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

CAC Co-intelligence is designed to encourage deliberate human thinking before and after AI engagement, while maintaining active cognitive involvement throughout the process.

The framework aims to help educators and students recognise and address four interconnected risks associated with AI-supported learning:

• Cognitive Offloading: Students may delegate the cognitive activities they need to practise, allowing AI to complete tasks without developing the intended knowledge and skills.

• Automation Bias: Students may place excessive trust in AI-generated explanations, recommendations or solutions without adequately questioning their accuracy, assumptions or relevance.

• Skill Erosion: Repeated dependence on AI may reduce opportunities for independent practice, potentially weakening the development or maintenance of important cognitive and disciplinary skills.

• Illusion of Learning: Students may mistake successful AI-assisted task completion for genuine understanding, developing confidence in knowledge or capabilities they cannot independently demonstrate.

These risks can reinforce one another. Excessive cognitive offloading can reduce independent practice, automation bias can discourage critical evaluation, and apparent success with AI can conceal gaps in understanding.

CAC Co-intelligence seeks to address these risks by encouraging students to establish their initial thinking, interact purposefully with AI, and return to independent evaluation, synthesis and reflection. Following the three CAC stages does not automatically prevent these risks or guarantee improved learning. Effective implementation depends on appropriate task design, instructional support and opportunities for students to demonstrate what they understand and can accomplish independently.

For a detailed explanation of the theoretical foundations, risks and limitations, see the CAC Co-intelligence Theory page.

Explanation of the layers at each stage

Cognitive (Before AI Use):

• Think first: Begin with deep reflection. Understand the problem, its context, and what truly needs solving before turning to AI. Develop pilot solutions so that human approaches are considered before being influenced by AI.

• Plan & Prepare: Define the problem clearly, explore traditional or human-driven approaches first, and decide whether AI is genuinely the right tool for this task.

• Reflect on proven methods & ethical implications: Draw on policy, existing knowledge and professional standards; anticipate potential ethical, security, or practical consequences of AI involvement.

• How can AI enhance your work?: Ask why you may wish to use AI. Is it necessary? Will it enhance the outcome, and which AI type or model best aligns with your goal?

AI Application:

• Apply as a supportive tool & not as your replacement: Treat AI as a collaborator (using an appropriate augmentation role) that enhances capability while preserving human direction and accountability. Use ethically.

• Which AI is the best fit? Type/Model: Select the right kind of AI (e.g., generative, predictive, analytical) to match your specific goal or problem.

• AI outputs can be flawed: Remember that AI can generate errors or biases; critical review is essential before use.

Cognitive (After AI Use):

• Apply Evaluative Judgment: Assess AI results critically for accuracy, logic, and alignment with your intent and values. Support students in developing this skill by requiring them to compare, synthesise, and justify their decisions across different stages of the activity.

• Refine and validate AI outputs: Edit, verify, and contextualise results to ensure they meet human and professional standards.

• Apply human touches, responsibility, and control: Integrate human voice, empathy, ethics, and ownership; maintaining human oversight over final outcomes.