
HOW IT WORKS
Beyond Practice. Find the Missing Piece.
From root-cause intelligent diagnosis to personalized learning paths — every step is precisely guided by AI-powered data decisions.
A 5-Step Learning Cycle: AI-Guided Personalization and Human Support
Each learning cycle begins with a diagnostic assessment, adapts to student performance, and includes ongoing support from a Learning Supervisor.
01
Assessment
We do more than pinpoint current Knowledge Gaps — we trace back 3 to 5 years to uncover foundational vulnerabilities. Fine-grained knowledge breakdown ensures no weakness remains hidden.
02
Learning
Dynamic personalized pathways generated in real time. Skip what’s mastered, focus on what’s missing. The learning pace adaptively scales with student performance, challenging advanced learners while scaffolding those who need extra support.
03
Practice
Ability-matched exercises for learn-as-you-go practice with real-time feedback. Consecutive correct answers automatically accelerate progress, while repeated mistakes trigger targeted explanations and varied practice for reinforcement.
04
Reassessment
Measure progress through concrete data, not vague “feelings.” Every metric — mastery progress, accuracy trends, and focus areas — provides actionable insights for the next learning cycle.
05
Human in the loop
Supervisor Support
AI delivers precise instruction, while Supervisors provide human guidance, helping students stay focused, build confidence, and develop learning habits.
Loop back · every cycle informs the next
One Complete Learning Solution: AI-Powered Precision Teaching, Human-Guided Learning Support

Smart Learning Tablet
Precision Learning
Squirrel Ai Smart Learning Tablet is powered by the Adaptive Learning Engine and is responsible for determining “what to learn and how to learn.” The system identifies knowledge gaps through root-cause analysis, generates personalized learning paths, and delivers explanations and exercises matched to each student’s level.
Students no longer waste time reviewing concepts they have already mastered, nor are they pushed forward before building the necessary foundation. Learning paths adjust dynamically based on data, and every learning interaction informs the next decision.
One shared learning plan & data

AI-powered Self-study Center
Ensuring Learning Execution
The AI-powered self-study center provides structured learning schedules, a low-distraction environment, and continuous support from Supervisors. Supervisors do not replace AI instruction; instead, they follow the same learning plan to support execution: helping students stay focused, complete tasks at the right pace, receive timely intervention when facing difficulties, and rebuild confidence after setbacks.
AI solves “teaching with precision”; Supervisors ensure students can continue learning effectively.
The smart learning tablet and AI-powered self-study center work together through one shared learning plan and data system. AI identifies what students need to learn, while Supervisors ensure consistent progress and support. Parents no longer need to manage every step — the system keeps learning on track.
Learning Supervisors: Turning Learning Insights into Support

Every moment, the system generates learning data — including mastery changes, accuracy trends, and knowledge progress. But data alone cannot tell students what to do next. Supervisors turn insights into action and help learning become a lasting habit.
01
Data-Driven Decisions
- Identify the root cause of learning challenges: whether students need conceptual clarification, more practice, or foundational knowledge repair.
- Respond quickly to learning signals: declining accuracy, stalled progress, or repeated mistakes — data helps Supervisors adjust the learning pace.
- Translate data for parents: showing what students have learned, where they are struggling, and what comes next.
02
Human Support and Continuous Guidance
- Stay focused: Create a structured learning environment and help students complete current tasks.
- Maintain progress: Follow the personalized learning plan and ensure students move forward at the right pace.
- Overcome setbacks: AI can explain concepts again, but human support helps rebuild confidence.
- Recognize progress: Highlight meaningful improvements through learning data.
- Build independence: Gradually help students move from relying on reminders to managing their own learning.
AI delivers precision learning. Supervisors make learning truly sustainable.
Progress You Can See
Learning reports deliver far more than study hours and completed tasks, they track tangible movement across the knowledge graph. Clear visual indicators show which knowledge points have turned from red to green, which competency metrics are improving, and which skill gaps are being closed. By converting raw learning data into clear, actionable next steps, each report keeps parents and academic mentors aligned on every stage of student progress.

● → ● Learning objectives mastered
● Learning Progression ↑
● Current Progress
Our Learning Stories
Three learning journeys that show what root-cause diagnosis and personalized paths look like in practice.

Case 1 · Qing’s Learning Journey
Six Years of Continuous Support: From Leaving School to Wuhan University
Qing left school due to osteogenesis imperfecta and discovered Squirrel Ai through a CCTV program in 2019. Over the following six years, Squirrel Ai provided continuous adaptive learning support. AI precisely identified knowledge gaps, the AI Learning Device delivered a complete learning system, and root-cause analysis technology helped her build a structured knowledge foundation despite the absence of a traditional classroom environment. In 2025, she scored 621 on the Chinese National College Entrance Examination and was admitted to Wuhan University.
6
years
of continuous learning support
621
points
National College Entrance Examination
2025
Admitted to Wuhan University
Case 2 · Mayeli’s Learning Progress
Grade 4 Mathematics · 55 Learning Sessions
Across 55 learning sessions, Mayeli’s average Knowledge Mastery increased from 34.5% to 93.3%. Among 193 Learning Objectives Mastery Rate tracked by the system, she mastered 185 and advanced across five curriculum levels. This improvement was not from repetitive practice alone — it resulted from identifying root causes, providing personalized learning paths, and dynamically adjusting difficulty based on progress.
93.3%
up from 34.5%
Average Knowledge Mastery
96%
185 / 193
Learning Objectives Mastery Rate
81%
up from 34%
Average Mastery Per Session


Case 3 · Valeria’s Learning Progress
Grade 4 Mathematics · 20 Learning Sessions
Across 20 learning sessions, Valeria’s average Knowledge Mastery increased from 75.7% to 94.5%. Among 479 Learning Objectives tracked by the system, she mastered 471 and advanced across five curriculum levels. This efficient improvement came from continuously identifying what she had already mastered and what still required reinforcement.
94.5%
up from 75.7%
Average Knowledge Mastery
98%
471 / 479
Learning Objectives Mastery Rate
80%
up from 34%
Average Mastery Per Session
Start by Understanding Your Child’s Math Learning Needs
Surface-level mistakes are just symptoms; the root cause often traces back to a foundational concept from two or three years ago. Try a free learning assessment powered by root-cause diagnostics to identify the exact location of knowledge gaps and create a personalized learning pathway for your child.