The way you assess matters: User interaction design of survey chatbots for mental health

IF 5.3 2区 计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS
Yucheng Jin, Li Chen, Xianglin Zhao, Wanling Cai
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Abstract

The global pandemic has pushed human society into a mental health crisis, prompting the development of various chatbots to supplement the limited mental health workforce. Several organizations have employed mental health survey chatbots for public mental status assessments. These survey chatbots typically ask closed-ended questions (Closed-EQs) to assess specific psychological issues like anxiety, depression, and loneliness, followed by open-ended questions (Open-EQs) for deeper insights. While Open-EQs are naturally presented conversationally in a survey chatbot, Closed-EQs can be delivered as embedded forms or within conversations, with the length of the questionnaire varying according to the psychological assessment. This study investigates how the interaction style of Closed-EQs and the questionnaire length affect user perceptions regarding survey credibility, enjoyment, and self-awareness, as well as their responses to Open-EQs in terms of quality and self-disclosure in a survey chatbot. We conducted a 2 (interaction style: form-based vs. conversation-based) × 3 (questionnaire length: short vs. middle vs. long) between-subjects study (N=213) with a loneliness survey chatbot. The results indicate that the form-based interaction significantly enhances the perceived credibility of the assessment, thereby improving response quality and self-disclosure in subsequent Open-EQs and fostering self-awareness. We discuss our findings for the interaction design of psychological assessment in a survey chatbot for mental health.

在心理健康调查聊天机器人中,采用封闭式问题的心理评估设计对用户回答开放式问题的影响
全球大流行将人类社会推向了心理健康危机,促使人们开发各种聊天机器人来补充有限的心理健康劳动力。一些组织已经使用心理健康调查聊天机器人进行公共心理状态评估。这些调查聊天机器人通常会提出封闭式问题(Closed-EQs)来评估特定的心理问题,如焦虑、抑郁和孤独,然后再提出开放式问题(Open-EQs)来深入了解。在调查聊天机器人中,开放式问卷是以对话形式自然呈现的,而封闭式问卷则可以嵌入表单或在对话中提供,问卷的长度根据心理评估的不同而不同。本研究调查了封闭式问卷的交互方式和问卷长度如何影响用户对调查可信度、乐趣和自我意识的看法,以及他们在调查聊天机器人中对开放式问卷在质量和自我披露方面的反应。我们使用一个孤独感调查聊天机器人进行了一项2(交互方式:基于表格的交互方式与基于对话的交互方式)×3(问卷长度:短问卷与中长问卷)的主体间研究(N=213)。结果表明,基于表单的互动能显著提高评估的可信度,从而提高后续开放式问卷的回答质量和自我披露程度,并促进自我认知。我们讨论了心理健康调查聊天机器人中心理评估互动设计的研究结果。
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来源期刊
International Journal of Human-Computer Studies
International Journal of Human-Computer Studies 工程技术-计算机:控制论
CiteScore
11.50
自引率
5.60%
发文量
108
审稿时长
3 months
期刊介绍: The International Journal of Human-Computer Studies publishes original research over the whole spectrum of work relevant to the theory and practice of innovative interactive systems. The journal is inherently interdisciplinary, covering research in computing, artificial intelligence, psychology, linguistics, communication, design, engineering, and social organization, which is relevant to the design, analysis, evaluation and application of innovative interactive systems. Papers at the boundaries of these disciplines are especially welcome, as it is our view that interdisciplinary approaches are needed for producing theoretical insights in this complex area and for effective deployment of innovative technologies in concrete user communities. Research areas relevant to the journal include, but are not limited to: • Innovative interaction techniques • Multimodal interaction • Speech interaction • Graphic interaction • Natural language interaction • Interaction in mobile and embedded systems • Interface design and evaluation methodologies • Design and evaluation of innovative interactive systems • User interface prototyping and management systems • Ubiquitous computing • Wearable computers • Pervasive computing • Affective computing • Empirical studies of user behaviour • Empirical studies of programming and software engineering • Computer supported cooperative work • Computer mediated communication • Virtual reality • Mixed and augmented Reality • Intelligent user interfaces • Presence ...
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