Flow Learning System

A learning system built on motion graphics and TTS. It works for any subject: math, science, English, biology, programming, and more. Write a lesson as JSON data and each sentence becomes one scene. The result is a single-file HTML textbook in which graphs, equations, sentence parts, 3D illustrations, and code move on screen the moment the voice starts reading the sentence they belong to. An AI uses MCP tools to write the textbook, check it, run its example code to verify the output, render 3D illustrations with Blender, record the narration, and build it. The bundled TTS (Supertonic) currently voices Korean and English, and the sample courses are in Korean.

Core idea: one sentence = one scene

• Each say in a lesson is one scene. A scene bundles the script sentence, a large key phrase, and the visuals for that moment (graph, equation, sentence parts, image, code, variables…).

• Narration is synthesized sentence by sentence and concatenated, and the start time of each sentence is recorded in timing.json.

• The player reads the audio's current playback position every frame and picks the scene from it. Changing the playback speed or jumping to the middle never puts the picture and the voice out of sync.

Related MCP server: manim-mcp

Example courses

Adding 3D illustrations with Blender

The render_3d tool runs an AI-written Blender scene script headlessly and renders it with Cycles. When the script marks a 3D position with spot("name", (x, y, z)), the tool computes where that point lands in the perspective-projected image and saves it alongside the render. Lessons then refer to those names for labels, zoom centers, and arrows.

# Block on an incline — compute the endpoints of gravity and its two components as 3D vectors
th = math.radians(30)
d = V3((math.cos(th), 0, -math.sin(th)))   # down-slope direction
n = V3((math.sin(th), 0, math.cos(th)))    # normal to the incline
spot("block", C)
spot("g_tip", C + V3((0, 0, -2.6)))                 # gravity mg
spot("par_tip", C + d * 2.6 * math.sin(th))         # mg sinθ
spot("perp_tip", C - n * 2.6 * math.cos(th))        # mg cosθ

{"img": ["assets/incline.png", {
  "spots": {"theta": {"label": "θ = 30°"}},
  "arrows": [{"from": "block", "to": "g_tip", "label": "mg", "c": "bad"},
             {"from": "block", "to": "par_tip", "label": "mg sinθ", "c": "g1"}],
  "zoom": 1.35, "focus": "block"}]}

Because the arrow tips are points computed in 3D, they line up exactly with the rendered incline. Scene scripts are kept in assets/src/, so they can be re-rendered at any time.

Screen layout

The top bar holds only lesson navigation, play, scene navigation, and playback speed (0.75×–2×). On phones, the contents and script panels become drawers that open over the stage.

Motion rules

When the scene changes, on-screen elements are matched by name (key) and animated. No animation needs to be specified separately: changing the data is enough to produce motion.

Anatomy of a lesson

{
  "id": 1, "group": "수학", "title": "이차함수의 평행이동",
  "ops": [
    {"op": "part", "title": "x축 방향으로 평행이동"},
    {"op": "say", "head": "{m|y = (x - 2)^2} : 오른쪽으로 2",
     "text": "x 자리에 x 빼기 2를 넣으면 그래프 전체가 오른쪽으로 2만큼 미끄러집니다.",
     "board": {"seq": [
       {"plot": [{"curves": [{"fn": "(x-2)**2", "c": "g1", "key": "p"}],
                  "points": [{"x": 2, "y": 0, "label": "꼭짓점 (2, 0)", "key": "v"}]}]},
       {"math": [["y = x^2", "y = (x - 2)^2"], {"hl": [1]}]}]}}
  ]
}

The sample courses are narrated in Korean; {en|…} segments switch to an English voice.

The ops before a say build up the screen state, and the say captures that moment, together with its sentence, as a scene. The 22 ops, 24 widgets, and sentence markup ({m|equation}, {en|English}, [[written form|spoken form]]) are documented in the authoring guide (in Korean). The MCP authoring_guide tool returns the same content.

Workflow

sequenceDiagram
    participant U as User
    participant AI as AI (Claude, etc.)
    participant M as Flow Learning System MCP
    participant F as Course folder
    U->>AI: "Make a lesson on cell organelles"
    AI->>M: authoring_guide
    AI->>M: course_init
    AI->>M: render_3d(cell scene script)
    M->>F: assets/cell.png + hotspot coordinates
    AI->>M: lesson_save(lesson JSON)
    M-->>AI: Validation results (missing hotspots, hard-to-read words …)
    AI->>M: speech_preview → pronunciation_add
    AI->>M: code_verify (subjects with code)
    AI->>M: audio_synthesize
    M->>F: audio/l01.mp3 + timing.json
    AI->>M: course_build
    M->>F: dist/index.html
    U->>F: Open with fls serve

What the checks catch:

• Validation: unknown ops or widgets, hotspots that don't exist in the image, scenes that try to highlight lines not in the code, English words the TTS can't read

• Code verification: actually compiles and runs each example and compares it character by character with the on-screen console output (C#: Mono or .NET SDK; Python)

• Reading preview: 17개 → 열일곱 개 (native Korean counting), 19.6 → 십구 점 육 (Sino-Korean "nineteen point six"), 8분의 1 → 팔분의 일 ("one eighth"), {en|…} → English voice

Installation

git clone https://github.com/ghlee0786/flow-learning-system.git
cd flow-learning-system
pip install -e ".[tts]"

Registering the MCP server

There is no need to keep the MCP server running. Register it once with your client, and the AI launches it on its own whenever it uses a tool (stdio).

Claude Code

claude mcp add flow-learning-system -- python -m flow_learning_system.server

Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "flow-learning-system": {
      "command": "python",
      "args": ["-m", "flow_learning_system.server"],
      "env": { "FLS_BLENDER": "C:\\Program Files\\Blender Foundation\\Blender 4.2\\blender.exe" }
    }
  }
}

On Windows, if python points to a different environment, use the full path to python.exe in the virtual environment where you installed the package.

Tools (13)

Command line

fls validate examples/biology-cell              # validation
fls render   examples/biology-cell --name cell --script scene.py   # Blender render
fls speech   examples/english-passive --lesson 1   # TTS reading preview
fls verify   examples/csharp-grammar            # run and verify example code
fls audio    examples/math-quadratic            # speech synthesis
fls build    examples/math-quadratic            # dist/index.html
fls serve    examples/math-quadratic            # open locally (also from phones on the same Wi-Fi)
fls all      examples/physics-incline           # validate → verify → audio → build

A built textbook consists only of index.html plus the audio/ and assets/ folders, so it works without a server. It can be uploaded as-is to static hosting such as GitHub Pages. Audio files are not included in the repository. Generate them with fls audio <course>.

Folder structure

src/flow_learning_system/
  server.py      MCP server (13 tools)
  cli.py         fls command
  lesson.py      Lesson ops interpretation · validation (same rules as the player)
  speech.py      Script → TTS sentences (code-term readings, Korean number reading, English segments)
  tts.py         Supertonic synthesis · per-sentence timing · cache
  render.py      Blender rendering · hotspot projection
  verify.py      Example code execution (C#: Mono/.NET, Python)
  build.py       Single-file HTML build
  guide.md       Lesson authoring guide
  player/        Player (core.js equations/graphics, lesson.js scene builder/widgets, interp.js JSON interpretation, app.js playback/sync/motion)
examples/                  Math · science · English · biology · C# example courses (3D scene scripts in assets/src/)
docs/                      Authoring guide, README figures
tools/                     README figure generators, legacy JS lesson converter
tests/                     pytest

License

MIT. The Supertonic Python package used for speech synthesis is MIT-licensed and its model is under the OpenRAIL license; neither is included in this repository (the model is downloaded on first run).