# It's Complicated: On the Design and Evaluation of AI-Powered AAC Interfaces

arXiv: https://arxiv.org/abs/2606.24854 · HTML: https://mlsanity.com/paper/2606.24854/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 70 references that are in the map.
- First posted on arXiv 23 Jun 2026.

## Builds on (most cited first, 3 of 3)

- [One Does Not Simply 'Mm-hmm': Exploring Backchanneling in the AAC Micro-Culture](https://mlsanity.com/paper/2506.17890/) (2025)
- [A Performance Evaluation of Nomon: A Flexible Interface for Noisy Single-Switch Users](https://mlsanity.com/paper/2204.01619/) (2022)
- [Adapting Large Language Models for Character-based Augmentative and Alternative Communication](https://mlsanity.com/paper/2501.10582/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Cross Community Agenda for Speech AI](https://mlsanity.com/paper/2609.13168/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) can enhance what people who use augmentative and alternative communication (AAC) are able to do with their systems. However, evaluating AI-powered AAC interfaces can be difficult. People are intersectional beings and current evaluation metrics can struggle to capture the multifaceted and nuanced desires people may have for their AAC. We explore the complicated nature of six AAC problem spaces, explore how AI might be used in these spaces, and suggest more robust methods of evaluation that take the intersectional nuances of people into account. We also discuss broader issues that arise across these problem spaces and how they could be addressed using our proposed evaluation methods.