Definition Statement
Individuals with ADHD and executive function challenges struggle to bridge the gap between having goals and actually executing on them. Traditional task management tools assume a level of natural task decomposition, prioritisation, and sustained motivation that these users lack. This is all because of the neurology and brain makeup. There is no mainstream tool designed around their cognitive patterns that helps them break ambiguous goals into concrete steps, maintain momentum through small wins, and build a deliberate path from intention to completion.
The app: A mobile-first tool that helps individuals with ADHD turn overwhelming goals into structured, bite-sized tasks, providing guided decomposition, deliberate sequencing, and a positive reward loop built around small wins.
Who This Is For
The target user is a high school student juggling ambitious personal projects (like building an app) and daily academic demands (finishing homework across multiple classes). This leaves the student feeling paralysed, unable to work towards them at all. Tasks feel convoluted before they even begin, and the inertia of starting is the biggest wall. The existing tools weren’t built for how their brain works.
Ideas I Explored
Before landing on the prototype, I considered several directions, some of which include: automatically breaking tasks into subtasks, dynamic priority sorting, an AI chat interface to talk through blockers, a single-focus view that hides everything else, a milestone tracker separate from daily tasks, batch input for grouping similar work, and a gamified reward system. Each addressed part of the problem. The prototype combines these into one cohesive project.
Prototype
A functional mobile app (likely built in React Native or Swift) demonstrating the core task input and focus flow.
Users input tasks in two modes: Batches (grouped work like homework) and Milestones (bigger goals like “finish college essay”). Only one task is shown at a time in a focus view. From there, the user can break it down further or chat with an AI assistant to figure out the next concrete step.
Tools needed: an AI API (Anthropic, GLM, or OpenAI) and 3 to 5 student testers.
Build Plan
Phase 1 – Learn and Set Up (now to March 1):
- Get comfortable with React Native basics via Expo. Set up the repo and a working task input screen.
- Test: Can you add a task and see it in a list?
Phase 2 – Core Task Flow (March 1 to March 21):
- Build batch and milestone input screens. Build the focus view that surfaces only the top priority task.
- Test: input 5 tasks across 2 batches and confirm the right one surfaces.
Phase 3 – Task breakdowns and AI (March 21 to April 11):
- Build the breakdown screen. Integrate AI to suggest concrete next steps from a task title.
- Test: does “finish history essay” return something actually useful?
Phase 4 – Reward Loop and Polish (April 11 to April 25):
- Add satisfying task completion animations and auto-surface the next task. Clean up the UI.
- Test: run a full session end-to-end and time it.
Phase 5 – User Testing (April 25 to May 1):
- Run 2 to 3 sessions with real students. Watch without explaining. Fix the top friction points. Prep demo materials.
- Test: Was the user satisfied? Does the app work for those without previous knowledge?
Key Takeaways
The whole design philosophy is hiding complexity until the user needs it. AI earns its place only if its decomposition is genuinely better than what the user would write themselves. Stay tight on scope.
Pros: Fills a real gap; technically achievable; testable quickly with real users.
Cons: AI quality can be unpredictable and prompt-dependent; React Native can be annoying to work with; recruiting quality testers takes coordination.
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