CAC Co-intelligence:
Learning with AI, Not from it

A Teacher's Guide to Implementing CAC Co-intelligence

CAC Co-intelligence provides a practical approach to integrating artificial intelligence into teaching and learning while maintaining students' cognitive engagement, independent thinking, and responsibility for learning.

At its centre is a simple, iterative process:

Cognitive → AI → Cognitive (Thinking → AI → Thinking)

However, effective implementation involves more than introducing students to these three stages. Teachers need to design and facilitate learning experiences that encourage meaningful thinking, purposeful AI interaction, critical evaluation, and synthesis.

Therefore, it is important that before engaging with the information on this page, you have read the associated theoretical information.

The following guide provides a practical approach to implementing CAC in the classroom.

Step 1: Start with the Learning, Not the AI

Before designing an AI-supported activity, establish what students are expected to know, understand, or be able to do.

The availability of AI should not determine the learning activity. Instead, the learning objectives should determine whether AI is appropriate, how it should be used, and which cognitive activities students need to undertake independently.

Consider:

What knowledge or skills are students expected to develop?
Which cognitive processes are essential to achieving the learning objectives?
What thinking should students undertake independently?
How might AI extend, challenge, or support their learning?
Would AI assistance enhance the learning experience, or bypass important opportunities for cognitive development?

For example, if the learning objective is to develop students' ability to construct persuasive arguments, having AI generate a complete argument may undermine the intended learning. However, using AI to challenge an independently developed argument or provide feedback may strengthen students' reasoning.

The guiding principle is to use AI to support the learning process, not simply to make task completion easier.

Step 2: Select an Appropriate AI Augmentation Role

Once the learning objectives are established, consider what role AI should play. AI can support different forms of cognitive engagement, and teachers should intentionally select approaches that align with the knowledge and capabilities they want students to develop.

These approaches can overlap and may change during an activity. For example, students might initially use AI as a tutor to explore unfamiliar concepts, then as a Socratic questioner to test their understanding, and finally as an adversarial opponent to challenge their conclusions.

CAC remains the overarching cognitive process regardless of which AI augmentation approach is selected. Teachers should introduce different approaches progressively rather than expecting students to master them all at once.

Step 3: Design the Three CAC Stages

Once the learning objectives and AI approach have been selected, structure the activity around the three CAC Co-intelligence stages.

Stage 1: Cognitive — Thinking Before AI

Purpose: Establish independent cognitive engagement and prepare students to use AI purposefully.

Students should begin by considering what they already know, what they need to learn, and how they might approach the task. Depending on the learning objectives and students' existing knowledge, this could involve:

Activating prior knowledge and identifying uncertainties.
Developing an initial explanation, prediction, argument, or possible solution.
Formulating questions for further investigation.
Considering alternative approaches.
Deciding what assistance AI could appropriately provide.
Identifying relevant ethical considerations before using AI.

Students do not necessarily need to produce a complete solution before engaging with AI. For unfamiliar topics, identifying knowledge gaps and formulating meaningful questions may be sufficient.

Teacher's role: Model independent thinking, establish appropriate expectations, and provide scaffolding without making the first stage unnecessarily demanding.

Stage 2: AI — Purposeful Engagement

Purpose: Use AI to support, challenge, extend, or refine student thinking.

Students interact with AI according to the selected augmentation approach and the intended learning outcomes. During this stage, students should be encouraged to:

Ask purposeful questions and provide appropriate context.
Explore alternative explanations, ideas, and perspectives.
Challenge AI-generated claims and assumptions.
Request clarification or feedback.
Monitor the accuracy and relevance of responses.
Recognise the strengths and limitations of the AI system and augmentation roles.
Maintain responsibility for the direction of the interaction.

Students should remain cognitively engaged throughout their AI interactions.

Teacher's role: Facilitate productive human–AI interaction, demonstrate effective questioning and prompting, and intervene when students begin accepting AI outputs without sufficient evaluation.

Stage 3: Cognitive — Thinking After AI

Purpose: Critically evaluate AI contributions, synthesise ideas, and consolidate learning.

This stage is particularly important because students should do more than review, edit, or accept AI-generated responses. They should return to their original thinking and consider how the AI interaction has influenced their understanding. Activities may involve:

Comparing initial ideas with AI-generated suggestions.
Identifying agreements, differences, errors, and new perspectives.
Verifying important information using appropriate sources.
Determining which ideas should be retained, revised, combined, or rejected.
Synthesising their initial thinking with valuable AI contributions.
Constructing and justifying an improved understanding or solution.
Considering the importance of applying human touches, and maintaining responsibility and control.
Reflecting on what they have learned and what remains uncertain.

Students should also have opportunities to demonstrate understanding independently, rather than relying on the quality of their final AI-supported output as evidence of learning.

Teacher's role: Encourage critical comparison, synthesis, explanation, and reflection. Ask students to justify their decisions and demonstrate the knowledge they have developed.

Remember: CAC Is an Iterative Cycle

Although CAC is represented as a three-stage sequence, it is not a rigid linear process. The final cognitive stage may reveal new questions, misconceptions, or opportunities for further investigation. Students can then begin another cycle, using their revised understanding to guide the next interaction with AI.

Each iteration should have a clear learning purpose and contribute to deeper understanding or more informed judgment. The aim is progressive improvement in thinking, not simply repeated AI interaction.

Step 4: Integrate Ethical Considerations

Ethical awareness should be developed alongside students' cognitive capabilities. AI is rapidly evolving and being taken up across the world and the ethical implications need to be understood. Teachers do not need to address every ethical issue in every lesson. Instead, select considerations that are relevant to the task, the AI tool, and the students' stage of development. These might include:

Privacy and data security: What information is appropriate to share with AI?
Bias and fairness: Whose perspectives are represented, omitted, or disadvantaged?
Academic integrity and transparency: How should AI contributions be acknowledged?
Intellectual property: Who owns or has rights over AI-assisted content?
Social and economic implications: How might AI affect people, employment, opportunities, or decision-making?
Environmental implications: What resources and environmental impacts are associated with AI systems?
More examples can be found here.

Ethical questions can be introduced throughout the CAC process.

Before AI: Is this an appropriate and responsible use of AI?
During AI: Are any ethical concerns becoming apparent in the interaction or outputs?
After AI: What are the implications of using these outputs, and who is responsible for the final decisions?

Across successive learning experiences, students should encounter different ethical challenges and develop increasing independence in recognising and responding to them.

Step 5: Assess the Thinking and Learning, Not Just the Output

One of the central challenges of AI-supported learning is distinguishing between the quality of students' completed work and the understanding they have actually developed. A sophisticated final product does not necessarily demonstrate deep learning. Teachers should therefore consider how students can provide evidence of their thinking throughout the CAC process.

Possible evidence of learning:

Before AI - Initial explanations, predictions, plans, questions, or draft solutions
During AI - Purposeful questions, critical responses, revisions, and justified decisions
After AI - Critical comparisons, synthesis, reflections, explanations, and independent applications

Assessment should focus on the intended learning outcomes rather than simply rewarding students for completing all three stages. For example, students might be asked to explain how their thinking changed, identify an AI suggestion they rejected and why, or independently apply their new understanding to a similar problem.

Where appropriate, short AI-free tasks, and oral explanations can provide additional evidence of independent understanding. More information about assessment options can be found in Table 10 here.

Important: Expectations of what the students can and should achieve need to be lifted when AI integration occurs. Without raising the bar, assessment and learning becomes problematic.

The key question is not simply, "What did the student produce?" but "What can the student now understand, explain, justify, or do?"

Step 6: Progressively Develop Student Independence

CAC should become a thinking habit rather than a process students follow only when explicitly instructed. Initially, teachers may need to provide structured templates (such as the one I provide as a resource), demonstrate each stage, model effective AI interactions, and guide reflection. As students become more capable, the degree of scaffolding can gradually be reduced.

A possible progression involves three levels:

Guided Application: Teachers specify the learning goals, AI augmentation approach, and CAC checkpoints. Students practise the process with explicit guidance and feedback.
Supported Independence: Students make more decisions about how to approach tasks, which AI interactions may be beneficial, and how to critically evaluate and synthesise AI contributions.
Independent Application: Students independently determine when AI is appropriate, select suitable augmentation approaches, recognise ethical implications, and regulate their own learning through iterative CAC cycles.

This progression is not determined by age or the number of lessons completed. It should reflect students' demonstrated readiness, existing knowledge, and capacity for responsible independent judgment.

Across successive lessons, teachers can deliberately vary AI augmentation approaches, cognitive demands, and ethical considerations to broaden students' understanding and capabilities.

Final Considerations for Teachers

Effective CAC implementation does not require AI to be used in every lesson, every activity, or every stage of learning. There will be occasions when independent practice, direct instruction, human collaboration, or AI-free assessment better serves the learning objectives.

CAC should also remain adaptable. Some activities require only brief initial thinking, while others justify extensive independent work before AI engagement. Similarly, some tasks may benefit from several iterations, while others require only one cycle.

The framework should support thoughtful pedagogical decisions, not become a rigid procedure or compliance exercise. Ultimately, implementing CAC involves more than teaching students to follow Thinking → AI → Thinking. It involves designing experiences in which students learn to direct their own thinking, use AI purposefully, critically evaluate its contributions, synthesise new understanding, and take responsibility for their learning.

The goal is to progressively develop learners who can think independently, collaborate intelligently with AI, and make informed decisions about when, why, and how to use it.

Key Starting Resources

The following video is for teachers who are looking for visual guidance of how to apply CAC Co-intelligence in the classroom. Using the slide template below (designed for workshop-styled delivery), I guide you through the process.

2026 1015 CAC Co-intelligence Workshop Slides Public.pptx

Ten Implementation Tips

Be explicit about the three CAC stages (Cognitive → AI → Cognitive) and clearly signal transitions between them
Make the “learning how to learn” component explicit by prompting reflection (e.g. How did you learn by undertaking this step? What helped you improve your answer?)
Build repetition deliberately: one-off use is unlikely to establish lasting learning habits
Emphasise that CAC is iterative; the value comes from cycles, not a single pass
Enforce the first cognitive step: require visible initial attempts, time-box AI access, and verify completion to prevent AI-first dependency
Structure AI use as a critic/refiner, not a generator: guide prompts toward evaluation, assumptions, and weaknesses
Treat the final synthesis (comparison between steps 1 and 2) as the core learning moment. Require explicit comparison and justification
Use open-ended or non-routine tasks where AI is imperfect to create necessary cognitive friction
Scaffold early, then deliberately remove supports over time to build independence
Use CAC explicitly as a learning process to develop thinking. Separate this from assessment, which should be determined independently by learning outcomes

Original Resource

CAC Theory

CAC Home

CAC Co-intelligence: Learning with AI, Not from it