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  • Personal study
  • AI mobile concept

Recallry

A study companion that turns personal notes into cited explanations, practice questions and revision maps.

Recallry product concept in a branded setting

Designed around4 AI workflows

Project overview

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.

The challenge

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.

  • Keep study capture and ask my notes connected within a short mobile flow.
  • Make handwriting extraction correctable when the model misreads the input.
  • Explain adaptive practice through visible references or user-selected preferences.
  • Keep changes, sharing and data retention under explicit user control.

Our solution

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.

  • Turn photographed notes into editable text with uncertain words marked.
  • Answer questions using selected notes and linked excerpts.
  • Draft exercises based on topics the learner chooses to revisit.
  • Group incorrect practice attempts into editable revision themes.

Key features

  • Study capture

    Review a scanned handwritten study page with OCR text corrections and topic tags.

  • Ask my notes

    Review a cited AI explanation with highlighted source excerpts and a Follow-up question field.

  • Practice round

    Review a multiple-choice question with an expandable reasoning hint and Check answer button.

  • Revision map

    Review a topic map with review dates, saved mistakes and downloadable study sets.

  • Handwriting extraction

    Turn photographed notes into editable text with uncertain words marked.

  • Source-grounded tutoring

    Answer questions using selected notes and linked excerpts.

  • Adaptive practice

    Draft exercises based on topics the learner chooses to revisit.

  • Misconception review

    Group incorrect practice attempts into editable revision themes.

  • Source notebooks

    Group uploaded material by course and topic.

  • Correction tools

    Fix extracted text before using it in practice.

  • Mistake journal

    Save selected questions for later review.

  • Offline study sets

    Download approved notes and review cards.

Proposed technology stack

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.

  • React Native
  • TypeScript
  • SQLite
  • AI retrieval

The results

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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