A user model to directly compare two unmodified interfaces: a study of including errors and error corrections in a cognitive user model

Farnaz Tehranchi, Amirreza Bagherzadeh, Frank E. Ritter

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

User models that can directly use and learn how to do tasks with unmodified interfaces would be helpful in system design to compare task knowledge and times between interfaces. Including user errors can be helpful because users will always make mistakes and generate errors. We compare three user models: an existing validated model that simulates users' behavior in the Dismal spreadsheet in Emacs, a newly developed model that interacts with an Excel spreadsheet, and a new model that generates and fixes user errors. These models are implemented using a set of simulated eyes and hands extensions. All the models completed a 14-step task without modifying the system that participants used. These models predict that the task in Excel is approximately 20% faster than in Dismal, including suggesting why, where, and how much Excel is a better design. The Excel model predictions were compared to newly collected human data (N = 23). The model's predictions of subtask times correlate well with the human data (r2 =.71). We also present a preliminary model of human error and correction based on user keypress errors, including 25 slips. The predictions to data comparison suggest that this interactive model that includes errors moves us closer to having a complete user model that can directly test interface design by predicting human behavior and performing the task on the same interface as users. The errors from the model's hands also allow further exploration of error detection, error correction, and different knowledge types in user models.

Original languageEnglish (US)
Article numbere27
JournalArtificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
Volume37
DOIs
StatePublished - Jan 2 2024

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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