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Spatial Scholar - AI @ Meta Hackathon

We were among the 50 students across the nation to participate in the AI @ Meta Hackathon at Meta's global HQ in Menlo Park, CA, where we explored how VR, AI, and mixed reality can make life easier and more meaningful.

Our Idea

Spatial Scholar is a mixed reality learning companion built for the Meta Quest 3. It turns a spoken learning request into an interactive 3D concept map anchored in your real space, with an AI-guided explanation.

Demo

Watch the demo

What it does

  • Voice → lesson: you ask a question out loud (e.g., “Teach me BFS vs DFS”)
  • Concept breakdown: the system generates a short explanation + a structured concept graph
  • 3D visualization: nodes/edges render as a spatial graph in passthrough
  • Speech + text: the explanation is spoken and saved for reuse

Why we built it

Learning content still lives on flat screens (docs, videos, chat windows). We wanted a way to:

  • visualize abstract topics in 3D
  • show relationships/prereqs spatially
  • make explanations feel more “hands-on” and memorable

How it works (high level)

  1. Speech input in Unity
  2. A structured prompt is sent to Llama 4
  3. The model returns:
    • a short explanation
    • a JSON concept graph (nodes + relationships)
  4. Unity parses the JSON and renders a dynamic 3D graph in passthrough
  5. TTS reads the explanation out loud

Tech stack

  • Meta Quest 3
  • Unity (C#)
  • Meta XR SDK (passthrough / spatial visualization)
  • Llama 4 (reasoning + JSON graph generation)
  • Hugging Face + ElevenLabs (model + speech integration)
  • Custom agents:
    • LlmAgent
    • SpeechToTextAgent
    • TextToSpeechAgent
  • JSON graph parser + runtime graph layout/renderer

Current MVP

  • Voice-based requests
  • LLM explanation output (speech + text)
  • JSON concept graph generation
  • 3D nodes + edges in passthrough
  • Expandable/interactable graph layout

Next steps

  • Hand-based node expansion and richer visual subtopics
  • Persistent “knowledge rooms”
  • Multi-user sessions
  • Light assessments (quick checks / recall prompts)
  • Optional integrations (Canvas / Google Classroom)

Team

Built by Ansh, Riten, Vinay, Ashlie, and Nathan

About

Invitation Only AI @ Meta Hackathon

Resources

Readme
Activity

Stars

2 stars

Watchers

0 watching

Forks

1 fork
Report repository

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Spatial Scholar - AI @ Meta Hackathon
We were among the 50 students across the nation to participate in the AI @ Meta Hackathon at Meta's global HQ in Menlo Park, CA, where we explored how VR, AI, and mixed reality can make life easier and more meaningful.
Our Idea
Spatial Scholar is a mixed reality learning companion built for the Meta Quest 3. It turns a spoken learning request into an interactive 3D concept map anchored in your real space, with an AI-guided explanation.
Demo
Watch the demo
What it does
  • Voice → lesson: you ask a question out loud (e.g., “Teach me BFS vs DFS”)
  • Concept breakdown: the system generates a short explanation + a structured concept graph
  • 3D visualization: nodes/edges render as a spatial graph in passthrough
  • Speech + text: the explanation is spoken and saved for reuse
Why we built it
Learning content still lives on flat screens (docs, videos, chat windows). We wanted a way to:
  • visualize abstract topics in 3D
  • show relationships/prereqs spatially
  • make explanations feel more “hands-on” and memorable
How it works (high level)
  1. Speech input in Unity
  2. A structured prompt is sent to Llama 4
  3. The model returns:

    • a short explanation
    • a JSON concept graph (nodes + relationships)
  4. Unity parses the JSON and renders a dynamic 3D graph in passthrough
  5. TTS reads the explanation out loud
Tech stack
  • Meta Quest 3
  • Unity (C#)
  • Meta XR SDK (passthrough / spatial visualization)
  • Llama 4 (reasoning + JSON graph generation)
  • Hugging Face + ElevenLabs (model + speech integration)
  • Custom agents:

    • LlmAgent
    • SpeechToTextAgent
    • TextToSpeechAgent
  • JSON graph parser + runtime graph layout/renderer
Current MVP
  • Voice-based requests
  • LLM explanation output (speech + text)
  • JSON concept graph generation
  • 3D nodes + edges in passthrough
  • Expandable/interactable graph layout
Next steps
  • Hand-based node expansion and richer visual subtopics
  • Persistent “knowledge rooms”
  • Multi-user sessions
  • Light assessments (quick checks / recall prompts)
  • Optional integrations (Canvas / Google Classroom)
Team
Built by Ansh, Riten, Vinay, Ashlie, and Nathan
About
Invitation Only AI @ Meta Hackathon
Resources
Readme
Activity
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
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Spatial Scholar - AI @ Meta Hackathon
We were among the 50 students across the nation to participate in the AI @ Meta Hackathon at Meta's global HQ in Menlo Park, CA, where we explored how VR, AI, and mixed reality can make life easier and more meaningful.
Our Idea
Spatial Scholar is a mixed reality learning companion built for the Meta Quest 3. It turns a spoken learning request into an interactive 3D concept map anchored in your real space, with an AI-guided explanation.
Demo
Watch the demo
What it does
  • Voice → lesson: you ask a question out loud (e.g., “Teach me BFS vs DFS”)
  • Concept breakdown: the system generates a short explanation + a structured concept graph
  • 3D visualization: nodes/edges render as a spatial graph in passthrough
  • Speech + text: the explanation is spoken and saved for reuse
Why we built it
Learning content still lives on flat screens (docs, videos, chat windows). We wanted a way to:
  • visualize abstract topics in 3D
  • show relationships/prereqs spatially
  • make explanations feel more “hands-on” and memorable
How it works (high level)
  1. Speech input in Unity
  2. A structured prompt is sent to Llama 4
  3. The model returns:

    • a short explanation
    • a JSON concept graph (nodes + relationships)
  4. Unity parses the JSON and renders a dynamic 3D graph in passthrough
  5. TTS reads the explanation out loud
Tech stack
  • Meta Quest 3
  • Unity (C#)
  • Meta XR SDK (passthrough / spatial visualization)
  • Llama 4 (reasoning + JSON graph generation)
  • Hugging Face + ElevenLabs (model + speech integration)
  • Custom agents:

    • LlmAgent
    • SpeechToTextAgent
    • TextToSpeechAgent
  • JSON graph parser + runtime graph layout/renderer
Current MVP
  • Voice-based requests
  • LLM explanation output (speech + text)
  • JSON concept graph generation
  • 3D nodes + edges in passthrough
  • Expandable/interactable graph layout
Next steps
  • Hand-based node expansion and richer visual subtopics
  • Persistent “knowledge rooms”
  • Multi-user sessions
  • Light assessments (quick checks / recall prompts)
  • Optional integrations (Canvas / Google Classroom)
Team
Built by Ansh, Riten, Vinay, Ashlie, and Nathan
About
Invitation Only AI @ Meta Hackathon
Resources
Readme
Activity
Stars
2 stars
Watchers
0 watching
Forks
1 fork
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try {
var __m = "*";
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Spatial Scholar - AI @ Meta Hackathon
We were among the 50 students across the nation to participate in the AI @ Meta Hackathon at Meta's global HQ in Menlo Park, CA, where we explored how VR, AI, and mixed reality can make life easier and more meaningful.
Our Idea
Spatial Scholar is a mixed reality learning companion built for the Meta Quest 3. It turns a spoken learning request into an interactive 3D concept map anchored in your real space, with an AI-guided explanation.
Demo
Watch the demo
What it does
  • Voice → lesson: you ask a question out loud (e.g., “Teach me BFS vs DFS”)
  • Concept breakdown: the system generates a short explanation + a structured concept graph
  • 3D visualization: nodes/edges render as a spatial graph in passthrough
  • Speech + text: the explanation is spoken and saved for reuse
Why we built it
Learning content still lives on flat screens (docs, videos, chat windows). We wanted a way to:
  • visualize abstract topics in 3D
  • show relationships/prereqs spatially
  • make explanations feel more “hands-on” and memorable
How it works (high level)
  1. Speech input in Unity
  2. A structured prompt is sent to Llama 4
  3. The model returns:

    • a short explanation
    • a JSON concept graph (nodes + relationships)
  4. Unity parses the JSON and renders a dynamic 3D graph in passthrough
  5. TTS reads the explanation out loud
Tech stack
  • Meta Quest 3
  • Unity (C#)
  • Meta XR SDK (passthrough / spatial visualization)
  • Llama 4 (reasoning + JSON graph generation)
  • Hugging Face + ElevenLabs (model + speech integration)
  • Custom agents:

    • LlmAgent
    • SpeechToTextAgent
    • TextToSpeechAgent
  • JSON graph parser + runtime graph layout/renderer
Current MVP
  • Voice-based requests
  • LLM explanation output (speech + text)
  • JSON concept graph generation
  • 3D nodes + edges in passthrough
  • Expandable/interactable graph layout
Next steps
  • Hand-based node expansion and richer visual subtopics
  • Persistent “knowledge rooms”
  • Multi-user sessions
  • Light assessments (quick checks / recall prompts)
  • Optional integrations (Canvas / Google Classroom)
Team
Built by Ansh, Riten, Vinay, Ashlie, and Nathan
About
Invitation Only AI @ Meta Hackathon
Resources
Readme
Activity
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
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Languages

, 'i');
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Spatial Scholar - AI @ Meta Hackathon
We were among the 50 students across the nation to participate in the AI @ Meta Hackathon at Meta's global HQ in Menlo Park, CA, where we explored how VR, AI, and mixed reality can make life easier and more meaningful.
Our Idea
Spatial Scholar is a mixed reality learning companion built for the Meta Quest 3. It turns a spoken learning request into an interactive 3D concept map anchored in your real space, with an AI-guided explanation.
Demo
Watch the demo
What it does
  • Voice → lesson: you ask a question out loud (e.g., “Teach me BFS vs DFS”)
  • Concept breakdown: the system generates a short explanation + a structured concept graph
  • 3D visualization: nodes/edges render as a spatial graph in passthrough
  • Speech + text: the explanation is spoken and saved for reuse
Why we built it
Learning content still lives on flat screens (docs, videos, chat windows). We wanted a way to:
  • visualize abstract topics in 3D
  • show relationships/prereqs spatially
  • make explanations feel more “hands-on” and memorable
How it works (high level)
  1. Speech input in Unity
  2. A structured prompt is sent to Llama 4
  3. The model returns:

    • a short explanation
    • a JSON concept graph (nodes + relationships)
  4. Unity parses the JSON and renders a dynamic 3D graph in passthrough
  5. TTS reads the explanation out loud
Tech stack
  • Meta Quest 3
  • Unity (C#)
  • Meta XR SDK (passthrough / spatial visualization)
  • Llama 4 (reasoning + JSON graph generation)
  • Hugging Face + ElevenLabs (model + speech integration)
  • Custom agents:

    • LlmAgent
    • SpeechToTextAgent
    • TextToSpeechAgent
  • JSON graph parser + runtime graph layout/renderer
Current MVP
  • Voice-based requests
  • LLM explanation output (speech + text)
  • JSON concept graph generation
  • 3D nodes + edges in passthrough
  • Expandable/interactable graph layout
Next steps
  • Hand-based node expansion and richer visual subtopics
  • Persistent “knowledge rooms”
  • Multi-user sessions
  • Light assessments (quick checks / recall prompts)
  • Optional integrations (Canvas / Google Classroom)
Team
Built by Ansh, Riten, Vinay, Ashlie, and Nathan
About
Invitation Only AI @ Meta Hackathon
Resources
Readme
Activity
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Spatial Scholar - AI @ Meta Hackathon
We were among the 50 students across the nation to participate in the AI @ Meta Hackathon at Meta's global HQ in Menlo Park, CA, where we explored how VR, AI, and mixed reality can make life easier and more meaningful.
Our Idea
Spatial Scholar is a mixed reality learning companion built for the Meta Quest 3. It turns a spoken learning request into an interactive 3D concept map anchored in your real space, with an AI-guided explanation.
Demo
Watch the demo
What it does
  • Voice → lesson: you ask a question out loud (e.g., “Teach me BFS vs DFS”)
  • Concept breakdown: the system generates a short explanation + a structured concept graph
  • 3D visualization: nodes/edges render as a spatial graph in passthrough
  • Speech + text: the explanation is spoken and saved for reuse
Why we built it
Learning content still lives on flat screens (docs, videos, chat windows). We wanted a way to:
  • visualize abstract topics in 3D
  • show relationships/prereqs spatially
  • make explanations feel more “hands-on” and memorable
How it works (high level)
  1. Speech input in Unity
  2. A structured prompt is sent to Llama 4
  3. The model returns:

    • a short explanation
    • a JSON concept graph (nodes + relationships)
  4. Unity parses the JSON and renders a dynamic 3D graph in passthrough
  5. TTS reads the explanation out loud
Tech stack
  • Meta Quest 3
  • Unity (C#)
  • Meta XR SDK (passthrough / spatial visualization)
  • Llama 4 (reasoning + JSON graph generation)
  • Hugging Face + ElevenLabs (model + speech integration)
  • Custom agents:

    • LlmAgent
    • SpeechToTextAgent
    • TextToSpeechAgent
  • JSON graph parser + runtime graph layout/renderer
Current MVP
  • Voice-based requests
  • LLM explanation output (speech + text)
  • JSON concept graph generation
  • 3D nodes + edges in passthrough
  • Expandable/interactable graph layout
Next steps
  • Hand-based node expansion and richer visual subtopics
  • Persistent “knowledge rooms”
  • Multi-user sessions
  • Light assessments (quick checks / recall prompts)
  • Optional integrations (Canvas / Google Classroom)
Team
Built by Ansh, Riten, Vinay, Ashlie, and Nathan
About
Invitation Only AI @ Meta Hackathon
Resources
Readme
Activity
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

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})();
(function(){
try {
var __m = "*";
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Spatial Scholar - AI @ Meta Hackathon
We were among the 50 students across the nation to participate in the AI @ Meta Hackathon at Meta's global HQ in Menlo Park, CA, where we explored how VR, AI, and mixed reality can make life easier and more meaningful.
Our Idea
Spatial Scholar is a mixed reality learning companion built for the Meta Quest 3. It turns a spoken learning request into an interactive 3D concept map anchored in your real space, with an AI-guided explanation.
Demo
Watch the demo
What it does
  • Voice → lesson: you ask a question out loud (e.g., “Teach me BFS vs DFS”)
  • Concept breakdown: the system generates a short explanation + a structured concept graph
  • 3D visualization: nodes/edges render as a spatial graph in passthrough
  • Speech + text: the explanation is spoken and saved for reuse
Why we built it
Learning content still lives on flat screens (docs, videos, chat windows). We wanted a way to:
  • visualize abstract topics in 3D
  • show relationships/prereqs spatially
  • make explanations feel more “hands-on” and memorable
How it works (high level)
  1. Speech input in Unity
  2. A structured prompt is sent to Llama 4
  3. The model returns:

    • a short explanation
    • a JSON concept graph (nodes + relationships)
  4. Unity parses the JSON and renders a dynamic 3D graph in passthrough
  5. TTS reads the explanation out loud
Tech stack
  • Meta Quest 3
  • Unity (C#)
  • Meta XR SDK (passthrough / spatial visualization)
  • Llama 4 (reasoning + JSON graph generation)
  • Hugging Face + ElevenLabs (model + speech integration)
  • Custom agents:

    • LlmAgent
    • SpeechToTextAgent
    • TextToSpeechAgent
  • JSON graph parser + runtime graph layout/renderer
Current MVP
  • Voice-based requests
  • LLM explanation output (speech + text)
  • JSON concept graph generation
  • 3D nodes + edges in passthrough
  • Expandable/interactable graph layout
Next steps
  • Hand-based node expansion and richer visual subtopics
  • Persistent “knowledge rooms”
  • Multi-user sessions
  • Light assessments (quick checks / recall prompts)
  • Optional integrations (Canvas / Google Classroom)
Team
Built by Ansh, Riten, Vinay, Ashlie, and Nathan
About
Invitation Only AI @ Meta Hackathon
Resources
Readme
Activity
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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Spatial Scholar - AI @ Meta Hackathon
We were among the 50 students across the nation to participate in the AI @ Meta Hackathon at Meta's global HQ in Menlo Park, CA, where we explored how VR, AI, and mixed reality can make life easier and more meaningful.
Our Idea
Spatial Scholar is a mixed reality learning companion built for the Meta Quest 3. It turns a spoken learning request into an interactive 3D concept map anchored in your real space, with an AI-guided explanation.
Demo
Watch the demo
What it does
  • Voice → lesson: you ask a question out loud (e.g., “Teach me BFS vs DFS”)
  • Concept breakdown: the system generates a short explanation + a structured concept graph
  • 3D visualization: nodes/edges render as a spatial graph in passthrough
  • Speech + text: the explanation is spoken and saved for reuse
Why we built it
Learning content still lives on flat screens (docs, videos, chat windows). We wanted a way to:
  • visualize abstract topics in 3D
  • show relationships/prereqs spatially
  • make explanations feel more “hands-on” and memorable
How it works (high level)
  1. Speech input in Unity
  2. A structured prompt is sent to Llama 4
  3. The model returns:

    • a short explanation
    • a JSON concept graph (nodes + relationships)
  4. Unity parses the JSON and renders a dynamic 3D graph in passthrough
  5. TTS reads the explanation out loud
Tech stack
  • Meta Quest 3
  • Unity (C#)
  • Meta XR SDK (passthrough / spatial visualization)
  • Llama 4 (reasoning + JSON graph generation)
  • Hugging Face + ElevenLabs (model + speech integration)
  • Custom agents:

    • LlmAgent
    • SpeechToTextAgent
    • TextToSpeechAgent
  • JSON graph parser + runtime graph layout/renderer
Current MVP
  • Voice-based requests
  • LLM explanation output (speech + text)
  • JSON concept graph generation
  • 3D nodes + edges in passthrough
  • Expandable/interactable graph layout
Next steps
  • Hand-based node expansion and richer visual subtopics
  • Persistent “knowledge rooms”
  • Multi-user sessions
  • Light assessments (quick checks / recall prompts)
  • Optional integrations (Canvas / Google Classroom)
Team
Built by Ansh, Riten, Vinay, Ashlie, and Nathan
About
Invitation Only AI @ Meta Hackathon
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