Simulating Word Suggestion Usage in Mobile Typing to Guide Intelligent Text Entry Design

2026-02-06

AI Summary

This work proposes WSTypist, a novel computational model that integrates rational modeling with hierarchical typing control to address the limitations of current intelligent text input systems, which are hindered by insufficient understanding of users’ cognitive mechanisms in adopting AI suggestions and reliance on time-consuming longitudinal user studies. By leveraging reinforcement learning, WSTypist simulates users’ high-level decision-making processes during mobile typing, explicitly balancing efficiency, spelling uncertainty, and reliance on AI-generated suggestions. The framework successfully reproduces individual differences and human-like strategies in suggestion usage, enabling “what-if” analyses during the design phase of diverse input systems. Evaluated across four scenarios, it accurately predicts user adaptation behaviors, substantially reducing the need for extensive empirical user studies.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Intelligent User Interfaces
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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. It builds on recent hierarchical control models of typing, but focuses on the cognitive mechanisms that underlie the high-level decision-making for effectively integrating word suggestions into manual typing: assessing efficiency gains, considering orthographic uncertainties, and including personal reliance 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 computational rationality models 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.

Problem

• intelligent text entry
• word suggestion
• cognitive mechanisms
• behavioral adaptation
• mobile typing

Innovation

• reinforcement learning
• computational rationality
• word suggestion
• typing simulation
• intelligent text entry