Conversational interviews are commonly used to complement structured surveys by eliciting rich and contextualized responses, which are typically analyzed qualitatively. However, their potential contribution to quantitative measurement remains underexplored. In this paper, we introduce ConvScale, an AI-supported approach that transforms psychometric scales into natural conversational interviews while preserving the original measurement structure. Based on interview data, ConvScale predicts item-level scores and aggregates them to derive scale-based assessments. In a within-subjects study with 18 participants, our results show that ConvScale-derived scores align closely with participants' self-report scores at both the item and construct levels, while maintaining moderate internal reliability; however, the structural validity was inadequate. In light of this, we discussed the potential of supporting quantitative measurement through interviews and proposed implications for future designs.

翻译：对话式访谈通常用于补充结构化调查，以获取丰富且情境化的回答，这些回答通常通过定性方法进行分析。然而，其在定量测量方面的潜在贡献仍未得到充分探索。本文提出ConvScale，这是一种人工智能支持的方法，能够将心理测量量表转化为自然的对话式访谈，同时保留原有的测量结构。基于访谈数据，ConvScale预测项目级别的分数并将其聚合，从而得出基于量表的评估。在一项包含18名参与者的组内研究中，我们的结果表明，ConvScale导出的分数在项目水平和构念水平上均与参与者的自我报告分数高度一致，同时保持了中等程度的内部信度；然而，其结构效度不足。鉴于此，我们探讨了通过访谈支持定量测量的潜力，并对未来设计提出了启示。