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

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Blade Frisch, Will Wade, Dylan Gaines, Michelle Kinsella, Betts Peters, Tamara Broderick, Keith Vertanen
• Venue: arXiv: cs.HC
• Published: 2026-06-23
• Collected: 2026-06-24
• URL: http://arxiv.org/abs/2606.24854v1
• PDF: https://arxiv.org/pdf/2606.24854v1
• DOI: UNKNOWN

Plain-Language Brief

The research paper discusses the complexities in evaluating AI-powered augmentative and alternative communication (AAC) interfaces, as people's needs can be multifaceted and nuanced. The authors propose six AAC problem spaces, suggest ways AI could be used in these areas, and propose more inclusive evaluation methods.

What the Document Says

• AI can enhance augmentative and alternative communication (AAC).
• Evaluating AI-powered AAC interfaces can be challenging due to the complex and nuanced desires of people who use these systems.
• The paper identifies six AAC problem spaces.
• The authors discuss potential uses of AI in these problem spaces.
• They propose more robust evaluation methods that consider the intersectional nuances of users.
• The research also touches upon broader issues across these problem spaces and suggests how they could be addressed using the proposed evaluation methods.

Key Concepts Mentioned

• artificial intelligence
• augmentative and alternative communication (AAC)
• evaluation
• intersectional beings
• problem spaces

Claims Made by the Document

• AI can enhance AAC.
• Current evaluation methods struggle to capture the nuanced desires of users.
• The authors propose six problem spaces for AI-powered AAC.

Evidence Used

• The text mentions that AI can enhance what people who use AAC are able to do with their systems.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.