Designing Scenario-Based Learning at Scale – With AI as Your Co-Author
How generative tools are helping us build richer, more authentic learning experiences without burning out our team.
Introduction: Moving from Knowledge to Authentic Application
One of the most urgent challenges in higher education today — particularly at postgraduate level — is supporting students to move beyond knowledge acquisition into authentic application of learning.
Universities are under increasing pressure to show that their graduates are not just well-informed, but able to apply theory and frameworks in real-world settings, especially in relation to their own professional or workplace contexts.
At the same time, the rise of generative AI has forced a rethinking of traditional assessment methods. Authentic learning — where learners engage in realistic tasks, solve complex problems, and reflect on professional dilemmas — is one of the most promising ways to ensure assessment remains meaningful, and resistant to automation.
This is where Scenario-Based Learning (SBL) becomes a powerful tool.
SBL immerses students in realistic situations where they must make decisions, reflect on outcomes, and justify their thinking. It transforms passive learning into active participation, offering:
Higher-order cognitive engagement (analysis, evaluation, judgement).
Opportunities to practise applying concepts in ambiguous contexts.
Pathways toward learners connecting course content with their own professional experience.
A foundation for authentic assessment and AI-resilient task design.
Yet despite these clear benefits, SBL has often been underused in online courses — not because it’s pedagogically unsound, but because it’s time-consuming and design-intensive.
That’s why, at Learning Design Solutions, we’ve begun to use AI as a design assistant to co-author scenario-based tasks — from simple text-based decision points to complex, branching simulations in tools like Articulate Storyline or H5P.
We see scenario-based learning as a key design option — not something to be used universally, but when it clearly supports the intended learning outcomes and enables students to develop real-world skills, judgement, and decision-making capacity. Where appropriate, it provides a structured, engaging way to link academic theory to professional practice.
By using AI as a co-author, we can now design and implement scenario-based learning more efficiently — even across large, multi-module programmes — while maintaining academic rigour, pedagogical integrity, and design quality.
What Do We Mean by Scenario-Based Learning?
Scenario-Based Learning (SBL) involves placing the learner in a realistic, role-based situation where they are required to make a decision — often with consequences that unfold based on their choices. This approach is active, immersive, and particularly effective for developing judgement, critical thinking, and applied skills.
It’s important to distinguish this from Case-Based Learning (CBL). While case-based learning typically presents a static situation for learners to analyse (often retrospectively), scenario-based learning goes a step further — asking learners to act within the situation, often through branching choices or simulated tasks.
In short:
Case-Based Learning = “What should they have done?”
Scenario-Based Learning = “What will you do now?”
At Learning Design Solutions, we are increasingly using SBL to support learning outcomes that require applied decision-making in authentic contexts, particularly in disciplines like leadership, business ethics, and engineering.
Why Scenario-Based Learning Matters
Scenario-based learning bridges theory and practice. It allows learners to:
Apply knowledge in realistic contexts.
Explore ambiguity and consequence.
Engage emotionally and cognitively.
Make (and learn from) decisions in low-risk environments.
These are particularly valuable outcomes in fields like leadership, ethics, business, health, and engineering — where there may not be a single “correct” answer, but rather a need to reason through complexity.
It aligns well with Bloom’s higher-order cognitive processes (analysis, evaluation, creation), and fits within Laurillard’s "practice" and "decision-making" activity types.
The Challenge: Time and Cognitive Load
Designing high-quality scenario-based activities is difficult and time-consuming. Each one requires:
A credible, context-rich narrative.
A decision point (or branching series).
Clearly articulated options with pedagogical intent.
Feedback or consequence modelling.
Accessibility and delivery considerations.
Doing this once is hard. Doing it for 50+ modules across 3 programmes (as we’re currently doing) would normally be impossible without cutting corners.
This is where AI has become an invaluable partner.
Our AI-Enabled Design Process
We don’t just ask AI to “write a case study.” We use carefully structured prompts, parameters, and iteration — based on a pedagogical framework and aligned with the module's intended learning outcomes.
Here’s how we typically use AI as a scenario co-author:
Step 1: Align with the Learning Outcome
We begin by identifying the specific intended learning outcome that the scenario is designed to support. For example:
“Evaluate ethical decision-making frameworks in leadership contexts” (Level 7, critical thinking)
Step 2: Identify the Real-World Context
In collaboration with the subject matter expert (SME), we define a realistic professional context. This ensures authenticity and relevance. AI can help suggest situations, industries, roles, and dilemmas.
Step 3: Generate a Draft Scenario
The AI is prompted to generate a draft scenario. For example:
“Create a 300-word scenario where a mid-level manager faces a conflict between meeting short-term business targets and upholding long-term ethical responsibilities. Include three possible responses, each reflecting a different leadership style or ethical approach.”
Step 4: Review, Refine and Iterate
At this point, the learning designer and SME review the output. This step is essential.
We’ve found that designing scenario-based learning with AI is always an iterative process. AI rarely produces a perfect, ready-made solution on the first try. Instead, it becomes a creative and conceptual assistant — offering useful drafts, varied phrasings, and fresh ideas.
The human input remains critical. The learning designer brings pedagogical judgement, ensuring the structure and tone are appropriate, and the SME provides essential disciplinary nuance and quality control.
Why AI Makes It Achievable
We’re not at the stage where AI replaces human judgement in learning design — and nor should we be. But what we can do is use an appropriately trained AI tool to make this kind of high-value design work far more achievable across an entire programme or suite of modules.
AI enables:
Faster drafting of ideas and options.
Greater flexibility to explore variations.
More confidence for SMEs who aren’t sure where to begin.
Less time spent on content formatting and refinement.
This leads to stronger outputs for the learning designer, a more manageable experience for the SME, and — most importantly — a richer, more authentic experience for the student.
Scaling Scenario-Based Learning
In our current large-scale programme redevelopment, we’re applying this process across dozens of modules using:
Reusable scenario templates (e.g. ethical dilemma, leadership crisis, policy conflict).
AI-assisted content generation with structured review.
A mix of delivery formats including:
Simple on-screen branching activities.
Interactive H5P objects.
Fully developed simulations using tools like Articulate Storyline.
This means that scenario-based learning becomes a scalable, repeatable practice — not a boutique addition limited to one or two modules.
Final Thoughts
Scenario-based learning is one of the most effective tools available for designing authentic, engaging, and applied online learning experiences. But its potential has often been constrained by how labour-intensive it is to produce.
By combining human expertise with the support of an AI design assistant, we’re now creating these experiences at scale — without losing sight of pedagogy, quality, or student engagement.
The result? Online learning that prepares students not just to know — but to act.
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