The problem
What needs solving
AI systems are increasingly used to support planning, decision-making, learning and everyday tasks, yet their interfaces and evaluation methods often assume relatively consistent cognitive and communication abilities. For neurodivergent users, including people with ADHD and autism, this may introduce additional barriers such as information overload, unclear instructions, rigid workflows and increased executive-function demands. The challenge is to establish how AI can adapt to different cognitive needs without requiring excessive user configuration, reinforcing dependency or reducing meaningful human control.
Why it matters
AI has significant potential to remove barriers for people who experience executive dysfunction, cognitive overload and difficulties navigating conventional systems. However, accessibility should not depend on users continually adapting themselves to technology. If AI systems are developed without accounting for cognitive diversity, they risk reproducing existing inequalities and excluding people who could benefit most. Designing for cognitive diversity could improve usability, independence and accessibility for a much wider population.
What counts as progress
Progress would include developing and validating cognitive accessibility benchmarks with neurodivergent participants, and comparing adaptive AI interfaces against conventional interfaces in controlled, real-world tasks. Outcomes should measure cognitive load, task completion, error rates, time required, user satisfaction and perceived autonomy. Further progress would demonstrate sustained improvements across different cognitive profiles without increasing unwanted dependency, privacy risks or loss of user control. Findings should be independently reproducible and inform practical accessibility standards for AI developers.
Stater & version history
Stated by Charlie Ruttle
10 Nov 2026 — Proposed by Charlie Ruttle and numbered by the ideas council.