
Adaptive Problem Generation for Math & Science Tutoring
Generate adaptive math & science problem sets that detect misconceptions and adjust difficulty in...
What You Can Do
Generate targeted problem sets that diagnose student misconceptions and scaffold learning pathways in real-time. This skill analyzes student responses to identify conceptual errors, then creates parallel problems at multiple complexity levels that expose gaps and build understanding. Present the same mathematical or scientific principle through algebraic, graphical, numerical, and verbal formats to match how each student learns best.
Features
Analyze student responses to identify specific conceptual errors and generate follow-up problems that address root causes rather than surface mistakes
Create parallel problems at easy, medium, and advanced levels from the same core concept, allowing you to challenge or support students at their exact competency level
Present concepts through algebraic, graphical, numerical, and verbal formats in a single problem set to reinforce understanding across learning modalities
Build diagnostic problem sequences that pinpoint exactly where learning breaks down rather than just testing what students know
Generate graduated problem chains that transition students from procedural fluency to conceptual mastery with scaffolded complexity
Create multiple problem variations on the same principle (different numbers, contexts, or structures) to prevent memorization and build transfer
Input student work and receive newly generated problems specifically targeting their error patterns
Example Output
Example 1: Algebra Misconception Detection
Student repeatedly fails to distribute correctly: -2(3x - 5) = -6x - 10
Generated Problem Set (addressing the sign error):
- Graphical: Show graphs of y = -2x and y = -2(3x - 5) overlaid; ask student to identify where they differ
- Numeric: Substitute x = 2 into both expressions and verify the discrepancy
- Conceptual: 'When you multiply -2 by (-5), what sign should the result have? Why?'
- Procedural: Work through -3(2x - 4) with explicit color-coded distribution steps
Example 2: Physics Energy Concept — Three Difficulty Levels
Easy: A 2 kg ball falls 5 meters. Calculate gravitational potential energy using PE = mgh.
Medium: A 2 kg ball falls 5 meters. At what height is its kinetic energy equal to its remaining potential energy?
Advanced: A 2 kg ball falls 5 meters through air with 10% energy loss to friction. Graph total mechanical energy vs. height and explain the non-linear relationship.
What's Included
- SKILL.md instruction file with misconception-detection protocols and difficulty-scaling frameworks:
- Misconception Diagnosis Template: structured prompt for analyzing student errors and generating targeted interventions
- Difficulty Level Rubric: criteria for easy/medium/advanced classification across math and science domains
- Multi-Representation Problem Checklist: ensures every problem set includes algebraic, graphical, numerical, and verbal formats
- Adaptive Sequencing Workflow: step-by-step guide for transitioning students from procedural to conceptual understanding
Who It's For
- Math tutors working with students in algebra, geometry, or calculus who need personalized problem sets beyond textbook materials
- Science educators creating formative assessments for physics, chemistry, or biology that diagnose specific conceptual gaps
- Special education instructors providing differentiated instruction to students with varying learning speeds and misconception patterns
- Test prep tutors building targeted remediation materials for students failing on specific problem types
- Homeschool educators needing adaptive scaffolding tools to adjust difficulty based on real-time student performance
Best For
- Diagnosing and correcting persistent student misconceptions (e.g., sign errors, conservation principle confusion)
- Creating differentiated problem sets for mixed-ability classrooms without manually designing multiple versions
- Building formative assessments that pinpoint exactly where learning breaks down rather than just testing final knowledge
- Transitioning students from procedural fluency (following steps) to conceptual mastery (understanding why)
- Generating rapid problem variations to prevent memorization and strengthen transfer skills across contexts







