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

2026-06-23

AI Summary

Current evaluation metrics for augmentative and alternative communication (AAC) systems inadequately capture the diverse and intersecting identity-based needs of AAC users, thereby limiting the effective assessment of AI-enhanced AAC technologies. This study addresses this gap by systematically integrating an intersectional perspective into the design and evaluation of AI-driven AAC interfaces. Drawing on methods from human-computer interaction, AI system design, and qualitative social science research, the authors develop a context-sensitive evaluation framework grounded in real-world user experiences. The work not only exposes critical limitations in prevailing assessment practices but also proposes a novel paradigm that accounts for users’ multidimensional identities and needs. By doing so, it offers both theoretical grounding and actionable pathways for the future development and evaluation of AI-powered AAC systems.

Abstract

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.

Problem

• AI-powered AAC
• evaluation metrics
• intersectionality
• augmentative and alternative communication
• user-centered evaluation

Innovation

• AI-powered AAC
• intersectionality
• evaluation methods
• human-centered design
• assistive technology