Physics - Performance vs Learning Matrix

Example Physics: Performance vs Learning Matrix

Mathematical & Problem-Solving Skills

Task/Activity Primary Goal AI Role Rationale & Recommendations
Unit Conversion Learning Prohibited Must master dimensional analysis independently.
Scientific Notation Learning Initially Prohibited Must understand magnitude representation independently.
Variable Isolation Learning Prohibited Critical algebraic skill for physics problem-solving.
Graphing Data Learning Initially Prohibited Must understand how to create and scale graphs manually first.
Graph Interpretation Learning Prohibited Must develop skills in analyzing physical relationships.
Data Analysis Learning → Performance Staged Implementation Master basic statistics manually, then use AI for complex analysis.
Error Calculation Learning → Performance Staged Implementation Master basic uncertainties, then use AI for complex propagation.
Linear Regression Learning → Performance Phased Approach Learn manual best-fit lines, then use AI for complex relationships.
Problem-Solving Strategy Learning Prohibited Essential process of breaking down physics problems.
Data Tables Learning Prohibited Must learn proper organization of experimental data.

Kinematics & Dynamics

Task/Activity Primary Goal AI Role Rationale & Recommendations
Position/Velocity/Acceleration Learning Prohibited Must understand relationships between motion variables.
Motion Graphs Learning Initially Prohibited Essential skill in representing and interpreting motion.
1D Motion Analysis Learning Prohibited Fundamental understanding of straight-line motion.
2D Motion Analysis Learning Initially Prohibited Must master projectile motion independently.
Relative Motion Learning Prohibited Understanding motion from different reference frames.
Graph-to-Motion Translation Learning Prohibited Critical skill linking graphs to physical motion.
Complex Motion Problems Learning → Performance Staged Implementation Master basic problems, then use AI for complex scenarios.

Force and Newton's Laws of Motion

Task/Activity Primary Goal AI Role Rationale & Recommendations
Free Body Diagrams Learning Prohibited Critical skill for force analysis and problem setup.
Force Identification Learning Prohibited Must recognize and categorize forces independently.
Newton's First Law Learning Prohibited Fundamental understanding of inertia.
Newton's Second Law Learning Prohibited Critical force-acceleration relationship.
Newton's Third Law Learning Prohibited Essential understanding of force pairs.
Normal Force Analysis Learning Initially Prohibited Understanding surface interaction forces.
Friction Problems Learning Initially Prohibited Must understand static and kinetic friction.
Circular Motion Forces Learning → Performance Staged Implementation Master basic concepts, use AI for complex scenarios.

Momentum and Impulse

Task/Activity Primary Goal AI Role Rationale & Recommendations
Momentum Calculations Learning Prohibited Must understand mass-velocity relationship.
Conservation of Momentum Learning Prohibited Essential principle requiring independent mastery.
Collision Analysis Learning → Performance Phased Approach Learn simple collisions, use AI for complex systems.
Impulse Problems Learning Initially Prohibited Understanding force-time relationship.
Multiple Collision Systems Learning → Performance Staged Implementation Master two-body collisions, then use AI for multiple bodies.
Explosion Problems Learning Initially Prohibited Understanding systems with internal forces.

Energy & Work

Task/Activity Primary Goal AI Role Rationale & Recommendations
Work Calculations Learning Prohibited Must understand force-displacement relationships.
Energy Conservation Learning Prohibited Fundamental principle requiring independent mastery.
Power Analysis Learning Initially Prohibited Essential understanding of energy transfer rates.
Energy Transformations Learning Prohibited Must track energy changes independently.
Mechanical Energy Learning Prohibited Understanding PE and KE relationships.
Energy Loss Systems Learning → Performance Staged Implementation Learn basic systems, use AI for complex analysis.
Work-Energy Problems Learning Initially Prohibited Critical understanding of work-energy theorem.

Electricity & Magnetism

Task/Activity Primary Goal AI Role Rationale & Recommendations
Circuit Analysis Learning Initially Prohibited Must understand basic circuit principles.
Ohm's Law Learning Prohibited Fundamental electrical relationships.
Series Circuits Learning Prohibited Essential understanding of current behavior.
Parallel Circuits Learning Prohibited Essential understanding of voltage behavior.
Complex Circuits Learning → Performance Staged Implementation Master basic circuits, use AI for complex networks.
Power in Circuits Learning Initially Prohibited Understanding electrical energy transfer.
Magnetic Field Effects Learning Prohibited Must understand field interaction basics.

Laboratory Skills

Task/Activity Primary Goal AI Role Rationale & Recommendations
Safety Procedures Learning Prohibited Critical understanding for lab safety.
Equipment Usage Learning Prohibited Must master proper use of lab equipment.
Data Collection Learning Prohibited Must master measurement techniques.
Experimental Design Learning Prohibited Must develop independent investigation skills.
Measurement Precision Learning Prohibited Understanding significant figures and uncertainty.
Equipment Calibration Learning Prohibited Essential skill for accurate measurements.
Digital Sensors Learning Initially Prohibited Understanding modern data collection tools.

Writing Lab Reports

Task/Activity Primary Goal AI Role Rationale & Recommendations
Title/Abstract Writing Learning Initially Prohibited Must learn to summarize experiments concisely.
Introduction/Background Learning → Performance Staged Implementation Write initially manually, use AI to enhance research.
Procedure Documentation Learning Prohibited Must master clear procedural writing.
Data Presentation Learning Initially Prohibited Must learn proper table/graph creation.
Error Analysis Section Learning Initially Prohibited Must understand and explain uncertainties.
Results Discussion Learning Prohibited Critical analysis of experimental outcomes.
Conclusion Writing Learning Prohibited Must draw own conclusions from data.
Citation Formatting Performance Allowed AI can assist with proper citation format.

Problem Practice & Review

Task/Activity Primary Goal AI Role Rationale & Recommendations
Problem Generation Performance Allowed AI can create varied practice problems.
Solution Verification Performance Allowed with Guidelines Check solutions after independent work.
Concept Review Learning Initially Prohibited Must develop understanding independently.
Test Preparation Learning → Performance Phased Approach Independent practice first, then AI-assisted review.
Example Problems Learning Initially Prohibited Must learn problem-solving strategies independently.
Practice Quizzes Performance Allowed AI can generate targeted practice assessments.

Implementation Guidelines

  1. Mastery Verification

    • Establish clear criteria for demonstrating mastery before allowing AI use
    • Create checkpoints for transitioning from prohibited to allowed AI use
    • Document student progress in mastering fundamental skills

  2. AI Usage Progression

    • Start with no AI for fundamental concept learning
    • Introduce AI for verification after basic mastery
    • Allow AI for complex problems only after strong foundational understanding
    • Use AI for performance enhancement only after learning goals are met

  3. Documentation Requirements

    • Students must show manual work even when using AI
    • Clear notation of when and how AI was used
    • Maintain learning logs to track progress and AI usage

  4. Ethical Considerations

    • Clear policies on appropriate AI use
    • Guidelines for citing AI assistance
    • Consequences for misuse clearly stated
    • Regular discussions about responsible AI use

  5. Assessment Strategy

    • Different AI policies for learning vs. testing situations
    • Clear communication about when AI is/isn't allowed
    • Regular assessment of both AI-assisted and non-AI skills

  6. Differentiation Approach

    • Adjust AI permissions based on individual student mastery
    • Create personalized progression plans
    • Monitor impact of AI use on learning outcomes

  7. Laboratory Safety and Integrity

    • Never use AI for safety-critical decisions
    • Maintain strict protocols for lab work
    • Ensure data integrity in experiments
    • Document all safety procedures

Remember: The goal is to use AI to enhance learning without replacing essential skill development. Regular review and adjustment of these guidelines may be necessary as both AI technology and our understanding of its educational impact evolve.