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.

翻译：标准化调查虽能高效扩展规模，但牺牲了深度；而对话式访谈虽能提升回答质量，却以可扩展性和一致性为代价。本研究通过引入人工智能辅助对话式访谈框架，弥合了这两种方法之间的鸿沟。为评估该框架，我们开展了一项网络调查实验，将1800名参与者随机分配至使用大型语言模型（LLMs）的AI“聊天机器人”组。这些聊天机器人能动态引导受访者详细阐述，并对由人类研究者设计的固定问题中的开放式回答进行交互式编码。我们从编码准确性、回答质量和受访者体验三个维度评估了AI聊天机器人的表现。研究发现：即使未经特定调查任务的微调，AI聊天机器人在实时编码中仍表现尚可，尽管因受访者默许偏差导致误报错误略有增加。开放式回答更为详细且信息量更大，但这对受访者体验造成轻微影响。我们的研究结果凸显了利用LLMs增强的聊天机器人等人工智能方法改进网络调查中开放式数据收集的可行性。