Study capture
Review a scanned handwritten study page with OCR text corrections and topic tags.
A study companion that turns personal notes into cited explanations, practice questions and revision maps.
Recallry explores personal study through a mobile experience built around a document-led study screen with a compact bottom learning dock. A study companion that turns personal notes into cited explanations, practice questions and revision maps. The proposed journey connects study capture, ask my notes, practice round, revision map. Four original phone showcases establish the visual direction, while the case study defines the supporting feature set and a proposed React Native architecture.
Students need generated practice to stay connected to their actual source material and reveal mistakes without hiding the reasoning. On a phone, the person also needs to capture information quickly, understand where an answer came from and return to the original material without navigating through a dense desktop interface.
The design begins with a document-led study screen with a compact bottom learning dock, using large touch targets, short task sequences and contextual sheets. Turn photographed notes into editable text with uncertain words marked. Answer questions using selected notes and linked excerpts. The companion views keep drafts, original material and accepted decisions distinct. A navy, lilac and lemon visual system gives this app its own character within the collection.
Inside the platform
Original mobile interface concepts with fictional content. AI responses illustrate proposed interactions; they are not live model results.
Proposed iOS & Android architecture using React Native and TypeScript. AI processing, storage and synchronization are design proposals for this concept, not integrations implemented by this portfolio.
The concept defines four mobile views, four AI workflows and twelve feature areas. The next validation would ask a user to complete study capture, inspect the proposed ask my notes output and correct an intentional extraction or suggestion error. Evaluation would focus on source traceability, correction effort and task completion. No usability study or business-impact measurement is claimed.
4Original mobile showcase views
4Proposed AI workflows
12Designed feature areas
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