部署机器人积极心理学教练提高大学生心理健康水平

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS User Modeling and User-Adapted Interaction Pub Date : 2023-04-01 Epub Date: 2022-07-11 DOI:10.1007/s11257-022-09337-8
Sooyeon Jeong, Laura Aymerich-Franch, Kika Arias, Sharifa Alghowinem, Agata Lapedriza, Rosalind Picard, Hae Won Park, Cynthia Breazeal
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引用次数: 0

摘要

尽管人们对心理健康的认识和支持不断提高,但在许多国家,大学生的心理健康状况却逐年下降。目前已经提出了几种心理健康互动技术,旨在使治疗服务更容易获得,但大多数技术只能为用户提供单向的被动内容,如心理教育、健康监测和临床评估。我们介绍的机器人教练不仅能提供互动式积极心理学干预,还能提供其他有用的技能,与大学生建立融洽的关系。我们在校园宿舍部署的可行性研究结果表明,机器人干预与学生心理健康、情绪和改变动机的提高有显著关联。我们还发现,学生的个性特征与干预结果、他们与机器人的工作联盟以及他们对干预的满意度都有关联。此外,学生与机器人之间的合作关系还与他们在改善健康状况的动机方面的前后变化有关。对学生行为线索的分析表明,一些语言和非语言行为与自我报告的干预结果的变化有关。对研究后访谈的定性分析表明,机器人教练的陪伴给学生留下了积极的印象,但也揭示了机器人教练设计中需要改进的地方。我们的可行性研究结果让我们深入了解了学习用户特征和识别行为线索如何帮助人工智能代理提供个性化的干预体验,从而获得更好的心理健康效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Deploying a robotic positive psychology coach to improve college students' psychological well-being.

Despite the increase in awareness and support for mental health, college students' mental health is reported to decline every year in many countries. Several interactive technologies for mental health have been proposed and are aiming to make therapeutic service more accessible, but most of them only provide one-way passive contents for their users, such as psycho-education, health monitoring, and clinical assessment. We present a robotic coach that not only delivers interactive positive psychology interventions but also provides other useful skills to build rapport with college students. Results from our on-campus housing deployment feasibility study showed that the robotic intervention showed significant association with increases in students' psychological well-being, mood, and motivation to change. We further found that students' personality traits were associated with the intervention outcomes as well as their working alliance with the robot and their satisfaction with the interventions. Also, students' working alliance with the robot was shown to be associated with their pre-to-post change in motivation for better well-being. Analyses on students' behavioral cues showed that several verbal and nonverbal behaviors were associated with the change in self-reported intervention outcomes. The qualitative analyses on the post-study interview suggest that the robotic coach's companionship made a positive impression on students, but also revealed areas for improvement in the design of the robotic coach. Results from our feasibility study give insight into how learning users' traits and recognizing behavioral cues can help an AI agent provide personalized intervention experiences for better mental health outcomes.

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来源期刊
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction 工程技术-计算机:控制论
CiteScore
8.90
自引率
8.30%
发文量
35
审稿时长
>12 weeks
期刊介绍: User Modeling and User-Adapted Interaction provides an interdisciplinary forum for the dissemination of novel and significant original research results about interactive computer systems that can adapt themselves to their users, and on the design, use, and evaluation of user models for adaptation. The journal publishes high-quality original papers from, e.g., the following areas: acquisition and formal representation of user models; conceptual models and user stereotypes for personalization; student modeling and adaptive learning; models of groups of users; user model driven personalised information discovery and retrieval; recommender systems; adaptive user interfaces and agents; adaptation for accessibility and inclusion; generic user modeling systems and tools; interoperability of user models; personalization in areas such as; affective computing; ubiquitous and mobile computing; language based interactions; multi-modal interactions; virtual and augmented reality; social media and the Web; human-robot interaction; behaviour change interventions; personalized applications in specific domains; privacy, accountability, and security of information for personalization; responsible adaptation: fairness, accountability, explainability, transparency and control; methods for the design and evaluation of user models and adaptive systems
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