmic precision on human sequential decision-making
2. Improving worker learning in the future of work
3. Optimal design of incentives for the future of work
4. Modeling human-AI interactions in various settings
5. Improving teachers’ productivity with generative AI
6. Improving how humans learn to use new tools (e.g., AI)
7. Human learning across tasks and contexts
8. Algorithmic management and its impact on human behavior
9. Recommendation systems for a network of agents (multi-agent recommendations/coordination)
10. Designing performance feedback with AI (how feedback format, timing, and explanations change behavior)
11. Improving team collaboration with AI (coordination, division of labor, shared context)
Role: You will gain exposure to research in human-computer interaction and behavioral operations management with real-world applications. The Game-Building track focuses on developing front-end, online games that help researchers study how humans interact with algorithms. Most games we have built are JavaScript-based, but we are open to more ideas.
You will get to brainstorm ideas for game design, build up the game, engineer the data collection process, and test the game out with real human participants.
What you might work on
- Interactive sequential decision games (advice vs no advice, precision vs broad advice, explanations vs none)
- Experiments studying learning, exploration, compliance/aversion, and feedback design
- Instrumentation and logging that makes data analysis reliable and replicable
Typical tasks
- Implement experimental logic: randomization, conditions, incentives, timers, attention checks
- Engineer reliable logging and data schemas (clean event streams, edge-case handling)
- Pilot, debug, and harden the task for deployment
- Document the experiment clearly (data dictionary, conditions, flow, and known limitations)
Qualifications: - Strong JavaScript skills and comfort with web development basics
- Extremely strong attention to detail and a testing mindset (you like catching edge cases)
- Ability to implement specs precisely and document decisions
- Bonus: experience with Qualtrics JS, jsPsych, oTree, React, or running pilots/data QA
Please also complete this google form as part of the applicatiobn:
https://forms.gle/8AQfQog5R2DxpRR17.
Hours: 9-11 hrs
Off-Campus Research Site: Remote is possible.
Related website: https://parksinchaisri.github.io/files/paper-tips.pdf
Related website: https://parksinchaisri.github.io/files/paper-tips.pdf