Designing With Neurodiversity In Mind
Every individual processes information differently, and that variation is not a deficit. The term neurodiversity, coined in 1998 by Australian sociologist Judy Singer, frames these differences — in thinking, behaving, or learning — as natural human variation rather than something to correct. Singer, whose daughter is autistic, drew an analogy to biodiversity: just as ecosystems need diverse species to remain stable, cultures may need diverse minds.
“Why not propose that just as biodiversity is essential to ecosystem stability, so neurodiversity may be essential for cultural stability?”
— Judy Singer
Culture is abstract, tied to values, expectations, customs, and social norms. That abstraction makes acceptance harder. Humans instinctively classify things to simplify them, which leads to neurodivergence being framed as "different" and therefore difficult to normalize. Yet many groundbreaking figures were neurodiverse: Alan Turing, who cracked the Enigma code and built the first AI machine, was autistic; Steve Jobs had dyslexia; Emma Watson has ADHD.
As AI becomes embedded in technology, it offers a path toward more inclusive design — personalized tools, reminders, alerts, and adaptable language that can support neurodiverse users without singling them out. These improvements benefit everyone, since even neurotypical people learn in different ways: some kinesthetic, some auditory, some visual.
What Neurodivergence Covers
Neurodivergence is a non-medical umbrella term for brain function, behavior, and processing that differs from the norm. It includes several common conditions:
- Autism spectrum disorder (ASD): A neurological and developmental disorder affecting interaction, communication, learning, and behavior.
- Learning disabilities, including:
- Dyslexia: Difficulty reading;
- Dyscalculia: Difficulty with numbers;
- Dyspraxia: Difficulty with coordination;
- Dysgraphia: Difficulty with writing.
- Attention-Deficit/Hyperactivity Disorder (ADHD): A persistent pattern of inattention and/or hyperactivity-impulsivity that interferes with functioning or development.
AI makes it feasible to build affordable, adaptable, and supportive products that normalize neurodiversity rather than stigmatize it. The choice before innovators is whether to invest now in inclusive design or repeat the pattern of treating accessibility as an afterthought until a technology matures. Given the scale of the neurodiverse population, inclusivity should be a priority — driven by the same moral obligation Judy Singer acted on as a designer and parent.
Making AI Tools Work for Neurodivergent Users
AI systems operate on algorithmic logic built from vast datasets, using neural networks that loosely mirror human brain function. The technology itself is neutral; what needs to shift is how we apply it. No special-purpose AI is required to support neurodiversity — the capabilities already exist, and the real work lies in changing usage patterns.
Four areas deserve immediate attention if we want AI tools to serve autistic, ADHD, and learning-disabled users more effectively.
Workflow Improvements
For: Autistic and ADHD users
Focus: Working memory
The average human can hold only 3–5 ideas in working memory simultaneously. Beyond that, something gets lost unless it's documented. Mundane, repetitive tasks pile up in that limited space, and for neurodivergent employees — who may struggle with attention regulation or hyper-focus — the cognitive load becomes overwhelming.
AI-driven automation can offload routine work, reducing distractions and giving users clearer direction. The goal is not just efficiency but also memory support: tools that capture thoughts and moments for later recall.
A tool worth improving: Zoom's AI companion auto-generates meeting summaries, which helps participants avoid note-taking. But it only reacts to what's said in the meeting.
Opportunity: Zoom could let users create their own in-app notes during meetings and then merge those with AI-generated summaries. Users should also choose the summary format that works for them — short, simplified, or list-based. This would help autistic users who hyper-focus on content and ADHD users who need a place to park stray ideas without losing them. Larger companies often stick to incremental change; smaller, agile teams are where innovation is more likely to appear.
A neurodivergent-friendly alternative: Fireflies.ai already covers more ground. It auto-generates meeting notes, lets participants add their own notes that are appended to the summary, and offers both bulleted and paragraph formats. It can even include slide-deck transcriptions and audio snippets alongside the text, which provides better support for diverse processing styles.
Natural Language Processing
For: Autistic, learning-disabled, and ADHD users
Focus: Plain language and emotional assistance
Figurative language, metaphors, and jargon can be genuinely difficult to parse — not just for autistic users who take language literally, but also for users with learning disabilities and for ADHD users who lose interest in complex sentences. Complex language creates frustration, and frustration pushes users away from content they might otherwise engage with
AI can dissolve that barrier by converting dense, figurative text into straightforward language, giving users the confidence to tackle topics they would otherwise avoid.
A tool worth improving: Grammarly is strong on grammar correction, tone selection, and organization-specific style guides. But its recommendations are rule-bound, and the rules are preset by Grammarly or by an organization's style guide. Sentiment analysis is absent, so the tool can't tell when a negative message should stay negative.
Opportunity: Large language models could expand Grammarly's reach to include cultural and regional language nuances, going beyond fixed rules. Adding sentiment awareness would prevent inappropriate tone-flipping suggestions.
A neurodivergent-friendly alternative: Writer takes a different approach. Beyond grammar checking, it lets users rewrite sentences with options like simplify, polish, or shorten — and it tailors reconstructions based on content type, such as error messages or tooltips. That flexibility is more accommodating to varied language-processing needs.
Cognitive Assistance
For: Autistic, learning-disabled, and ADHD users
Focus: Suggestive technology
The Equality Act of 2010 set a legal foundation for workplace adjustments, and the same principle can apply to product design: tools should adapt to cognitive differences. Cognitive variation is universal. One study found that fewer than 10% of people score in the typical range on cognitive assessments. In that light, every AI assistive technology is essentially a cognitive supplement — and many users need more support than others to perform at the same level.
A tool worth improving: ClickUp, a project management platform, has extensive automation features and integrations, but the automation is limited to predetermined actions.
Opportunity: Users with ADHD can struggle to start or finish tasks without a nudge, while autistic users often prefer to see a task through in one session. An intelligent layer that plans each user's day around their preferred work style — recommending actions, building to-do lists, and offering organizational help — would do a lot more than fixed automation.
A neurodivergent-friendly alternative: Motion centers on AI-driven day planning. Users connect their calendars and the tool schedules meetings around heads-down time, suggesting optimal windows for focused work. Users can customize the whole schedule dynamically, and the AI proactively makes scheduling recommendations and helps plan around deadlines.
Adaptive Onboarding
For: Learning-disabled and ADHD users
Focus: Reducing frustration
Onboarding is key to product adoption — 80% of consumers want a personalized experience, and personalized onboarding is where that starts. Learning, however, doesn't stop at the first tutorial. Users will hit features they forgot how to use, tasks with multiple steps that got lost in their short-term memory, and poorly explained functions. For a user with a learning disability or ADHD, that experience is especially frustrating.
Adaptive onboarding makes re-learning possible, not just at signup but whenever users need help. AI-driven onboarding can respond to user behavior, offering guidance in the moment — in multiple formats, whether that's text, audio, or video.
A tool worth improving: Product Fruits offers onboarding customization and lets product teams control the walkthrough for new users. It tracks product usage across the onboarding experience. There is no real adaptivity once the user is beyond the initial setup.
Opportunity: AI-driven, persona-based nudging could help users who get stuck mid-task. If a user with ADHD is struggling to find a function, the tool could proactively surface a helpful prompt. For a user with a learning disability who is completing a complex multi-step task, the AI could offer simplified re-instructions at the point of need.
A neurodivergent-friendly alternative: Chameleon embeds AI directly into the onboarding experience. Users can request help at any time, from anywhere in the product, and the AI generates contextual answers on the spot — no sifting through help pages or asking colleagues required.
Why Intensity Matters
Every person experiences lapses in memory, confusion with complex language, or difficulty absorbing detailed information. The difference lies in the frequency and severity of those moments. For neurodiverse individuals, these are not occasional frustrations but persistent barriers. Neurotypical users can often adapt and move past such obstacles quickly, while neurodiverse users may not have that flexibility. This distinction is critical when designing tools: building for the more intense end of the spectrum solves problems that benefit everyone.
The same logic applies to AI. A model cannot reliably address a problem it has not encountered in sufficient variety. If training data lacks robust examples of neurodiverse challenges, the system will struggle to respond appropriately. This is where additional context becomes essential. Large Language Models like ChatGPT are accurate more often than not, but they can hallucinate. In scenarios where no other guidelines exist, that unreliability is a risk. Supplementing the model with clear company policies and relevant information improves the odds of producing correct, useful answers.
This dependency on AI should not be seen as a weakness. It is impractical to expect a human to be available with endless patience for every interaction. For neurodiverse professionals, having an AI that is direct and unemotional can be an advantage, particularly in workplace communication where ambiguity causes stress.
A Present-Day Necessity
Design workflows should actively remove friction for users who struggle with working memory, intricate details, or complex language. Cognitive assistance and adaptive technologies powered by AI are well suited to this task, but they require deliberate design attention. Neurodiversity cannot remain an afterthought in product development.
AI is already embedded in nearly every user interaction, whether visible through conversational interfaces or hidden inside recommendation algorithms. Accessibility solutions are improving, but the question remains whether those solutions account for neurodiverse needs specifically. As Jamie Dimon once said, “Problems don’t age well.” Waiting to address these issues means inheriting larger, more intractable ones later.
“Problems don’t age well.”
— Jamie Dimon
With an estimated 1.6 billion people affected by neurodiversity, inclusive design is not a future goal but a current requirement. The AI boom makes this the ideal moment to embed inclusivity at the foundation. Scaling inclusive practices now is far easier than retrofitting them once the technology becomes a sprawling part of daily life.
