Evaluating the Viability of Additive Models to Predict Task Completion Time for 3D Interactions in Augmented Reality

2026-01-30

AI Summary

This work addresses the absence of additive models capable of predicting task completion times in three-dimensional augmented reality (AR) interfaces. Building upon the Keystroke-Level Model (KLM) framework, the authors integrate atomic task time parameters from prior literature to construct the first additive predictive model tailored for 3D AR interaction, supporting multimodal input evaluation. The model’s applicability across different input modalities is systematically validated through two user studies involving menu selection and complex manipulation tasks. Experimental results demonstrate that the model achieves prediction errors below 20% in both studies, effectively estimating both absolute and relative task completion times in 3D AR environments. This study provides the first empirical evidence of the feasibility and accuracy of additive modeling approaches for 3D AR interaction.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Humans and AI: Interaction Techniques and Devices
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Additive models of interaction performance, such as the Keystroke-Level Model (KLM), are tools that allow designers to compare and optimize the performance of user interfaces by summing the predicted times for the atomic components of a specific interaction to predict the total time it would take to complete that interaction. There has been extensive work in creating such additive models for 2D interfaces, but this approach has rarely been explored for 3D user interfaces. We propose a KLM-style additive model, based on existing atomic task models in the literature, to predict task completion time for 3D interaction tasks. We performed two studies to evaluate the feasibility of this approach across multiple input modalities, with one study using a simple menu selection task and the other a more complex manipulation task. We found that several of the models from the literature predicted actual task performance with less than 20% error in both the menu selection and manipulation study. Overall, we found that additive models can predict both absolute and relative performance of input modalities with reasonable accuracy.

Problem

• additive models
• task completion time
• 3D interaction
• augmented reality
• Keystroke-Level Model

Innovation

• additive models
• Keystroke-Level Model
• 3D interaction
• augmented reality
• task completion time prediction