Insight: Enhancing Mobile Accessibility for Blind and Visually Impaired Users with LLMs

Researchers introduce Insight, an Android accessibility service leveraging large language models to provide natural language interaction and real-time screen summarization for blind and visually impaired users. A comparative study shows Insight reduces mental effort and task completion time compared to TalkBack, though users identified a need for better interruption management.

Insight addresses a critical accessibility gap in mobile technology by replacing sequential gesture-based feedback with conversational AI interfaces. Current solutions like TalkBack require users to navigate screens through manual gestures and linear information delivery, creating cognitive overhead for blind and visually impaired (BVI) users. By incorporating LLMs, Insight enables natural language queries and contextual screen summarization, fundamentally changing how users interact with mobile devices.

This research emerges within a broader movement toward AI-driven accessibility solutions. As LLM technology matures, researchers increasingly recognize its potential to bridge gaps in digital inclusion. The study's within-subject experimental design provides empirical validation that LLM-based interfaces outperform traditional accessibility tools on measurable usability metrics, lending credibility to the approach beyond theoretical promise.

The implications extend across multiple stakeholder groups. For developers, this research suggests hybrid modal approaches—combining gesture and dialogue interfaces—could become industry standards. For accessibility advocates, it demonstrates that emerging AI technology can meaningfully improve quality of life for marginalized users. The reduction in mental effort and task time translates directly to reduced fatigue and improved user autonomy.

The identified limitation around interruption management presents an important design consideration moving forward. Users require mechanisms to control when and how the LLM provides information, preventing information overload. Future iterations should prioritize user agency over conversational informativeness. This work likely catalyzes further research into LLM-based accessibility solutions and may influence how major platforms—Google, Apple, Meta—prioritize accessibility feature development in coming years.

• →LLM-based interfaces reduce mental effort and task completion time compared to traditional gesture-based accessibility services.
• →Natural language dialogue modalities are preferred by blind and visually impaired users over sequential gesture navigation.
• →Interruption management emerged as a critical design requirement that current implementations lack.
• →Hybrid solutions combining gesture and dialogue modalities represent the most promising path toward inclusive mobile design.
• →Empirical validation of LLM accessibility tools provides evidence for broader adoption across mainstream platforms.