Performance vs Learning Matrix

The Performance vs Learning Matrix helps educators thoughtfully integrate AI by clarifying which tasks benefit from AI support and which require independent student work for essential skill development. While the examples below demonstrate one approach, teachers should adapt the matrix to align with their specific course objectives and student needs.

The purpose of this page

The Performance vs Learning Matrix is a decision-making tool that helps educators thoughtfully integrate AI by distinguishing between tasks where students need to develop independent mastery (learning goals) and tasks where AI can enhance efficiency without compromising skill development (performance goals). For each course-specific task or skill, the matrix clearly identifies whether AI use should be prohibited to protect essential learning, or allowed to support performance. When AI is allowed, the matrix specifies how it should be used and what AI skills need to be explicitly taught to students to ensure effective use.

This page begins with examples of sample matrices and then walks you through a 3-step process for creating your own.

You can learn more by reading this post about the Performance vs Learning Matrix.

Examples of matrices from various courses

Creating Your Performance vs Learning Matrix: A Step-by-Step Guide

Step 1: Define Categories and Skills

Meet as a team to establish the framework of your matrix.

Tips:

  • Schedule 2-3 hours for initial meeting

  • Review course objectives and standards

  • Focus on observable student actions

  • Document your reasoning

Actions:

  • Identify 5-8 major categories that encompass your course content and skills

  • Under each category, list specific tasks/skills students should master

  • Be specific - avoid vague actions like "understand" or "know"

  • Include both basic and advanced skills

  • Consider assessment tasks and classroom activities

Step 2: Determine Goals and AI Role

For each skill/task, decide whether the goal is learning or performance, then determine appropriate AI use.

Tips:

  • Prioritize learning over convenience

  • Be specific about AI permissions and restrictions

  • Consider skill progression (learning → performance)

  • Document your reasoning

Key Questions:

  • Learning Goal: Must students master this independently for future success?

  • Performance Goal: Is this task about demonstrating/applying already-mastered skills?

  • AI Role: Will AI support or hinder the goal?

  • Implementation: If AI is allowed, what specific uses will be permitted?

Step 3: Plan AI Instruction

Review all tasks where AI is allowed and plan necessary instruction.

Tips:

  • Include both technical and ethical instruction

  • Consider varying student tech abilities

  • Create clear documentation requirements

  • Plan for regular skill assessment

Actions:

  • List specific AI skills needed for each approved task

  • Identify common AI skills across tasks

  • Create instruction plan for each required AI skill

  • Develop assessment strategies for AI skill mastery

✺ Example of Step 3: Plan AI Instruction ✺

After creating a Performance vs Learning Matrix for a course or creating them for multiple courses, you can collect all of the Tasks/Activities where AI is allowed and begin designing strategies to teach students how to use AI to accomplish these tasks and activities. Effective AI use should be intentionally taught to students.

I worked through this process for the six sample matrices that are linked above. After reviewing them, I collected all of the AI skills that need to be taught. They are listed below as an example.

    1. Using AI to efficiently search academic databases and find relevant sources

    2. Evaluating and filtering AI-generated search results

    3. Using AI to expand research after initial independent investigation

    4. Finding level-appropriate supplementary materials

    5. Using AI to locate relevant cultural and historical information

    6. Strategies for using AI to find appropriate reference materials

    7. Using AI to explore current applications and real-world connections

    1. Using AI for complex statistical analysis after understanding basics

    2. Creating sophisticated data visualizations

    3. Generating complex graphs and charts

    4. Analyzing large datasets

    5. Creating detailed timelines and maps

    6. Using AI for complex system modeling

    7. Verifying calculations and analysis

    1. Generating effective practice problems

    2. Creating targeted study guides

    3. Developing review materials

    4. Creating differentiated practice exercises

    5. Making flash cards and study aids

    6. Organizing notes and study materials

    7. Generating self-assessment quizzes

    1. Understanding when AI should and shouldn't be used

    2. Proper documentation of AI assistance

    3. Ethical use of AI tools

    4. Evaluating AI output quality

    5. Recognizing AI limitations

    6. Citing AI appropriately

    7. Maintaining academic integrity with AI use

    1. Using AI for citation formatting

    2. Enhancing research paper organization

    3. Refining outline structure after initial creation

    4. Verifying grammar and mechanics after self-editing

    5. Using AI for document formatting and layout

    6. Organizing presentation materials

    7. Structuring complex documents

    1. Managing project timelines and organization

    2. Creating presentation materials

    3. Documenting project progress

    4. Organizing portfolio materials

    5. Managing research logs and documentation

    6. Creating digital archives

    7. Planning exhibition/presentation logistics

    1. Verifying solutions after independent work

    2. Checking final answers

    3. Confirming formatting

    4. Validating references

    5. Cross-referencing information

    6. Fact-checking after initial research

    7. Reviewing work for errors after self-review

    1. Using AI to identify knowledge gaps

    2. Creating personalized study plans

    3. Tracking learning progress

    4. Generating practice assessments

    5. Using AI for self-evaluation

    6. Creating learning portfolios

    7. Documenting skill development

The Performance vs Learning Matrix is a powerful tool for thoughtfully integrating AI into education while protecting essential learning outcomes. By working through this process, educators can make intentional decisions about AI use that support both learning and performance goals.

Remember that creating your matrix is just the beginning - you'll need to explicitly teach students how to use AI effectively for the tasks where it's permitted. While this may seem like a significant investment of time and effort, it will ultimately lead to more meaningful technology integration and better student outcomes. As AI continues to evolve, your matrix should evolve too, making this a living document that grows with your understanding and experience.