EXPRESS: Robotic Artistry: Four Surprise Pathways for GenAI-Assisted Abductive Theorization
提出生成式AI可通过四条路径（多重透镜、浮现缺失、桥接层次、检验类别）辅助定性研究中的溯因分析，增强理论发现中的惊喜元素，并以组织未来构建的实证案例展示人机协作如何产生单独一方无法获得的洞见。
The role of generative AI (GenAI) in qualitative research is subject to intense debate, with critics warning that it could undermine human sensitivity and contextual understanding. We argue that thoughtfully integrated AI can enhance qualitative research by promoting discovery and surprise, both essential elements of theory building. Drawing on Picasso’s iterative abstraction in The Bull and Refik Anadol’s Unsupervised exhibition at MoMA, we treat reduction and synthesis as complementary engines of insight and identify four surprise generation pathways in GenAI-assisted abductive analysis: multiplying lenses, surfacing absences, bridging levels, and testing categories. When paired with interpretive vigilance operationalized through four heuristics, meaning-making remains squarely in human hands. Using an empirical example of organizational future-making, we show how AI’s pattern recognition combined with human interpretation reveals insights neither could achieve alone. Our framework positions AI as a collaborative partner that amplifies researchers’ capacity for theoretical discovery while preserving methodological rigor and interpretive depth.