AI-guided math training withdiagnosis, feedback, and adaptation.
MathMagicAI reads every answer step, classifies error patterns, maps prerequisite gaps, and chooses the next question to train the weakest concept.
Signal A
Accuracy by topic and sub-skill
Signal B
Response latency and confidence drift
Signal C
Misconception type and severity level
Current training target
Quadratic factorization and root extraction
Prompt: Solve x² - 5x + 6 = 0 with method explanation
Error class
Sign confusion
frequency: medium
Next question type
Scaffolded pair
one guided + one independent
AI training decision
Issue two focused factorization questions, then run a mixed-form checkpoint to confirm transfer.
Core capabilities
AI capabilities used in every training cycle.
The model separates careless slips from concept gaps, so practice targets the real issue.
Each weak answer is traced back through topic dependencies to find the true blocker.
Question complexity updates continuously based on accuracy, speed, and confidence.
Learning flow
The closed-loop training process.
01
Choose a Player
Set region, grade, and learning style to anchor the practice context.
02
Run Smart Warm-up
A short diagnostic reveals strengths and hidden gaps before full practice starts.
03
Train with Precision
MathMagicAI opens focused practice sets and clear explanations for each next move.
Player profile includes region and grade from day one.
Practice generation respects local sequencing expectations.
Training output format
Diagnosis
Error taxonomy + prerequisite map
Intervention
Generated question set with scaffold strategy
Evaluation
Mastery delta and next-step recommendation