Law Meets GenAI: Using Artificial Intelligence to Derive Conceptual Models from Legal RegulationsOpen Access

Nguyen, Binh An Patrick; Scholta, Hendrik; Roth-Isigkeit, David; Djeffal, Christian; Chasin, Friedrich

Research article in digital collection (conference) | Peer reviewed

Abstract

Artificial intelligence (AI) and conceptual models are both important to public organizations. AI and generative AI (GenAI) can help to cope with an increasing resource shortage, workload, and requirements, while conceptual models are essential for the design of IT systems. However, the combination of both, the creation of conceptual models using GenAI tools in public organizations, has been barely addressed in extant research. Thus, we investigate (1) how legal experts use GenAI tools when deriving conceptual models for public services from legal regulations and (2) what their experiences are in this use. In a qualitative study with 18 administrative legal experts we obtained various insights. For instance, we show that the participants either submitted strict instructions or conducted open conversations and they followed a top-down, bottom-up or combined approach in their analysis. The GenAI tools performed better in generating text-based models (forms) than graphic-based models (process models, decision trees).

Details about the publication

Name of the repositoryhttps://scholarspace.manoa.hawaii.edu
Book titleHawaii International Conference on System Sciences 2026
Article number0233
Version1
StatusPublished
Release year2026
Language in which the publication is writtenEnglish
Conference59th Hawaii International Conference on System Sciences (HICSS), Maui, Hawaii, United States
Link to the full texthttps://hdl.handle.net/10125/111681
KeywordsLaw; Large Language Model; Prompting; Conceptual Modeling; Digital Public Service

Authors from the University of Münster

Nguyen, Binh An Patrick
Department of Information Systems (WI)
Scholta, Hendrik
Department of Information Systems (WI)

Projects the publication originates from

Duration: 01/01/2023 - 31/12/2026 | 1st Funding period
Funded by: DFG - Research Unit
Type of project: Main DFG-project hosted at University of Münster
Duration: 01/01/2023 - 31/07/2024 | 1st Funding period
Funded by: DFG - Research Unit
Type of project: Subproject in DFG-joint project hosted at University of Münster