Understanding User Intent in Generative Information Retrieval through Tasks Characteristics: Insights from a Meta‐analysis

ABSTRACT Generative information retrieval (GenIR) systems represent a paradigm shift in interpreting and addressing user needs, yet their effectiveness remains constrained by limited interaction design capabilities for accurate intent understanding. Recognizing the connection between search intentions and task characteristics, this study identifies two categories of task characteristics that can inform the interaction design of GenIR systems: (a) general task characteristics, which consistently influence search behaviors across diverse contexts and user groups, and (b) specific task characteristics, which are particularly influential in certain contexts or among specific user groups. To identify these characteristics, the study quantitatively synthesizes existing research through a meta‐analysis of 22 experimental studies. The findings suggest that task difficulty, task urgency, and task sources are key general task characteristics. However, task goal specificity is more influential in learning contexts, while task complexity predominates in daily life and healthcare contexts, as well as for middle school students. Based on these findings, this study develops a task‐aware interaction design framework that strategically guides users in articulating both general and specific task characteristics through iterative dialogues. This design optimizes intent understanding by achieving: (1) operational efficiency through the analysis of general characteristics, and (2) personalized adaptation through the interpretation of specific characteristics.