AI辅助的对话访谈：对数据质量和受访者体验的影响
AI-Assisted Conversational Interviewing: Effects on Data Quality and Respondent Experience
AI总结：
本研究提出AI辅助对话访谈框架，通过聊天机器人提升开放式数据收集质量，发现其在编码准确性上表现良好，但需平衡受访者体验与数据详细度。
摘要：
标准化调查在规模上效率高，但牺牲了深度，而对话访谈提高了响应质量，但以可扩展性和一致性为代价。本研究通过引入一个AI辅助对话访谈的框架来弥合这两种方法之间的差距。为了评估该框架，我们进行了一项网络调查实验，其中1800名参与者被随机分配到使用大型语言模型（LLMs）动态探测受访者进行详细说明并互动编码开放式回答的AI'聊天机器人'中，这些开放式回答是由人类研究人员开发的固定问题。我们评估了AI聊天机器人在实时编码方面的表现，包括编码准确性、响应质量和受访者体验。我们的发现表明，即使没有针对特定调查的微调，AI聊天机器人在实时编码中表现尚可，尽管由于受访者顺从偏差导致假阳性错误略有增加。开放式回答更加详细和有信息量，但这也略微牺牲了受访者的体验。我们的发现突显了使用LLMs增强的聊天机器人等AI方法来增强网络调查中开放式数据收集的可行性。
英文摘要：
Standardized surveys scale efficiently but sacrifice depth, while conversational interviews improve response quality at the cost of scalability and consistency. This study bridges the gap between these methods by introducing a framework for AI-assisted conversational interviewing. To evaluate this framework, we conducted a web survey experiment where 1,800 participants were randomly assigned to AI 'chatbots' which use large language models (LLMs) to dynamically probe respondents for elaboration and interactively code open-ended responses to fixed questions developed by human researchers. We assessed the AI chatbot's performance in terms of coding accuracy, response quality, and respondent experience. Our findings reveal that AI chatbots perform moderately well in live coding even without survey-specific fine-tuning, despite slightly inflated false positive errors due to respondent acquiescence bias. Open-ended responses were more detailed and informative, but this came at a slight cost to respondent experience. Our findings highlight the feasibility of using AI methods such as chatbots enhanced by LLMs to enhance open-ended data collection in web surveys.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
