# ClientBot: A conversational agent that supports skills practice and feedback for Motivational Interviewing for AUD

> **NIH NIH R44** · LYSSN.IO, INC. · 2021 · $855,238

## Abstract

PROJECT SUMMARY/ABSTRACT
 Millions of Americans are in need of evidence-based counseling, such as motivational interviewing (MI),
for alcohol use disorders (AUDs) each year. To develop competence in an evidence-based practice like MI,
trainees require ample opportunities for practice and immediate, performance-based feedback on the skills that
they are learning. However, this is challenging if not impossible to offer at scale -- to the large number of
providers in need of training. Opportunities for practice typically rely on roleplays with other trainees with
limited experience, and feedback requires either direct supervision from an expert trainer or behavioral coding
from a trained coding team; these are costly, limited, and time consuming. AI-based technology can meet this
need, generating many opportunities for practice, and providing regular, actionable feedback. Many practice
opportunities coupled with rapid, performance-based feedback can enhance and expand training in
evidence-based counseling for AUDs in a scalable and cost-efficient manner.
 Lyssn.io?, Inc., (“Lyssn”) is a start-up developing AI-based technologies to support training, supervision,
and quality assurance of evidence-based counseling. Our goal is to develop innovative health technology
solutions that are objective, scalable, and cost efficient. ?Lyssn’s? team includes expertise in natural language
processing, machine learning, user-centered design, software engineering, and clinical expertise in
evidence-based counseling. Previous research demonstrated the basic utility of a prototype conversational
agent (ClientBot) for training counselors. Currently, ClientBot simulates a general mental health client who can
engage in open-ended interaction with trainees and provides immediate, performance-based feedback to
trainees using machine learning.
 The current Fast-Track SBIR proposal partners ?Lyssn? with Prevention Research Institute (PRI), who
has a long track-record of training counselors in evidence-based approaches for AUD and currently trains
approximately 1,250 counselors per year. Phase I will adapt ClientBot to an AUD training context, including
understanding PRI training workflows, assessing usability, and accuracy of machine learning based MI
feedback. Phase II will conduct a field-based usability trial and a randomized training trial (N = 200 PRI
trainees) to evaluate the effectiveness of ClientBot on learning of MI skills compared to a wait-list and PRI
training-as-usual. Analyses will also examine the hypothesized mechanisms of behavior change underlying
ClientBot’s MI skills training. The successful execution of this project will break the reliance on role plays with
peers and human judgment for training and performance-based feedback and support commercialization of a
ClientBot product for training of AUD counselors in evidence-based practices.

## Key facts

- **NIH application ID:** 10449463
- **Project number:** 4R44AA028463-02
- **Recipient organization:** LYSSN.IO, INC.
- **Principal Investigator:** David Charles Atkins
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $855,238
- **Award type:** 4N
- **Project period:** 2020-06-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10449463

## Citation

> US National Institutes of Health, RePORTER application 10449463, ClientBot: A conversational agent that supports skills practice and feedback for Motivational Interviewing for AUD (4R44AA028463-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10449463. Licensed CC0.

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