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 Enqu\^ete 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.

翻译：定性研究常采用情境询问或实地研究，通过对少量研究参与者进行深入观察和现场访谈，以全面了解参与者在兴趣领域内的思想、行为及经历背后的原因与情境。然而，在敏感数据研究中，情境询问面临挑战，包括参与者隐私问题以及现场参与和定性数据手动分析等物理限制。本文提出ECHO（Enquête Contextuelle Habile Ordinateur）——一种虚拟助手框架，用于自动化原本手动进行情境询问和分析受访者主观定性数据的过程。ECHO自动化了情境询问流程，同时不损害隐私保护或响应完整性。其自适应对话界面允许受访者以自由形式的自然语言提供非结构化或半结构化响应，从而使研究人员能够探索参与者响应数据中的更广泛叙事。它支持响应驱动的探索性问题，并自动化定性数据的编码方法，使询问者能够深入探讨相关问题并进行更好的因果分析。该框架专注于解决手动标注的局限性，为自由形式文本带来标准化，并消除不同审阅者对主观响应的视角偏差。一项参与式心理健康研究对167名年轻成年人进行，分为两个焦点组：一组采用传统情境询问，另一组通过ECHO进行虚拟询问。结果显示，ECHO在参与者透明度、响应详细程度以及每位参与者完成端到端询问的中位时间方面表现更优。