从野外测试心理健康聊天机器人中获得的见解和经验教训

C. Potts, Raymond R. Bond, M. Mulvenna, E. Ennis, A. Bickerdike, Edward K. Coughlan, T. Broderick, Con Burns, M. McTear, L. Kuosmanen, H. Nieminen, K. Boyd, B. Cahill, A. Vakaloudis, I. Dhanapala, A. Vartiainen, C. Kostenius, M. Malcolm
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引用次数: 2

摘要

这项研究报告了聊天机器人(ChatPal)的发展和“野外”试验,它能促进良好的心理健康。采用以利益相关者为中心的设计方法,最终用户、心理健康专业人员和服务用户参与以积极心理学为中心的设计。从7月20日至3月21日,对聊天机器人的野外使用情况进行了调查。使用事件日志数据对使用度量进行了探索性分析。在k均值聚类中使用用户任期、唯一使用天数、聊天机器人总交互和平均每日交互来识别用户原型。该聊天机器人的用户年龄层(18-65岁以上)和性别各不相同,主要是居住在爱尔兰的人。K-means聚类识别出三种不同使用特征的用户群:零星用户(n=4)、频繁瞬时用户(n=38)和放弃用户(n=169)。本研究强调了事件日志数据分析对改进心理健康聊天机器人的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Insights and lessons learned from trialling a mental health chatbot in the wild
This study reports on the development and ‘in the wild’ trialling of a chatbot (ChatPal) which promotes good mental wellbeing. A stakeholder-centered approach for design was adopted where end users, mental health professionals and service users were involved in the design which was centered around positive psychology. In the wild usage of the chatbot was investigated from Jul-20-Mar-21. Exploratory analyses of usage metrics were carried out using the event log data. User tenure, unique usage days, total chatbot interactions and average daily interactions were used in K-means clustering to identify user archetypes. The chatbot was used by a variety of age groups (18-65+) and genders, mainly those living in Ireland. K-means clustering identified three clusters: sporadic users (n=4), frequent transient users (n=38) and abandoning users (n=169) each with distinct usage characteristics. This study highlights the importance of event log data analysis for making improvements to the mental health chatbot.
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