Why better prompts lead to better learning, and why
What I found was that Gen AI (Gen AI) becomes a true partner in learning design when approaching purposefully. It’s not just time savings. It is a prototype generator, sounding board, and when well-inspired, a community of rich, personalized, reusable learning assets. The keys don’t just use artificial intelligence (AI). That’s the way we encourage it, and more importantly, we encourage it. As an educational designer, I am always looking for ways to scale our work without compromising quality or intention. Demand for timely, engaging, and outcome-aligned learning content continues to grow across departments, campuses and organizations. It’s not just about meeting that demand, it’s about working faster. It’s about working smarter and more collaboratively.
Some of the most effective prompts I used were not made on their own. They emerged from live cocreation sessions with faculty, subject matter experts (small businesses), team leads, and even learners themselves. Because when we prompt together, we are not just generating content, but building a common understanding. That understanding changes to templates rather than just one time. Not just the solution, but also the system. Let’s explore how to do that using three interconnected frameworks.
The prompt is the design. It’s not just a command
Instructional design uses frameworks such as Addie, Sam, and Bloom taxonomy to bring structure and clarity to those that build structure. The prompt is no exception, hopefully. That’s not a one-line question we throw at the machine. It’s an intentional design move.
Fitting quick creation together with thoughtful frameworks will give you better output. But even more importantly, we create a scalable, repeatable, teaching system that others on our team can use and adapt. One of the simplest and most powerful tools I use to do this is the Pentagon model.
Pentagon model: Prompts can be moved
The Pentagon model breaks down the key components of a well-structured prompt into five core components: persona, context, task, output, and constraints. If each of these are clearly defined, the prompts will be specific enough to provide relevant results and general enough to be reused across different learning scenarios. Let’s break this down:
Persona is about roles
Who does AI respond to? Professor, nurse, coach, historian? Giving an AI defined persona gives its output audio, viewpoint, and reliability. Contexts frame environments or situations
Is the content intended for onboarding, clinical practice, student projects, or leadership coaching? That background ensures that AI ensures how it coordinates its response. Tasks clarify the purpose
Are you asking AI to summarise, generate dialogue, simulate scenarios, or create an outline? A well-defined task keeps the output focused and useful. The output defines the format
Do you need bullet lists, dialog scripts, quizzes and charts? Setting this expectation reduces editing and improves usability. Constraint adds guardrails
Should the tone be conversational or academic? Should the response fit within the 200 word limit? Should it be suitable for learners with different reading levels?
With the Pentagon model, teams can collete prompt templates that are not tied to one situation but can be adapted to suit their department and use cases. For example, the prompts originally created to generate nursing case studies simply tweaked roles, audiences, and context, later adapted to the onboarding material in HR. The structure remained the same. This means that the process didn’t have to start from scratch. This is how you can scale your content creation with consistency and quality.
Design Thinking: Prompt as a Team Process
The Pentagon model offers excellent prompt anatomy, while design thinking provides a way of thinking. It brings empathy, repetition and collaboration. All of this will be more meaningful and sustainable. Design thinking is not just about product development. This is a creative and human-centric way to write better AI prompts. Instead of jumping straight to the output, step into the user’s shoes, experiment and refine. the goal? Prompts that make AI responses more convenient, personalized and practical.
When education designers work alongside faculty, staff and learners to create prompts, important things happen. Prompts become more co-creation processes, not solo acts.
One project developed a set of AI prompts to simulate real-world conflict resolution scenarios for professional development courses. However, rather than designing the content myself, I invited my manager, support staff and even an intern to the prompt session. Their living experiences shaped the tone, complexity and vocabulary of the scenario. result? That’s because the content I immediately found realistic and useful.
This collaborative approach speeds up iterations and increases buy-in. Instead of revisiting and revising content after missing the mark, it’s consistent from the start. And because knowledge is shared, the process becomes scalable. Others within an organization can use the same design approach to generate new content without relying on a single gatekeeper or team.
Backward Design: Adjust the prompt with your learning goals
When the Pentagon model brings collaboration between structure and design thinking, everything that the backward design creates actually supports learning outcomes. The AI’s rear design encourages borrowing from the well-known Wiggins and the Mctighe framework, but it gives a twist. It’s all about creating the prompt to get the results you actually need. Whether you’re helping AI to design lessons, create scripts, generate images, or break down data, this approach helps you stay focused on the results, not just on the output.
The rear design starts with your eyes in mind. What should learners know, do or feel after this experience? From there, we decide how to measure success (assessment). Only then will we design a learning experience and a prompt to support it.
For example, customer service training required learners to demonstrate empathy and problem-solving skills in real-time conversations. Instead of asking AI to “write a scenario,” I started with my learning goals. “Employees use active listening techniques to break away their frustrated customers.” It promoted tasks (“creating realistic conversations”), context (“in a retail setting with long latency”), and output (“roleplay script with labeled speaker turns”).
The output was quickly aligned as I tied the prompts to the performance goals. Better yet, this structure can be reused in a variety of industries. Simply substitute a hospital, university, or call center as a setting and the same framework applies. Prompts rooted in results do not drift. They scale, translate, and evolve.
Why prompts should be a cooperative habit
With AI, you can feel faster, but using AI together using shared prompt models isn’t just faster, but smarter. When stakeholders are involved early in the prompt process, avoid the typical before and after that, resulting from organized expectations. Connected prompts reflect your actual needs, use a shared language, and generate reusable formats. Over time, these prompts become part of the design toolkit. This is a library of modular components that can be mixed, matched and adapted.
Is it even stronger? Encouraging cooperation is a type of upskill. Teachers, staff and designers learn how to speak the language of AI together. They start thinking in the framework, making tasks clearer and more clearer and using AI on their own. The prompt will be shared literacy. That makes it sustainable.
Build a scalable prompt culture
Scaling content does not mean creating more from scratch. This means creating smarter and more reusable systems through collaboration. AI is useful, but only when you use it intentionally and encourage it with purpose. This really works what I’ve learned:
To build prompts using frameworks such as Pentagon models, design thinking, and backward design, you can easily access prompt jam sessions that host planning or sprint cycles, as well as review stages, as well as stakeholders early, build shared prompt templates, and make them easy to access and adapt to others.
In short, we handle designs that look like prompts. It’s collaborative, purposeful and repeatable. Move faster. You’re better aligned. And most importantly, it is to create a learning ecosystem that is not only generated, but also strategically created, embedded in the community and scaled.
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