Thesis Committee: Manoel Horta Ribeiro (adviser), Andrés Monroy-Hernández (reader)

Thesis Title: AI Mediation for Discretionary Work: Reducing the Activation Energy of Feedback Provision

Thesis Abstract:

AI systems are increasingly integrated into human workflows, often as mediators that shape human behavior and decision-making by generating intermediate artifacts. While prior work shows that AI mediation can improve task efficiency and accuracy, it remains unclear whether it can increase participation in discretionary but beneficial tasks that people often intend to perform but frequently skip. To explore this idea, we study one such task: feedback provision in a university course.

We conduct a randomized field experiment in a 300-level machine learning course in which students' question-level submissions are assigned to either (1) a treatment condition where teaching assistants (TAs) receive AI-mediated feedback templates that they can choose to use or (2) a control condition where TAs do not receive any templates. Using behavioral logs, surveys, and interviews, we examine how AI mediation influences participation, interaction patterns, and downstream student experiences. We find that AI-mediated feedback templates significantly increase feedback provision and that they lower the small but persistent barriers required to initiate feedback, which we refer to as the task's activation energy, rather than reducing the total effort required to produce it. Importantly, this increased provision does not degrade downstream experience of students, suggesting that AI mediation can scale access to beneficial practices while maintaining satisfactory quality. Together, our results position AI mediation as a mechanism for increasing participation in discretionary work and surface important tradeoffs for the design of systems that position AI as mediators.

All are welcome to attend.