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

项目摘要/摘要 数以百万计的美国人需要循证咨询，如动机访谈(MI)， 每年的酒精使用障碍(AUD)。为了在像MI这样的循证实践中培养能力， 学员需要有充分的练习机会，以及对以下技能的即时、基于绩效的反馈 他们正在学习。然而，这是具有挑战性的，如果不是不可能提供的规模-对大量 需要培训的供应商。练习的机会通常依赖于与其他受训者的角色扮演 经验有限，反馈需要专家培训师的直接监督或行为编码 来自训练有素的编码团队；这些都是昂贵、有限和耗时的。基于人工智能的技术可以满足这一要求 需要，创造许多实践机会，并提供定期的、可操作的反馈。多多练习 机会与快速、基于绩效的反馈相结合，可以增强和扩大 以可扩展和经济高效的方式为AUDS提供循证咨询。 Lyssn.io？，Inc.是一家初创企业，开发基于人工智能的技术，以支持培训、监督 循证咨询的质量保证。我们的目标是发展创新的健康技术 客观、可扩展且经济高效的解决方案。？莱森的？团队包括自然语言方面的专业知识 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。先前的研究证明了对话原型的基本用处 用于培训顾问的代理(ClientBot)。目前，ClientBot模拟一个普通的心理健康客户，他可以 与学员进行开放式互动，并提供基于绩效的即时反馈 实习生使用机器学习。 目前的快速通道SBIR提案合作伙伴？Lyssn？与预防研究所(PRI)，世卫组织 在以证据为基础的方法方面培训澳大利亚大学的辅导员有很长的记录，目前正在培训 每年约有1250名辅导员。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。第二阶段将进行现场可用性试验和随机培训试验(N=200 PRI 学员)评估与等待名单和优先级相比，ClientBot在学习MI技能方面的有效性 像往常一样训练。分析还将检验潜在的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 针对培训和基于绩效的反馈的同行和人类判断，并支持 用于培训澳大利亚大学辅导员循证实践的ClientBot产品。

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.