Adaptive Learning Interfaces
AI productivity tools are most useful when they support human judgment, reduce cognitive friction, and fit naturally into real workflows. My interest is in designing and evaluating AI systems that help people move from information to action without losing agency, context, or quality.
Profiling Students
for Adaptive Social Comparison
Students constantly receive signals about how they are doing. A grade shows whether they passed. A progress bar shows how much work they have completed. A class average tells them how they compare with everyone else. That final signal may appear simple, but it can change the meaning of all the others.
Imagine that you have completed 60% of a course while the class average is 80%. You might treat the gap as a challenge and work harder. You might conclude that you are falling too far behind and disengage. You might also ignore the comparison because your goal is simply to understand the material at your own pace.
The information is identical. The response is not.
HIGHLIGHT
Conclusion
Social comparison should therefore not be treated as a decorative dashboard feature or a universal source of motivation. It is a psychologically meaningful intervention. Designing it responsibly requires attention not only to the information being displayed, but also to the person interpreting it.






