Despite a drastic increase in public reliance on peer-delivered helpline services (e.g., hotlines, warmlines) in the United States, very little is known about how evidence-based interventions (EBIs) such as motivational interviewing can be further implemented in this service setting. Innovations in Artificial Intelligence (AI) are rapidly developing, and AI-assisted services such as Lyssn, an evidence-based performance feedback (PF) tool, have demonstrated initial validity to support large-scale implementation of EBIs. This project will adapt performance feedback as an implementation strategy and evaluate its usability, engagement, and appropriateness to increase the use of motivational interviewing on a mental health teen-to-teen helpline. We will utilize human-centered design methods to systematically evaluate the process of adapting Lyssn’s PF interface to support the integration of Motivational Interviewing among teen helpers in Here App, a large helpline that provides teen-to-teen emotional support under the supervision of mental health staff. A Discover, Design/Build, Test model will be used. The discover stage will rely on teen helper and helpline supervisor/ leadership focus groups to assess usability issues and initial redesign solutions to support the uptake of this AI-assisted PF implementation tool. The design/build stage will involve iterative rapid prototyping through sustained engagement with participants completing surveys, cognitive interviews and focus groups to develop an adapted, tailored, teen-friendly PF interface that integrates Lyssn’s existing AI tools. The finalized adapted PF prototype will be used to support a larger grant application (NIMH K23) which will conduct a pilot feasibility implementation-effectiveness trial in teen-delivered helplines.