SimAB: Simulating A/B Tests with Persona-Conditioned AI Agents for Rapid Design Evaluation
A/B testing is a standard method for validating design decisions, yet its reliance on real user traffic limits iteration speed and makes certain experiments impractical. We present SimAB, a system that reframes A/B testing as a fast, privacy-preserving simulation using persona-conditioned AI agents. Given design screenshots and a conversion goal, SimAB generates user personas, deploys them as agents that state their preference, aggregates results, and synthesizes rationales. Through a formative study with experimentation practitioners, we identified scenarios where traffic constraints hinder testing, including low-traffic pages, multi-variant comparisons, micro-optimizations, and privacy-...
| Authors | Tim Rieder, Marian Schneider, Mario Truss et al. |
|---|---|
| Primary category | cs.HC |
| Published | 1 March 2026 |
| Last updated at source | 1 March 2026 |
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