Thinking Tools as Prompts: Six Hats, SCAMPER, SWOT and More

Study guide

Classic Thinking Tools as AI Prompts

This course shows you how to take thinking tools you already know, such as SWOT, Six Thinking Hats, SCAMPER, RICE and Bloom's taxonomy, and turn them into prompts you can run in ChatGPT, Claude or Gemini in about two minutes. These tools were built for slow workshops with whiteboards and sticky notes, which is why they often felt too expensive to use for everyday decisions. AI changes the cost, not the tool. The same structured steps now happen in a single conversation, so you can run a full analysis before a meeting rather than booking a session for it.

It is written for busy, non technical professionals: managers weighing a timetable change, creatives hunting for fresh product ideas, and educators designing questions for different thinking levels. You will learn why naming a tool in your prompt gives you defined boxes to inspect instead of a loose pile of opinions, and how to feed the AI your real situation so the output is useful. Every part includes a worked example, the exact prompt shape, and the common mistakes that make these tools produce generic filler.

What is a prompt framework?

A prompt is the request you type into an AI tool such as ChatGPT, Claude or Gemini. A prompt framework is a reusable structure for that request: a short checklist of the parts a good prompt contains, usually named with an acronym so it is easy to remember. Why it helps: the AI fills in whatever you leave out with generic guesses. A framework makes sure you include the parts that change the answer, such as who it is for, what you want to achieve and the format you need.

Frameworks make prompts repeatable: you fill the same parts each time, save the prompt as a template and share it with colleagues. They are not magic words: the value comes from the detail you put in each part. They help most when a request is complex or open to interpretation; a quick, simple question can stay short. There are well over sixty named frameworks, and most of them remix the same ingredients: role, task, context, audience, format, constraints and examples.

In this course you learn Thinking tools, which is a set of classic thinking and management tools that work as prompt frameworks for ideas, decisions and priorities. It is best for: ideas, decisions, prioritising and teaching: managers, creatives and educators.

Old tools, new partner

Tools like SWOT and Six Thinking Hats were designed for group workshops. They need a room, a whiteboard and time, which is why they felt expensive. AI changes the cost, not the tool. The same steps now take about two minutes, so you can run a structured analysis before a meeting rather than booking a session for it. These tools are not prompt frameworks in the strict sense, because they describe how to think rather than how to write an instruction. That is exactly why they work well as prompt scaffolds.

A scaffold is a frame around a task so nothing important is missed. Asking for thoughts gives a loose pile of opinions. Naming a tool gives you defined boxes you can inspect. The difference is visible in the output: a plain request returns a paragraph, while a scaffolded request returns labelled sections you can check, challenge and fill in.

Generic input produces generic output. Give the AI your real situation: who is involved, what you are deciding, what is fixed, and what is still open. A reliable prompt order is tool first, then the situation, then the constraints, then the output format. The AI locks onto the tool before it reads your details, so the structure holds even when your notes are messy.

Six Thinking Hats in one prompt

Six Thinking Hats is a decision tool from Edward de Bono. It looks at one plan from six angles: facts, feelings, risks, benefits, creativity and process. The white hat covers facts and data. The red hat covers feelings and gut reactions. The black hat covers risks. The yellow hat covers benefits. The green hat covers creativity and new ideas. The blue hat covers process, what has been covered and what comes next.

The prompt is simple: review this plan wearing each hat in turn, then summarise. This asks the AI to work through all six angles and give a short wrap up. You can use it for any decision that affects other people, from a timetable change to a new policy. It takes two minutes and gives a balanced view.

The biggest benefit is that it surfaces the angle a team usually forgets, often feelings or risks. Without the hats, those angles get skipped because the loudest voice in the room sets the frame. With the hats, each angle gets its own turn, and the summary shows you where the gaps are before you commit.

SCAMPER for more varied ideas

SCAMPER is a checklist of seven moves: Substitute, Combine, Adapt, Modify, Put to another use, Eliminate, Reverse. Each move asks a different question about the same product, so the answers cannot all be variations of one thought. The core prompt is: apply each SCAMPER move to our product and give two ideas per move. Naming the product and the audience keeps the ideas tied to real users rather than drifting into generic suggestions.

SCAMPER produces more varied ideas than brainstorm ten ideas because the question changes between answers. A plain brainstorm has no structure, so the model tends to reword one idea ten times. A useful follow-up is to ask the AI to mark its two strongest ideas and explain the choice. That turns a long list into a shortlist you can review, while the final decision stays with you.

Two common mistakes are running all seven moves as one flat list, which buries the good ideas, and accepting the first answer without ranking. Ask for a shortlist and judge it against your own constraints. SCAMPER works on services and processes too, not just physical products. Substitute a step, combine two tasks, or eliminate a handover, and the same seven moves apply.

SWOT, 3Cs and prioritising

SWOT sorts your situation into strengths, weaknesses, opportunities and threats. Strengths and weaknesses are internal, opportunities and threats are external. It gives the AI a structured place to put your raw notes. The 3Cs look at company, customer and competitor. Where SWOT describes your position, the 3Cs describe the players. Use both together for a fuller picture before you rank anything.

RICE scores each idea on reach, impact, confidence and effort. Reach is how many people it touches, impact is how much it moves the needle, confidence is how sure you are, and effort is the work involved. ICE is the lighter version with impact, confidence and effort. Use it for a fast sort when reach will not change the order. MoSCoW sorts into must, should, could and won't, and is about commitment rather than maths.

Always ask the AI to show its assumptions when it scores. The assumptions matter more than the numbers, because you can correct a wrong assumption but you cannot check a hidden one. The score is a conversation starter, not the decision. Check the assumptions against what you know, then choose. The tools rank your thinking, they do not replace it.

Bloom's taxonomy and ELI5 for teaching

Bloom's taxonomy is a framework with six levels: remember, understand, apply, analyse, evaluate, and create. Each level represents a different cognitive skill, from basic recall to complex creation. When prompting, specify the Bloom's level to get questions that target that thinking skill. For example, write five questions at the analyse level about this text will generate questions that require breaking down information.

ELI5 stands for explain like I'm five. It is a prompt that asks the AI to simplify a complex topic as if talking to a child. Grown-up versions include explain like I'm a busy manager or explain like I'm new to the field. Combine Bloom's and ELI5 to scaffold learning. Start with an ELI5 explanation to build understanding, then ask questions at higher levels like apply or analyse to deepen thinking.

Avoid mismatching the Bloom's level to the learner's prior knowledge. Beginners may struggle with create-level tasks. Also, ensure ELI5 explanations remain accurate and do not omit critical details. Use the anatomy and chat screens as templates. The anatomy screen shows how to structure a Bloom's prompt, and the chat screen demonstrates a realistic prompt and AI response for a compliance quiz.

Thinking tools in your job

Pick one tool per job rather than using all of them. Six Thinking Hats suits decisions because it forces several angles on the same choice. SCAMPER suits idea generation because it pushes past the first obvious answer. RICE suits priorities because it scores options so they can be compared. Feed the AI real facts. A SWOT built on assumptions is fiction. If you leave out real numbers, real constraints and real customer comments, the AI will fill the gaps with invented detail, and your team will end up discussing a made up business.

Use the output to start a team discussion, not to replace it. The AI can produce options and angles quickly, but the people doing the work know the kitchen, the staff and the budget. Their challenge is what turns a list into a decision. Keep the prompt specific. State the role, the situation and the format you want. In the restaurant example, the manager gave the quiet nights, the nearby cinema and gym, and the current offer, then asked for one idea per SCAMPER letter under 30 words.

Treat the answer as a starting point. The AI's ideas are raw material. Your judgement, your team's experience and your real constraints decide what actually happens next. Run the prompt before the meeting, bring the output to the room, and let the conversation do the rest.

Frequently asked questions