Research, professional learning and shared guidance for purposeful AI use.
The framework behind the work
Collaborative Intelligence
Shifting AI from a student tool to an educator’s catalyst.
Collaborative Intelligence is the approach I use to bring professional judgment and AI capability together. It starts with the educational purpose, then connects platform selection, strategic customization and effective prompting. I shared this framework in my NESA Fall Leadership Conference workshop.
Platform selection
Where should I do this work? Choose an environment that fits the task, the source materials and the access your school provides.
Strategic customization
How should AI understand my professional context? Work with AI rather than simply asking it to work for you. Establish the priorities, standards, teaching principles and evidence that should shape its contribution, then use the dialogue to develop and challenge ideas together.
Effective prompting
How should we work through the problem together? Use dialogue to clarify intent, question assumptions and revise the work with professional judgment.Prompting Hint: Use the Silver Bullet Prompt
The Silver Bullet Prompt
“Ask me any clarifying questions necessary to ensure that you are 99% confident you know exactly what I want.”
Use this invitation to begin a conversation. Answer the questions, review the work, and ask for changes. The percentage expresses the desire for a shared understanding; it is not a measured guarantee.
Schoolwide leadership
AI with purpose, across every level of CAS
As the AI Initiatives Lead, I work with a team of teachers, aides, faculty, and staff from each level of our organization. Together, we are developing practical ways to build AI capability while keeping human judgment, student agency, and safety at the center.
“To guide the purposeful, responsible and effective use of Artificial Intelligence (AI) across Casablanca American School, building capability at all levels while advancing teaching practice, student learning and the development of appropriate school policies and procedures.”CAS AI Working Team — Purpose and Goals, 2026–27
Differentiate professional learning so colleagues can use AI purposefully in planning, assessment, feedback, and everyday work.
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Students
Develop age-appropriate AI literacy that supports inquiry and independence without replacing the thinking students need to do.
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Shared expectations
Build connected policy, literacy, and learning-continuum guidance for responsible use across the school.
Research into practice
A substantial evidence base, distilled for decisions
Our initial planning drew on IB and UNESCO material covering AI literacy, pedagogy, ethics, assessment, and school policy. Working with my AI agent, we synthesized that research into an internal working reference to help the team weigh options and set priorities.
The CAS AI Profile is a live, formative tool that helps teachers reflect on their practice across five dimensions and identify useful next steps. Working with my AI agent, I helped design the diagnostic and build the application that delivers this school instrument.
Answer from your own teaching experience, or explore a hypothetical teacher profile to consider how differentiated professional learning could work at your school.
This is the existing 20-question diagnostic used with CAS high school teachers. Leaders are invited to explore it as a working example; the questions have not yet been adapted for leadership roles. No email address or account is required. It reports five dimension placements, rather than one overall score, and provides a downloadable PDF. Public answers and results are not saved to a CAS account.
Five dimensions of practice
Select a dimension for an explanation and example.
Human-centered practice keeps educational judgment and student agency visible. An AI suggestion can be useful, but a teacher must compare it with the student’s work, circumstances, and learning goal before acting.
For example: If a tool recommends a support strategy, the teacher checks the evidence, decides what to try, and later asks whether it actually helped. The decision remains accountable to people, not the tool.
Ethical use means anticipating who could be helped or harmed by an AI-assisted choice. This includes student privacy, bias, accessibility, transparency about AI use, and who is responsible if an output is wrong.
For example: Before sharing student work with a tool, a teacher checks the school’s rules and removes identifying details. They also review the output for unfair assumptions and make its role clear to students.
Knowing how AI tools work at a practical level helps educators choose the right tool for a task and recognize its limits. Fluent output can still be inaccurate, incomplete, or poorly matched to a school context.
For example: A teacher may use AI to propose lesson ideas, then verify any factual claims, inspect the source material, and decide which ideas genuinely serve the lesson.
Pedagogy starts with what students need to learn. AI belongs in an activity only when it improves that learning without doing the thinking students are meant to practice.
For example: Students might critique an AI-generated business recommendation against case evidence, defend a different choice, and reflect on what the tool missed. The teacher looks at their reasoning, not merely the polished output.
Professional learning is an ongoing cycle of trying, checking, revising, and sharing. A new tool is not evidence of better practice by itself; the useful question is what changed for learners or colleagues.
For example: A teacher tests one AI-supported feedback routine, gathers student responses, adjusts it, and shares both the benefit and the limitations with colleagues.
Before writing, students diagnose the prompt using three T’s:
Task
What action does the command term require, and how much depth do the marks call for?
Target
What precise result, relationship, decision, or effect must the response address?
Topic
Which business concept and case context give that target meaning?
The three T’s help students answer the question that was asked at an appropriate length rather than writing everything they know about a topic.
For developed responses, students build a Claim–Reasoning–Support chain. It is a planning check, not a requirement to write three separate sentences.
Claim
Answer the diagnosed Target directly.
Reasoning
Explain how or why the claim holds.
Support
Make the reasoning credible with relevant case evidence, data, course knowledge, or another sound basis.
In a ten-mark answer, students map several such arguments, compare and weigh them, and organize the logic that leads to a judgment.
A ten-mark answer needs more than a set of good individual points. Students turn their completed Map into a coherent response through four moves:
Shape
Check that each planned argument can be expressed clearly.
Sequence
Arrange the arguments so their relationships, balance, and relative weight are clear.
Check
Test the planned flow against the Task and markband before drafting.
Write
Produce the introduction, body, and conclusion, ending with a definitive judgment that answers the prompt.
If the reasoning breaks down while writing, the student returns to the Map and repairs it there.
The pressure: Consumers are asking harder questions about fairness and responsible products while becoming more price-sensitive. Trust is fragile, and the firm must respond without simply passing every cost to customers.
The decision: Which tactic protects credibility and competitiveness, and what trade-off would the team accept in the short term?
The pressure: Digital tools and production technologies promise speed, data, and efficiency, but standards are still emerging. A rushed rollout could disrupt service or reliability.
The decision: Should the firm invest in new capability now, wait, or adapt more selectively? Teams justify the risk and likely longer-term benefit.
The pressure: A recession weakens demand and makes customers more price-sensitive. Competitors are retreating or repositioning, creating both danger and opportunity.
The decision: Students weigh immediate financial resilience against moves that could strengthen the firm when conditions improve.
The pressure: Carbon taxes raise the cost of carbon-intensive operations and supply chains. Firms can absorb the cost, seek a quick workaround, or redesign more deeply.
The decision: Which tactic improves environmental performance without putting the firm’s short-term survival at undue risk?
The pressure: Geopolitical tension and fragmented regulation unsettle supply chains and market access. Responsible practices and innovation may create new routes forward.
The decision: Teams choose how to adapt to uncertainty while considering both current exposure and future positioning.
The pressure: New laws require greater product durability and sustainability. Noncompliance can bring fines or exclusion from important markets, while redesign costs time and money.
The decision: Students must choose a response that preserves market access and explain how the firm will handle the transition.
The pressure: Customers, employees, and regulators expect a firm’s stated values to show up in its actual treatment of people, products, and the environment.
The decision: Teams decide whether their tactic represents a substantive commitment or merely a message, and weigh the cost of credibility over time.
