How AI tools can redefine universal design to increase accessibility

Google Research proposes Natively Adaptive Interfaces (NAI), a framework that uses multimodal and agentic AI to make interfaces adapt to individual users rather than forcing everyone into a fixed design. Developed through co-design with disability communities, NAI aims to reduce the accessibility gap by embedding assistive capabilities directly into products. Early prototypes suggest that personalized, context-aware interfaces can improve experiences for disabled users while also benefiting the broader population.

Community-led co-design

• Google follows the principle “Nothing About Us, Without Us,” involving people with disabilities as co-designers from the beginning.
• Partnerships include RIT/NTID, The Arc of the United States, RNID, and Team Gleason.
• These collaborations focus on real-world barriers and recognize the expertise of disability communities.
• The approach also aims to create employment and economic opportunities for people who help shape the technology.

Moving from reactive accessibility to adaptive interfaces

• Google identifies an “accessibility gap” between the release of new features and the development of compatible assistive tools.
• NAI addresses this by making accessibility native to the interface instead of adding it afterward.
• Static navigation is replaced with dynamic, agent-driven modules that can interpret context and adjust the experience.

Multi-system agents

• An Orchestrator maintains shared context and delegates tasks to specialized sub-agents.
• A Summarization Agent breaks down complex documents and assigns subtasks to expert agents.
• A Settings Agent dynamically adjusts interface elements such as text size.
• This structure lets users accomplish tasks without navigating complicated menus or searching for the right control.

Multimodal interaction

• Gemini-based prototypes combine voice, vision, and text rather than limiting accessibility to text-to-speech.
• Live video can be converted into interactive audio descriptions.
• Users can ask follow-up questions about specific visual details as events unfold.
• Conversational interaction provides situational awareness and may reduce cognitive load.

Proven prototypes

• StreetReaderAI

  • Supports blind and low-vision users navigating physical spaces.
  • Combines an AI Describer that analyzes visual and geographic information with an AI Chat system for questions.
  • Maintains context so users can ask about previously encountered locations, such as the position of a bus stop.

• Multimodal Agent Video Player (MAVP)

  • Makes audio description interactive rather than static.
  • Users can change the level of detail or ask questions during playback.
  • Uses an offline “dense index” of visual descriptions and retrieval-augmented generation (RAG) for fast responses.

• Grammar Laboratory

  • Developed by RIT/NTID with Google.org support for American Sign Language and English learners.
  • Provides grammar instruction through ASL videos, English captions, spoken narration, and written transcripts.
  • Uses adaptive AI to customize lessons according to each student’s language preferences and interactions.

The curb-cut effect

• Accessibility features designed for people with significant constraints can benefit many other users.
• Voice interfaces created for blind users may help sighted people who are multitasking.
• AI synthesis and learning tools designed for people with learning disabilities can also support users who want information presented more clearly or flexibly.
• NAI therefore treats accessibility as a source of better universal design, not as a specialized add-on.

NAI’s central recommendation is to build accessibility into interfaces from the start, using multimodal AI, persistent context, and community-led design. The most effective systems will adapt to users while remaining accountable to the people whose needs they are intended to serve.