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

项目总结/摘要 数以百万计的美国人需要循证咨询，如动机访谈（MI）， 酒精使用障碍（AUDs）。为了在像MI这样的循证实践中培养能力， 学员需要有充分的实践机会，并立即获得基于绩效的技能反馈， 他们在学习。然而，这是具有挑战性的，如果不是不可能提供的规模-大量的 需要培训的供应商。实践的机会通常依赖于与其他学员的角色扮演， 经验有限，反馈需要专家培训师的直接监督或行为编码 从一个训练有素的编码团队;这些都是昂贵的，有限的，耗时。基于AI的技术可以满足这一点 需要，创造许多实践机会，并提供定期的，可操作的反馈。许多实践 机会加上快速的、基于绩效的反馈可以加强和扩大培训， 以可扩展和具有成本效益的方式为AUD提供循证咨询。 Lyssn.io?,股份有限公司、（“Lyssn”）是一家开发基于人工智能的技术的初创公司，以支持培训，监督， 和循证咨询的质量保证。我们的目标是开发创新的健康技术 客观、可扩展且经济高效的解决方案。?莱森的？团队包括自然语言专业知识 处理，机器学习，以用户为中心的设计，软件工程和临床专业知识， 循证咨询以前的研究表明，原型会话的基本效用 代理（ClientBot）用于培训辅导员。目前，ClientBot模拟了一个一般的心理健康客户， 与学员进行开放式互动，并提供即时的、基于绩效的反馈， 使用机器学习的学员。 目前的快速通道SBIR提案合作伙伴？莱森预防研究所（PRI）， 在AUD循证方法培训辅导员方面有很长的记录，目前正在培训 每年约有1，250名顾问。第一阶段将使ClientBot适应AUD培训环境，包括 了解PRI培训工作流程，评估基于机器学习的MI的可用性和准确性 反馈第二阶段将进行一项基于现场的可用性试验和一项随机培训试验（N = 200 PRI 学员），以评估ClientBot与等待列表和PRI相比在学习MI技能方面的有效性 训练如常。分析还将检查行为改变的假设机制， ClientBot的MI技能培训。该项目的成功实施将打破对角色扮演的依赖， 同行和人的判断，培训和基于性能的反馈，并支持商业化的一个 ClientBot产品，用于在循证实践中培训AUD咨询师。

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.