Agent simulation predicts human workload before task engagement
Synthetic TLX: Forecasting Human Workload Using Agent Simulation
Human-Computer Interaction
Summary
Measuring how hard a task feels to a person usually happens after they finish it, which can be too late to improve things. This paper presents Synthetic TLX, a method that uses computer agents to predict how much effort a person will feel before starting the task. The researchers tested if these agent-generated predictions match what people actually report. They found agents give similar workload estimates when given a specific human-like role and detailed task simulation, although they focus on different causes of workload compared to humans.
What this means in practice
- •For interface designers: Forecast how demanding a new user interface will feel before real users try it, enabling better design decisions early.
- •For game developers: Predict player workload for upcoming game levels to balance challenge without extensive playtesting.
Authors
Tzu-Sheng Kuo, Carrie J. Cai, Meredith Ringel Morris, Michael Terry
Abstract
Assessing human workload for technology-mediated tasks helps prevent task failure caused by poor technology design. Traditionally, workload is assessed retrospectively using the NASA Task Load Index (TLX) after humans complete a task. What if we could forecast workload before a human attempts a task using agent simulation? We introduce Synthetic TLX, a new paradigm for proactive workload estimation that predicts NASA TLX scores for a given task, unlocking novel interaction opportunities and evaluation methods. To understand its viability, we conducted three experiments comparing human and agent-generated scores to evaluate where they align and diverge. We found agent estimates align with human scores particularly when prompted with a human persona and active task simulation. However, agents and humans diverge in the sources of workload they are sensitive to. Based on our findings, we present three applications to showcase Synthetic TLX's potential and discuss the future of workload-aware human-AI interaction.