ECHO：一种使用对话式助手开展匿名定性研究的自动化情境探究框架
ECHO: An Automated Contextual Inquiry Framework for Anonymous Qualitative Studies using Conversational Assistants
AI总结：
本研究提出自动化情境探究框架ECHO，通过自适应对话界面、自动编码等技术实现定性研究流程自动化，在保护隐私的同时提升研究效率与质量，经心理健康研究验证其表现优于传统方法。
摘要：
定性研究通常会采用情境探究（contextual inquiry），即一种包含对少量研究参与者进行现场深度观察与访谈的实地研究，以深入理解参与者在感兴趣领域内产生相关想法、行动与体验的原因和背景。尤其在敏感数据研究中，情境探究可能因参与者隐私，以及需本人到场、需手动分析所收集定性数据等物理限制而成为一项具有挑战性的任务。在本研究中，我们介绍了Enquête Contextuelle Habile Ordinateur（ECHO）——这是一个虚拟助手框架，可将此前开展情境探究、分析受访者主观定性数据的手动流程实现自动化。ECHO可实现情境探究全流程的自动化，同时不会牺牲隐私保护效果或回复的完整性。其自适应对话界面支持受访者以自由形式的自然语言提供非结构化或半结构化回复，让研究人员能够从参与者的回复数据中挖掘更完整的叙事脉络。该框架支持基于回复的探索性提问，并可自动执行定性数据编码方法，从而让探究者能够更深入地挖掘关联问题，开展更优质的因果分析。它着力解决手动标注的局限性，为自由形式文本引入标准化规范，并消除不同评审者在主观回复评判中的视角偏差。我们开展了一项参与式心理健康研究，共招募167名年轻成年人，将其分为两个焦点小组：其中一组接受传统情境探究，另一组通过ECHO以虚拟方式接受探究。结果显示，在参与者透明度、回复细节丰富度，以及每位参与者完成端到端探究所需的中位时间方面，ECHO的表现均更优。
英文摘要：
Qualitative research studies often employ a contextual inquiry, or a field study that involves in-depth observation and interviews of a small sample of study participants, in-situ, to gain a robust understanding of the reasons and circumstances that led to the participant's thoughts, actions, and experiences regarding the domain of interest. Contextual inquiry, especially in sensitive data studies, can be a challenging task due to reasons such as participant privacy, as well as physical constraints such as in-person presence and manual analysis of the qualitative data gathered. In this work, we discuss Enquête Contextuelle Habile Ordinateur (ECHO); a virtual-assistant framework to automate the erstwhile manual process of conducting contextual inquiries and analysing the respondents' subjective qualitative data. ECHO automates the contextual inquiry pipeline, while not compromising on privacy preservation or response integrity. Its adaptive conversational interface enables respondents to provide unstructured or semi-structured responses in free-form natural language, allowing researchers to explore larger narratives in participant response data. It supports response-driven exploratory questions and automates coding methodologies for qualitative data, thus enabling the inquirer to dive deeper into correlated questions and to do better cause-effect analysis. It focuses on addressing the limitations of manual annotation, bringing standardisation to free-form text, and eliminating perspective bias amongst different reviewers of subjective responses. A participatory mental health study was conducted on 167 young adults bifurcated into two focus groups; one of which was administered a conventional contextual inquiry, and the other via ECHO, virtually. ECHO outperformed on participant transparency, response detail and median time required for end-to-end inquiry completion, per participant.
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