Using AI in Instructional Design to Increase Cognitive Demand

How instructional designers can use AI to create deeper learning, improve decision-making, and design for real-world performance.

AI Has Made Production Easy—Now Thinking Matters More

AI can generate a course outline in seconds. It can draft objectives, write scripts, generate assessments, and assemble a complete learning pathway. Speed is no longer the differentiator.

So, what is?

AI hasn’t reduced the value of instructional design expertise; it has clarified it. When production becomes easier to execute, thinking becomes more visible. The tool amplifies whatever level of analysis, intentionality, and performance alignment precedes the prompt.

The real opportunity with AI isn’t efficiency. It’s cognitive demand.

What Do We Mean by Cognitive Demand?

By cognitive demand, I don’t mean adding difficulty for its own sake. I mean designing learning experiences that reflect the complexity, constraints, tradeoffs, and ambiguity of real-world performance. By cognitive demand, I don’t mean adding difficulty for its own sake or increasing cognitive load in ways that overwhelm learners. Recognition and recall sit at one end of that spectrum. Analysis, prioritization, and decision-making sit at the other. AI can scale either approach. Instructional designers have the opportunity to choose which one.

Beyond Recall: Designing for Real-World Thinking

In practice, raising cognitive demand means asking learners to interpret signals, weigh competing priorities, anticipate consequences, and make decisions with incomplete information. The real world rarely presents problems as clean multiple-choice questions. It presents messy situations where several options are plausible and the best response depends on context. Designing for that level of thinking is what encourages transfer.

Why Cognitive Demand Drives Real-World Performance

AI gives instructional designers a powerful way to scale those conditions. Instead of building a single scenario, we can rapidly generate variations that change the constraints, the stakeholders, or the risks involved. One version might introduce time pressure. Another might introduce conflicting advice from two credible sources. A third might add organizational consequences that unfold later. Each variation forces the learner to re-evaluate the situation rather than simply remember the content.

How AI Can Increase (or Decrease) Cognitive Demand

If your instructional design workflow centers on content, AI will accelerate content. If the workflow begins with performance gaps, critical decisions, environmental constraints, and observable behaviors, AI accelerates that thinking instead. The depth of the output reflects the depth of the thinking.

Scaling Shallow Learning

A content-first approach leads to faster production—but often reinforces surface-level knowledge.

Scaling Deep, Decision-Based Learning

A performance-first approach uses AI to expand complexity, variation, and judgment—leading to stronger transfer.

Designing Scenarios That Reflect Real Decisions

Consider a scenario where a project manager is leading a high-stakes client implementation. The deadline is fixed. The client is influential. A team member raises a potential compliance concern that could delay delivery. Investigating thoroughly may protect the organization but strain the client relationship. Moving forward preserves momentum but introduces risk.

From Knowledge Checks to Judgment Under Constraint

A Surface-Level Prompt

A surface-level AI prompt might generate a knowledge check about “risk management best practices.” That reinforces policy awareness.

A Performance-Oriented Prompt

A performance-oriented prompt reframes the task:

• Introduce incomplete information
• Add stakeholder pressure
• Present competing incentives
• Model downstream consequences for each decision

Now the learner must weigh ethical responsibility, financial exposure, client trust, and long-term credibility. There is no single correct answer, there is judgment under constraint.

Practical Ways Instructional Designers Can Use AI

This is where AI becomes powerful in the hands of an experienced instructional designer. Instead of asking it to “create a course,” we ask it to:

• Generate contrasting cases with subtle variations
• Escalate ambiguity across practice rounds
• Introduce conflicting expert advice
• Calibrate feedback based on the learner’s level of reasoning

AI naturally defaults to explanation. Instructional design expertise redirects it toward application.

Start With Decisions, Not Content

Raising cognitive demand does not require larger budgets or complex technology. Often, it begins with a simple shift: from asking what learners should know to asking what they must decide and under what conditions. AI makes it easier to build layered scenarios, varied constraints, and progressive complexity, the kinds of conditions that support transfer rather than temporary recall.

AI Doesn’t Replace Expertise—It Reveals It

When everyone has access to AI, access is no longer the advantage.

The advantage is the ability to diagnose performance accurately, frame meaningful decisions, design for ambiguity, and use AI to deepen, not dilute, the thinking required.

AI raised the bar.

The question is whether we are using it to design at the level real performance demands.

If you’re integrating AI into your workflow, start with decisions, not topics. Identify tradeoffs. Calibrate feedback intentionally. Use the tool to expand cognitive demand, not just accelerate production.

Because in the age of AI, expertise isn’t replaced.

It’s revealed.

By Jenica Jones, Lead eLearning Specialist