A Conceptual Framework for Implementing AI in Digital Human Modelling Tools

Paper in proceeding, 2026

Digital human modelling (DHM) tools enable engineers to assess ergonomic conditions and predict risk factors before workstation designs are finalized, supporting proactive approaches to workplace safety. Despite these advantages, DHM tools remain underutilized in industry compared with computer aided design (CAD) and other digital engineering tools. Key documented barriers of the adoption of DHM tools include steep learning curves, requirements for specialized ergonomics expertise, time-consuming manual setup of simulations, and limited documentation resources. Meanwhile, advances in artificial intelligence (AI), particularly large language models (LLMs), offer new possibilities for supporting engineering workflows through natural-language interaction and automated analysis. However, structured approaches for integrating AI into DHM tools are largely absent. This paper proposes a framework for integrating AI capabilities into DHM tools and presents a prototype implementation to assess feasibility. The framework defines four functional roles: Dialog (natural-language interaction), Expert (documentation-grounded guidance), Analyser (ergonomics data interpretation), and Executor (command translation to simulation operations). The prototype, connected to a commercial DHM tool, demonstrates how AI-based support can guide users through modelling steps, automate repetitive tasks, and identify critical ergonomic indicators, illustrating how such support could potentially lower the threshold for non-expert users, reduce manual effort, and contribute to more systematic design of workstations that support worker well-being. The current work focuses on the conceptual framework and technical feasibility; formal user validation remains as future work.