Designing Human-AI System for Legal Research: A Case Study of Precedent Search in Chinese Law
Jiarui Guan, Ruishi Zou, Jiajun Zhang, Kimpan Xin, Bingsu He, Zhuhe Zhang, Chen Ye
TL;DR
This paper investigates why AI tools underperform in legal practice due to end-to-end designs and presents a human-AI collaboration framework, focusing on precedent search within Chinese law. Through a qualitative study with five Chinese practitioners, it reveals a three-phase, non-linear precedent-search workflow and identifies key friction points: semantic term generation, reading support for lengthy documents, and tool-context switching. It proposes a design vision for a step-by-step, user-controlled AI assistant that leverages semantic zoom and faceted search to support sensemaking while preserving human oversight. The work offers a practical design direction for responsible AI-enabled legal research and highlights areas for broader validation across roles, tasks, and legal systems.
Abstract
Recent advancements in AI technology have seen researchers and industry professionals actively exploring the application of AI tools in legal workflows. Despite this prevailing trend, legal practitioners found that AI tools had limited effectiveness in supporting everyday tasks, which can be partly attributed to their design. Typically, AI legal tools only offer end-to-end interaction: practitioners can only manipulate the input and output but have no control over the intermediate steps, raising concerns about AI tools' performance and ethical use. To design an effective AI legal tool, as a first step, we explore users' needs with one specific use case: precedent search. Through a qualitative study with five legal practitioners, we uncovered the precedent search workflow, the challenges they face using current systems, and their concerns and expectations regarding AI tools. We conclude our exploration with an initial prototype to reflect the design implications derived from our findings.