“The less I type, the better”: How AI Language Models can Enhance or Impede Communication for AAC Users

要旨

Users of augmentative and alternative communication (AAC) devices sometimes find it difficult to communicate in real time with others due to the time it takes to compose messages. AI technologies such as large language models (LLMs) provide an opportunity to support AAC users by improving the quality and variety of text suggestions. However, these technologies may fundamentally change how users interact with AAC devices as users transition from typing their own phrases to prompting and selecting AI-generated phrases. We conducted a study in which 12 AAC users tested live suggestions from a language model across three usage scenarios: extending short replies, answering biographical questions, and requesting assistance. Our study participants believed that AI-generated phrases could save time, physical and cognitive effort when communicating, but felt it was important that these phrases reflect their own communication style and preferences. This work identifies opportunities and challenges for future AI-enhanced AAC devices.

著者
Stephanie Valencia
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Richard Cave
UCL, LONDON, United Kingdom
Krystal Kallarackal
Google Research, Mountain View, California, United States
Katie Seaver
Google Research, Mountain View, California, United States
Michael Terry
Google, Cambridge, Massachusetts, United States
Shaun Kane
Google Research, Boulder, Colorado, United States
論文URL

https://doi.org/10.1145/3544548.3581560

動画

会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)

セッション: Visualization and Data

Room Y03+Y04
6 件の発表
2023-04-25 20:10:00
2023-04-25 21:35:00