Large Adaptive Model （LAM）
A Learning System
Built Around Every
Student
Large Adaptive Model (LAM): One Connected System for Personalized Learning
LAM connects learning data, intelligent models, and the student experience in a continuous cycle. Each layer has a clear role, and every new interaction helps the system make the next learning decision with greater precision.
Data Layer
Curriculum content, questions, responses, learning history, and feedback give the system a detailed view of how learning is progressing.
Model Layer
Built on a multimodal education foundation model, this layer integrates knowledge graphs, knowledge tracing (KT) models, error-cause diagnosis and localization models, learner models, prediction and recommendation engines, and specialized AI agents to turn multimodal learning data into timely, personalized guidance.
Application Layer
Assessment, instruction, practice, testing, feedback, and supervision bring those decisions into the student’s daily learning experience.
Model Layer: Intelligence That Drives Every Decision
Nano-level Knowledge Components (NKC): What the Learner Needs
Breaks every subject into nano-level concepts and maps their relationships. When a student makes a mistake, the system pinpoints the exact knowledge gap and the prerequisite to revisit, so they only learn what’s truly missing.
Learner Model: What the Learner Knows
Continuously assesses mastery using answer patterns, speed, and learning history. Advanced diagnostics (Bayesian, deep learning) identify strengths and weaknesses with minimal testing, giving a clear, real-time picture of current proficiency.
Decision Engine: What the Learner Does Next
Predicts future performance and recommends the optimal next activity based on mastery, goals, and difficulty. The dynamic path adapts in real time, balancing review, new content, and backtracking to keep each learner progressing efficiently.
Application Layer: Intelligence in Action
Adaptive Instruction
Difficulty, question volume, and instructional strategy shift in real time based on performance, keeping each student challenged but not overwhelmed and always in their optimal growth zone.
Multimodal AI Agents
Students engage through text, visuals, interactive videos, exercises, and error-aware multimodal AI tutors. Specialized agents analyze problem text, diagrams, handwriting, solution steps, and dialogue context to identify where reasoning changed and provide targeted, step-level guidance.
Learning Loop
Goals, process, outcomes, and supervision are continuously tracked. Every session informs the next, closing the loop between assessment, instruction, practice, testing, and feedback for steady, measurable progress.
LEARNING FOR LIFE: Build the Skills to Learn with Confidence
MCM: Mode of Thinking, Capacity & Methodology
Squirrel Ai’s Mode of Thinking, Capacity &Methodology framework expands learning beyond subject knowledge. It develops Mode of Thinking, Capacity, and Methodology so students can understand how they approach a problem, strengthen core learning abilities, and use effective strategies across subjects.
Mode of Thinking: Apply concepts, recognize patterns, and approach problems from different perspectives.
Capacity: Strengthen memory, reasoning, analysis, and problem-solving.
Methodology: Build practical study methods that help students learn and apply knowledge effectively.
L5 Self-Directed Learning: The Student Takes the Wheel
With clear goals, adaptive guidance, and regular reflection, students can take greater ownership of their progress. Teachers, parents, and learning coaches remain connected through visible learning data and can step in when support is needed.
Proven Results
Independent studies and real-world pilots consistently show that Squirrel Ai delivers measurable academic gains, often outperforming traditional instruction.
Research-Led. Globally Recognized.
AI for Education, Built on Evidence
Squirrel Ai advances AI for Education through multimodal error analysis, agentic mathematical reasoning, safe Socratic dialogue, knowledge tagging, and cognitive-depth question generation, connecting peer-reviewed research with real-world deployment at scale.
- 150+ research papers published at NeurIPS, ICML, ICLR, ACL, CVPR, KDD, AAAI, AIED.
- TIMEIOO Most Influential Companies 2026 and TIME Best Inventions 2025.
- GUINNESS WORLD RECORDS™： Squirrel Ai defeated traditional teaching in a 1,662-student experiment, leading human tutors by up to 13.84 points (2025).
- UNESCO Al Innovation Award (2020).
- 2025 GSV 150 and QS Reimagine Education Silver Award (2024).
- MIT Technology Review 50 Smartest Companies (2019).
See Personalized Learning in Action
Discover how Squirrel Ai can help your child build stronger skills, greater confidence, and a learning path shaped around their needs.