Automatic multimodal UX improvement recommendations from LLM agent user simulations
Evaluating user experience (UX) on live websites through user testing is expensive, subjective, and difficult to scale. LLM agents offer a promising route to automating UX testing by simulating realistic user behaviour. However, existing simulation approaches typically lack multimodality and require time-consuming manual review to extract actionable insights. We formalise UX improvement recommendation from simulation data as a structured natural language generation and ranking problem, and establish an evaluation protocol using expert annotation and LLM-as-a-Judge. We present AMUSER, a multimodal framework which simulates user behaviour and automatically generates prioritised UX improveme...
| Authors | Anu Chowdhury, Bin Wu, Hossein A. Rahmani et al. |
|---|---|
| Primary category | cs.CL |
| Published | 19 September 2026 |
| Last updated at source | 19 September 2026 |
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