Artificial intelligence support in synchronous collaborative CAD activities: task-dependent effects and methodological implications

Abstract

This study explores the task-dependent effects of ChatGPT support in synchronous collaborative Computer-Aided Design (CAD) activities. While large language models (LLMs) such as ChatGPT are increasingly being integrated into engineering design, evidence on their influence in collaborative CAD activities remains limited. Addressing this gap, a between-subjects experiment was conducted with 22 pairs of engineering students performing three synchronous collaborative CAD tasks requiring varying combinations of CAD and domain-specific knowledge. To assess the task-dependent effects, the study evaluated taskwork through the number of CAD user actions, productivity, efficiency and CAD dominance. Results show that ChatGPT’s influence was highly task-dependent. In tasks relying primarily on CAD knowledge, ChatGPT-supported pairs exhibited lower productivity and efficiency. Conversely, in tasks requiring domain-specific engineering knowledge, ChatGPT support showed potential to improve efficiency. The study also offers methodological insights for designing controlled experiments involving ChatGPT support in collaborative CAD, underscoring that effective integration depends on task, tool and user-related factors. The findings further imply that integrating ChatGPT into engineering design education and practice requires developing competencies for critical and coordinated ChatGPT use within collaborative workflows, ensuring that ChatGPT complements rather than replaces designers. Future research should examine professional settings, teamwork and long-term adaptation to ChatGPT-supported synchronous collaborative CAD activities.