Starts with a manipulable 3D subject instead of a static atlas page so learners can orient before memorizing.
AtlasFold
A 3D anatomy study workspace concept for students moving between spatial models, sectional slices, and active recall. AtlasFold is imagined as a front-end heavy learning environment that makes complex anatomy easier to manipulate, isolate, and revisit.
Pairs the 3D model with study chapters, labels, and sectional views to connect memorization with anatomy in space.
The ideal end state is a tool that lets users move from guided exploration into quiz and retrieval practice.
A study surface that treats anatomy like something to navigate, not just memorize.
AtlasFold is aimed at the moment where traditional diagrams stop being enough. It imagines a workspace where the learner can pivot between a 3D structure, labeled layers, and quick chapter-based study states without losing orientation.
- Side rail anchors the learner in chapter context and current layer selection
- Main stage keeps the 3D object central while still exposing labels and sectional hints
- Floating cards act like study prompts rather than passive decoration
- The visual language is designed to support teaching tools, self-study, and future quiz interactions
Visual Direction
The scene is designed to feel precise and quiet rather than game-like. Depth, orbit lines, and contextual cards help the model feel navigable, while the sidebar keeps the learner grounded in a structured lesson.
Rendering Pipeline
The 3D model uses a lightweight wireframe renderer on a 2D canvas with manual projection math (no Three.js dependency). Each anatomical layer is a separate vertex/edge set that can be toggled independently, giving the learner direct control over visual complexity. Depth sorting and opacity scaling create a sense of spatial structure without a full WebGL pipeline.
Study + Recall Loop
The workspace is structured around a three-phase loop: explore with labels visible, then hide labels and try to name structures from their spatial position, then enter quiz mode where the system highlights a region and asks for identification. This mirrors proven active-recall strategies from spaced-repetition research applied to spatial anatomy content.
Next Step
A stronger prototype would add real medical mesh data (e.g. skull or limb models), cross-section plane slicing, and study checkpoint scoring tied to anatomy selection. That would turn the concept from a polished mock interface into a genuinely interactive education demo.