Feasibility Study of Collaborative Modification in Spatial Safety Assessment by Generative AI

Abstract

This research aims to achieve a human-AI collaborative design and evaluation in system design activities. In previous study, we proposed a support tool for risk assessment using system and spatial design information provided by designers. However, it is challenging for designers to provide the design information with uniform abstraction level for necessary and sufficient assessment. In this paper, we experimentally study the feasibility of using generative AI for collaborative modification to uniformize the abstraction level of the design information in the previous study. The collaborative modification process is divided into four information processing functions. In each function, two large language models with different knowledge levels are examined to evaluate the impact of knowledge volume and retrieval methods. The evaluation using the use case and activity diagrams of the previous study suggests that knowledge retrieval methods, such as prompt engineering, are more essential than knowledge volume in achieving collaborative modification.