Evaluating Behavioral Interventions at Scale with AI - Felix Chopra; Ingar K. Haaland; Nicolas Roever; Christopher Roth (Munich Society for the Promotion of Economic Research - CESifo GmbH, 2026)
We test the effectiveness of different AI-delivered conversation protocols to increase people's motivation for change. In a large-scale experiment with 2,719 social media users, we randomly assign participants to a control conversation or one of three treatment arms: two Motivational Interviewing protocols promoting self-persuasion (change focus or decisional balance) and a direct persuasion protocol providing unsolicited advice and information. All conversations are led by an AI interviewer, enabling standardized delivery of each protocol at scale. Our results show that all three interventions significantly increase motivation for change and the perceived costs of social media use, with change-focused self-persuasion yielding the largest effects. These effects persist and translate into self-reported reductions in social media use more than two weeks after the intervention. Our findings illustrate how AI-led conversations can serve as a scalable platform both for delivering behavioral interventions and for testing what makes them effective by systematically varying how conversations are conducted.
Keywords: C90; D83; D91; 330; AI interviews; scaling; motivation; persuasion; social media; beliefs