The First Visual LLM
Built for Education
Built for Education
THE AI MODEL helps you
achieve Concept
Mastery faster
achieve Concept
Mastery faster
01VISUALIZATION다양한 시각 자료와 상호작용멀티 모달을 통해 활발한 상호작용이 가능하며, 텍스트를 넘어 3D 어셋, 애니메이션 등 다양한 시각 자료와 함께 설명에 생동감을 부여합니다.
02EXPLANATION일타 강사 선생님의 사고 유명 선생님들의 논리 구조, 설명 방식 뿐 아니라 비언어적인 표현까지 재현하여 직관적이고 몰입감있는 수업을 가능하게 합니다.
03PERSONALIZATION나보다 나를 더 잘 아는 선생님실시간으로 학습자의 상태를 확인하고 초개인화된 답변과 솔루션들을 제공합니다.
LLM
+
학생과 선생님의 머릿속 지식과 복잡한 체계가 온톨로지 위에 그려집니다.
CHALK AI는 세계 최고의 선생님들의 강의, 문제, 해설 데이터를 기반한 10만 건 이상의 온톨로지 데이터 기반으로 만들어졌습니다.
우리의 분석은 계속되고 온톨로지 체계는 매 순간 확장합니다.
HOW DOES IT
ALL WORK?
ALL WORK?
We develop task-specific AI Agents with educational
precision and real-time responsiveness, powered by a multi-agent system on an Ontology-based OS,
ensuring both educational sophistication and adaptive responses.
01STRUCTURE
Layer 01Data & Logic AcquisitionThis phase focuses on systematically acquiring essential educational data and formalizing the cognitive processes of domain experts.
Data
Collect educational data (e.g., lectures, quizzes) for content creation.Domain Logic
Capture expert decision-making to replicate in educational contexts.Layer 02OntologizationThis phase involves defining relationships among collected data and systematically connecting them to establish an interconnected data ecosystem.
Knowledge Graph
Organize concepts and relationships into a hierarchical structure.Purified Data
Convert raw data into a usable format with Knowledge Graph tags.Links
Connect data within the Data Hub for seamless interaction.Layer 03Agentic AI IntegrationThis phase focuses on designing and integrating AI agents capable of leveraging the ontology to deliver tailored solutions for diverse educational needs.
ConCreat Agent
AI that generates customized learning content.Tutoring Agent
Adaptive AI providing context-aware guidance.Learning Management Agent
AI that analyzes progress and offers recommendations.02CORE
Core 01 Cognitive Auto Extraction
Powered by CEE (Cognitive Extraction Engine)
01 Few-shot Extraction converts even small sets of lectures, problems, and solutions into structured knowledge.
02 Cognitive Layering breaks data into conceptual layers that reflect expert reasoning.
03 Ontology Expansion uses inference to continuously grow Ontologies with minimal data.
Core 02 ONTOLOGIZATION
Refining and connecting knowledge into a unified Brain Graph
01 Data Structuring organizing collected data into a Knowledge Graph with hierarchical relationships.
02 Algorithm Development converting raw inputs into clean, tagged Ontology-ready formats.
03 Model Development connecting concepts and datasets into an interconnected Brain Map.
Core 03 AGENTIC AI INTEGRATION
Bringing Ontology to life through intelligent AI agents
01 Analysis designing pipelines that replicate expert cognitive processes.
02 Pipeline Design building multimodal, context-aware agents (Tutoring Agent, Content Agent, Management Agent).
03 Ontology Binding ensuring agents operate seamlessly on top of Ontology for real-time, personalized guidance.
03APPLICATION
Application 01Visualizing Tutoring Chatbot Agent(US Patent Application No. 19/293,791)
Application 02ConCreat Agent in Content Creation System for
Teaching Materials Analysis(US Patent Application No. 19/295,168)
Application 03Learning Management Agent for AI Teacher Chatbot Operation(US Patent Application No. 19/295,205)