Simulating Word Suggestion Usage in Mobile Typing to Guide Intelligent Text Entry Design
This work addresses the problem of designing better mobile typing interfaces for users by providing a simulation tool to predict user adaptation, though it is incremental as it builds on existing hierarchical control models.
The researchers tackled the challenge of improving intelligent text entry systems by developing WSTypist, a reinforcement learning-based model that simulates how typists integrate word suggestions into mobile typing, demonstrating its ability to reproduce human-like strategies and inform design decisions in four cases, reducing the need for long-term user studies.
Intelligent text entry (ITE) methods, such as word suggestions, are widely used in mobile typing, yet improving ITE systems is challenging because the cognitive mechanisms behind suggestion use remain poorly understood, and evaluating new systems often requires long-term user studies to account for behavioral adaptation. We present WSTypist, a reinforcement learning-based model that simulates how typists integrate word suggestions into typing. We extend recent hierarchical control models of typing, by identifying and implementing important cognitive mechanisms that underlie the high-level decision-making for integrating word suggestions into manual typing: considering orthographic processes, assessing efficiency gains, and including personal preference on AI support. Our evaluations show that WSTypist simulates diverse human-like suggestion-use strategies, reproduces individual differences, and generalizes across different systems. Importantly, we demonstrate on four design cases how a computational rationality model can be used to inform what-if analyses during the design process, by simulating how users might adapt to changes in the UI or in the algorithmic support, reducing the need for long-term user studies.