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6 Pro Tips to Boost your Impact<br/>as an Instructional Designer

6 Pro Tips For Creating Stronger Scenario-Based Learning 

3 non-negotiable principles for scenario-based learning

Before she starts designing any type of training, Dorothy Bouldrick, DHA, MBA always asks herself one important question: 

“What are the critical behaviors the learner is not doing well?” 

Everything for her starts there. If that behavior doesn’t change, the training isn't working.

With more than 25 years of experience in health and education, Dorothy has been designing evidence-based learning experiences that build capability, change behavior, and support measurable performance outcomes. 

“What I really love is when the evidence shows the learning experience I created actually had an impact in the workplace. That's essential.”

That is why, when she looks into building on any learning design tool, she is always in search of one that can act as a delivery mechanism for the curriculum to support the learning objectives.

A learning design tool for practice-based training

One of the most difficult skills to develop in healthcare, Dorothy explains, is delivering difficult news to patients and their families.

"It's one of the most challenging things clinicians have to do," she says. "And they don't have a lot of opportunity for deliberate practice. Often, they're experiencing it for the first time in the flow of work."

Dorothy wanted to change that. Her plan was to build simulation-and scenario-based training grounded in the evidence-based frameworks clinicians actually use. Giving them a safe space to practice these conversations before facing real patient or their families.

She was already using an avatar tool for coaching, but she still needed somewhere for the scenario itself to live. That's when she came across We Are Learning.

"We Are is essential because that's where the deliberate practice takes place. It is great for creating these realistic scenarios. They give clinicians the opportunity to practice those skills, so they can feel competent in having these difficult conversations without losing the flow of their professionalism, while being empathetic.”

And in healthcare, to ensure that training is actually helping learners change their behavior, every response a learner chooses needs to be measured against the frameworks clinicians actually use in the field.

Applying evidence-based frameworks such as SPIKES and NURSE

Dorothy’s simulation is grounded in SPIKES and NURSE.

SPIKES is a widely used six-step protocol for delivering bad news in medicine: 

NURSE is its companion for expressing empathy (


She uses both of these frameworks within We Are so they can become something learners actually use. She says,

“What I love about We Are is that I'm able to enter critical behaviors into the skills. As learners select what to say to the patient or family, I can provide them with an assessment. As learners select what they would say to the patient or the family, they get immediate feedback. They know whether they selected the right response based on the SPIKES or NURSE framework.”

This way, within We Are, Dorothy was able to set the scene as closely as possible to a realistic environment. This also meant that the learner would be responsible for selecting the conference room or the office where they would have the conversation. They need to know what the setting would be, and with the 3D characters, she was able to convey a wide range of emotions through gestures that could be incorporated into the conversation.

“So that learners will be able to see, hear, and experience the emotional part of it. This is where it gets more immersive.”

Immersive doesn’t always need a headset

Dorothy needed something that felt real; however, for her, it didn’t require expensive technology or gear.

“For me, on an enterprise level, immersive means to be able to create an environment that matches or is very similar to your real-world environment. And you don't need a headset to do that.”

She emphasizes that for training to be really immersive, you need ‘a very good script that’s grounded in real-world context, and a great tool that lets you present emotions in the simulation.’

Her scenario was written from a real patient experience. The learner plays the role of a clinician who must tell a patient, Sarah, that her cancer treatment is not working, while Sarah recounts how her early concerns were dismissed, and her husband asks what they're supposed to tell their two little girls.

"When the learner is engaging with this scenario, they're able to feel and have an emotional response based on the character's emotional response," Dorothy explains. "The 3D characters have so many emotional gestures. The learner can see, hear, and experience its emotional aspect. It doesn't sound robotic — it's very real.”

The practicality matters too. Her learners learn in the flow of work, such as sometimes on a lunch break.

"If I've got a 15-minute break and I know I have a patient I'll have to deliver bad news to at 2:00, I don't have to find a headset. I can log on, get some practice in…maybe get rid of some of my nerves before I have to do it in a real-world setting."

For Dorothy, immersive learning is a psychologically safe space.

“You can practice, you can make mistakes, and you can correct them right there in the immersive environment. So when you actually have to do this with a live patient, you've already assessed: this is not the best response."

That balance is needed. Dorothy explained that simulations can be 

 real, sometimes invoking the uncanny valley— an experience, a psychological phenomenon where a humanoid object—like an AI avatar, robot, or CGI character—looks 

 perfectly human, but slight imperfections trigger a sense of unease, discomfort, or revulsion in real humans. 

We Are Learning avoids the uncanny valley trap altogether by using a friendly, Pixar-style 3D aesthetic—an obviously fictional yet highly expressive look that feels natural to viewers. This helps to create a psychologically safe environment for learners to engage in deliberate practice and provides a positive impact on learners.

"I never have my patients die during a simulation," she says. "It's realistic enough for learners to feel the consequences of their choices, but it's not detrimental to their psyche if they don't get it right the first time."

For Dorothy, the story ends with evidence. Her simulation is wrapped in pre- and post-tests, and the real litmus test comes later, in the workplace: has this skill actually changed any behavior?

"That's the data that lets me know what I'm doing is effective. If there has been a change in the behavior, that means I selected the right tool. Selecting the right tool is just as important as having the correct content."

And she's just getting started. Beyond the conference demo, she's already planning new scenarios, including conflict-resolution training for healthcare leaders facing high-pressure situations.

"I'm just getting started, and I'm already in love with it. There are so many learning designs I want to build here."

Built on We Are is a series that spotlights the people using We Are Learning in creative, unexpected ways and the thinking behind what they build.

For Dorothy, immersion doesn't have to require a headset. It can be done with relatable characters that evoke emotion. In the latest episode of Built on We Are, Dorothy Bouldrick explained that, for her, the true value of immersive training lies in its impact on critical behavior. We also discussed how she used We Are to create training to change behavior among healthcare staff. 

Built on We Are: How Dorothy Bouldrick is creating behavior-changing training in healthcare

If you skim LinkedIn or the 2026 trend reports, you will notice that AI in learning design sounds either like a magic box that can solve all kinds of problems or an absolute mystery that is yet to be discovered. There’s a lot of buzz around agentic AI building your courses, personalization running itself, and how learning designers' roles have evolved with this.

, we had some very interesting discussions, but one question stood out. We had asked the audience, consisting of 100+ learning designers:

In one word, how do you feel about AI in learning design right now?

The chat filled up fast, and the answers were strikingly consistent:

"Overwhelming, but exciting." "Excited, but worried." "Excited, but there are so many tools." "Excited, but there was too much slop." "More cautious because of people running without guidelines."

Nobody is really anti-AI. But also, almost nobody is all-in. People felt more excited but more cautious about AI than they did a year ago.

Tim Slade named the feeling as a duality.

"Professionally, we're excited about the possibilities that AI opens up and enables for us. But personally, there's a lot of hesitation about what AI means for skills, our jobs, intellectual property, the environment and the economy."

That duality deserves better than another hype list. So instead of publishing the usual predictions, we're doing something different: taking the biggest claims circulating in L&D right now and holding them up against what we discussed with practitioners. We have also included Lars-Petter's own field notes coming after 25+ years of building learning, from agency work to co-founding We Are Learning.

Claim by claim. What's true, what's hype, and what it means for your work in learning design.

1: "Agentic AI is replacing the content factory"

 We no longer need to ask if AI can build a course. Agentic AI - unlike a copilot that assists a human - diagnoses training needs, maps objectives, writes content, generates assessments, and deploys finished SCORM packages without a human author in the loop. Some go so far as to declare that traditional instructional design is obsolete for modern enterprises.

 When we asked practitioners about the AI-generated training they'd actually encountered, they mentioned that it was “full of tells, generic, unimaginative." "It comes off lazy and disingenuous." "Relying on AI helps build faster, but if not done with intention, the result is crap."

During the conversation, LP also mentioned that the output isn’t meaningfully improving.

"It is not magically getting better from what I have seen so far…The skill still lies in how well you understand and can apply learning design. AI can support you, but it doesn't do the work for you."

As a learning designer it lies in your hands to decide what actually needs to change in learner behavior, understand the emotional stakes of a course, and whether a simulation rings true or even ask the harder question: 

is this the right training intervention at all? 

Only when you understand this can you put AI to work to help produce a course.

 The content factory isn't being replaced; it's being replicated at speed. If your value is production alone, AI is genuine competition. But if your value is intent, analysis, and judgment, then you have to think beyond AI. It is then simply an output tool, and not the whole package. As one attendee put it: "With AI doing the design, it's opening up more time for analysis."

Read the article

AI in Learning Design: Its limits and advantages

2: "Hyper-personalization at scale is finally here"

 AI can now create learning paths that adapt to each person's role, skill gaps, prior knowledge, and behavior data, with reports of completion rates jumping 30% when paths are meaningfully personalized.

 It sort of can…perhaps. But for most tech stacks this is still something for the future. And very often, you don't 

There are two points here that a lot of trend reports skip.

 isn't always the goal. If you're training 4,000 frontline workers to respond to a safety situation in one specific, consistent way, adaptive divergence is a bug, not a feature. Compliance, safety, brand, and procedure training often demand uniformity, and not quite personalization.

Second, today's AI personalization mostly works on the flattest possible content. Adaptive paths tend to assume standardized building blocks — text, quizzes, images — that can be reshuffled per learner. The more immersive an experience becomes, with characters, scenarios, branching dialogue, and emotional stakes, the harder it gets to personalize automatically.

And then there's the bill. There have been instances where an L&D leader at a major tech company projecting a 10x increase in token costs next year, with some in the industry talking about 50x.

Imagine those costs if you have 50,000 employees!

Free-flowing, per-learner AI generation looks very different when the tokenomics land on your budget line.

As Tim framed it: teams used to budget for human capital alone; now it's human capital 

 AI token capital, and balancing the two is a new discipline.

 The organizations getting real value from personalization did the unglamorous foundational work first — clean skills taxonomies, defined job architectures, meaningful learning objectives. This is exactly what needs to be done, even before you start using AI.

Then, personalize where variation helps learning. Standardize where consistency 

 the learning. Knowing the difference is a design decision, not a platform feature.

 AI-driven coaching simulations and digital humans now make practice-based learning — once a premium option requiring weeks of expert build time — available to everyone, on demand, at scale.

 There’s a pitfall here, the uncanny valley.

"AI can absolutely create a virtual person you need to interact with," LP notes. "But the issue is that it very often is still a bit uncanny.” We still get AI-generated avatars that don’t quite look real, and not too artificial either, but there’s an eerieness to it that can often distract learners from completely focusing on training.

His broader point from the webinar: as a learner, you shouldn't be able to identify anything as AI-generated. The uncanny valley doesn't just apply to 3D faces — it applies to any AI output that doesn't feel like it was made by someone with intention. When it does feel that way, the whole experience falls flat.

"When it works really, really well, my tolerance for it goes way, way up," he said, describing the self-driving cars in Phoenix — right before recounting the time one trapped him doing a donut in a gas station. "I was like, get me out of here. I need a human driving this thing."

So where does AI-driven dialogue genuinely work today?

Language training, where conversational practice with a patient, tireless partner is exactly the point. Presenting to a virtual crowd. Rehearsing structured, high-repetition conversations. In those contexts, AI practice is a genuine breakthrough. But as a blanket default for every scenario produces exactly the flat, creepy experiences that make learners distrust the whole category.

 AI can build the structure, write the dialogue, and wire up the branching. But choosing 

 AI-driven practice belongs — which decisions matter, what's at stake, what good behavior looks like, and where a human or a crafted scenario serves better — is design judgment. Match the tool to the moment, and your learners' tolerance for AI goes way up. Mismatch it, and you've built a donut in a gas station.

4: "Learning designers are becoming strategists and data interpreters"

 As AI absorbs production, designers evolve into "learning strategists, experience architects, and data interpreters who align solutions with organizational goals." Specialist production skills fade; strategy is the future of the role.

 "A great learning designer — who has always been something of a jack of all trades — now no longer has an excuse for creating boring learning content," his says.

"But even with AI, it still requires creative skills, imagination, playfulness, an eye for detail, a visual mind. For a learning designer these are still key skills that are hard to master. AI does not do this job for you. And if you believe it does, and move over to focus only on strategy and data, then you will not do your job and will not change anything — because the actual content facing your learners will still be boring, unengaging, and flat."

In other words: strategy without craft is just as sloppy.

One attendee shared that their department "was reduced because it was seen as just order takers, and AI is faster."

Tim's warning in the webinar was the same: "If the business just sees us as order takers, eventually they're going to go — I don't need you to be my order taker. AI can be my order taker."

But the answer isn't fleeing production for pure strategy. It's raising the bar on both. Tim again: "The commodity of the future is imagination — who has the best ideas, and who has the best ability to explain those ideas and get to a prototype as quickly as possible."

And LP: "There's no excuse for bad or boring, unengaging learning anymore. It really isn't."

 Yes, move upstream — closer to behavior, business outcomes, and evaluation. And keep your craft sharp, because AI just removed every excuse for the click-next course. The designers who thrive won't be the ones who abandoned making things for slide decks about strategy. They'll be the ones whose taste, imagination, and eye for detail direct the AI — and whose learners can feel the difference.

Vibe coding is genuinely impressive. Both Tim and LP mentioned during the webinar: "If you haven't tried a vibe coding or vibe designing tool, I encourage everybody to give it a spin," Tim said, describing outputs that were "shocking" — things he didn't believe would be possible a year ago. 

LP agreed: for the ideas that used to die with "that can't be done with our templates" or "that's a $10,000 custom build," you can now have a working interactive prototype in minutes. As a prototyping and ideation tool, it's already 100% useful.

But the audience's questions cut straight to the unglamorous part:

How do you edit this content a year from now?

What happens when the tool you used disappears?

These aren't edge cases. They're the difference between a demo and a learning ecosystem. LP named the practical gaps on the call: vibe-coded output has to be hosted somewhere; large organizations won't accept files "flying around"; many tools still fumble basics like image handling; and security, procurement, and IT reviews ask questions — which models, hosted where, governed how — that a one-off generated artifact can't answer.

Tim's counterpoint is that much of this is promptable.

You can prompt a SCORM wrapper, alt text, captions, tab order — if you know to ask, which itself is a designer skill. And maybe some content becomes disposable: why maintain what you can regenerate? But "just regenerate it" is not a governance strategy when the content encodes compliance requirements, brand voice, localized versions, and a year of stakeholder revisions.

Content built without a plan for ownership, versioning, and updating is content with an expiry date.

 The real question is how the vibe coding and course authoring merge: the creative freedom of describing what you want, inside an environment that gives enterprises what they require — inline editing, localization, versioning, hosting, security, and a system that still opens a year later. 

That convergence is where we're placing our own bets at We Are Learning, and it's the question worth asking of every tool in your stack: 

not just "what can it generate?" but "who maintains what it generates?"

Look across these reality checks and one thing is impossible to miss: 

the work AI cannot do is the work that has always mattered most in learning design.

Asking the right question before building anything. Understanding why behavior hasn't changed yet. Designing a scenario that mirrors real emotional stakes. Choosing the right intervention for the right gap. Evaluating whether anything actually shifted. And — LP would insist we add — making the thing itself genuinely engaging, because your learners never see your strategy deck. They see the content.

These were always the valuable parts of the job. The writing, the formatting, the narrating, the rebuilding — necessary, but never sufficient. AI is absorbing the necessary. What's left is the craft, and the bar for it just went up.

AI won't save bad learning design. But in the hands of thoughtful designers, it might finally help good learning design reach everyone.

Watch now

The webinar, hosted by Jordan with guests Tim Slade and Lars Petter Kiosk, explored how AI is changing Learning & Development (L&D) and learning design work. The session focused on what has shifted (tools, workflows, interactivity) versus what has not (how people learn, the need for human-driven context and stakeholder alignment). 

Faster? Cheaper? Better? Learning design in the age of AI

We asked a live audience of learning designers how they really feel about AI, then paired their comments and responses with notes from We Are Learning co-founder Lars-Petter Kjos, a learning designer and EdTech expert since 1998, and Tim Slade, creator of The eLearning Designer's Academy. Here's what we found about the AI hype in learning design, what's real, what's overstated, and the skills that are most important right now. 


Watch webinar on-demand

A reality check of AI hype in learning design from 100+ practitioners and two experts

Most leadership skills are humane and relational. They cannot be learned by heart or incorporated through a manual. These skills are demanding qualities, such as self-awareness, compassion, vision, and communication…among others. Most leaders build them over the years, or while learning on the job. That is what leadership training exists to support: helping them develop these qualities to the point where their teams can rely on them, and where they can help those team members reach their full potential.

A great leader is as much a team player as a decision-maker, and either way, the capability comes from practice.

This is also why the training format matters so much. Imagine you had a manual for a CEO explaining what kindness means. But when an employee who has made a mistake at work that could cost the company apologizes, the leader is expected to serve as the bridge between the employee and the company. They have to be kind but also ensure that these mistakes do not repeat. A handover document might not teach a leader how to do that. But immersive learning provides that space for a nuanced understanding.

It provides the right opportunity to rehearse the hard moments safely, before they happen for real, rather than only hearing or reading about them.

And now, AI has steadily become part of that picture.

What are the advantages of using AI in learning design? And how is it limited? 

AI in Learning Design

Harvard Business Impact's 2025 Global Leadership Development Study

 of more than 1,100 L&D and HR professionals across 14 countries, 55% of organizations now prioritize generative AI and machine learning in their leadership strategy, up from 43% a year earlier. Much of that interest is about making immersive practice easier to produce and to scale.

The role of AI in leadership training, in other words, sits inside the larger immersive learning story rather than beside it.

So, the intent to make training immersive is clearly there. What often isn't there yet is the ability to deliver. A handful of gaps still sit between organizations and the immersive leadership training they want, and they appear consistently across practitioner surveys, academic research, and market data from 2025 and 2026.

Here are six of them, but we have also tried to address how each gap can be closed.

1. The human complexity of leadership is hard to replicate

The most consistent finding in the field concerns the human texture of leadership.

Training Magazine's March 2025 pulse survey

 of L&D professionals, 59% said AI cannot fully grasp cultural nuance, and 65% felt that emotional intelligence training still benefits from a human hand. On the other hand, 58% also believed AI genuinely improves leadership training.

They were simply marking where it helps and where it doesn't, with around 30% noting a risk that AI could reinforce bias.

That distinction is fair. Leadership is relational and context-dependent. A structured feedback conversation is straightforward to practice with AI, but capturing culturally specific or emotionally charged moments is harder to do well.

That’s why you need to apply AI where it is strongest, such as generating, varying, and scaling well-designed scenarios, while keeping experienced learning designers in the authoring loop to shape the cultural and emotional detail.

2. Reaching every leader who needs the practice

Immersive practice matters most when it can reach everyone who needs it, not just a subset of an organization. If well designed, VR and high-fidelity simulation can help deliver powerful, deeply realistic experiences and remain widely used to good effect.

However, to make leadership training truly accessible, many organizations are looking for forms of immersion that run on the devices people already have, so they can extend practice to more of their leaders without standing up specialized hardware or facilities.

It is important to broaden the definition of immersive learning to include experiences that don't depend on a headset.

Browser-based 3D scenarios, branching simulations, and interactive roleplay create a genuine sense of presence and consequence while running on an everyday laptop or phone. It is far more accessible. It can also complement headset-based approaches rather than compete with them, and it lowers the practical barrier to delivering immersive leadership training to an entire population of managers.

Find out more

How do you define immersion? Can you create an immersive learning experience without a headset?

What is immersive learning?

3. Production often can't keep pace with the business

Even when organizations want immersive content, building it quickly is difficult. Traditional production takes time, budget, and specialist skills that most L&D teams don't have in-house.

 research framed the cost of this: despite more than $400 billion spent on corporate training, 74% of companies report they are not keeping up with their own demand for new skills, meaning a significant share of that effort lands too late to be useful.

But with AI-assisted authoring, production time shortens from weeks to an afternoon and puts creation in the hands of the people who understand the learning need.

When a learning designer can build a polished interactive scenario themselves, immersive training becomes a routine part of the function's work rather than an occasional special project.

Measurement remains one of the most persistent challenges. Harvard Business Impact found that most organizations still rely on employee surveys (62%) as their primary read on leadership effectiveness, which is a limited proxy for actual behavior.

Many current training tools don't connect knowledge to practical action. Immersive platforms haven't fully solved this either; they tend to produce engagement data such as completion rates without linking it to whether someone has genuinely developed as a leader.

The path forward runs through the data that scenario-based learning naturally generates. A branching simulation records the decisions a learner makes, which path they chose, where they paused, how they recovered, not just whether they finished.

Connected to LMS analytics and xAPI-style tracking, that behavioral signal gives a more meaningful basis for measuring outcomes than a satisfaction survey. It helps when the format and the analytics layer are designed to work together from the start.

Leadership development is largely about soft skills: communication, empathy, influence, conflict resolution, and decision-making under pressure. The 

World Economic Forum's Future of Jobs Report 2025

 ranks leadership and social influence among the fastest-growing skill needs through 2030, alongside analytical thinking and resilience. These are also the skills that are hardest to teach immersively at scale.

Approaches built around live human facilitation produce real depth but are harder to scale. Highly automated approaches scale easily but can lose some of that depth. Delivering depth, scale, and affordability together — for the broad market rather than only the largest enterprises — is still an unsolved problem for much of the field.

maintains its depth without requiring a facilitator in every session, 

can help create realistic interpersonal situations, branching conversations, and contextualized workplace moments, 

can be paired with multilingual delivery so one strong program can serve a distributed, global workforce,

Reusable templates for recurring leadership moments, such as difficult feedback or change communication, help make this repeatable rather than bespoke each time.

6. Leaders need AI literacy, delivered as practice

A newer gap is specific to this moment. Leaders increasingly need to manage AI-augmented teams, make sound AI-related decisions, and guide people through automation-driven change.

McKinsey's January 2025 "Superagency" report

 found that while 92% of companies plan to increase AI investment, only 1% consider themselves mature in AI deployment. The WEF reinforces the demand: AI and big data literacy is now the second-fastest-growing skill employers are seeking.

The way this is taught hasn't caught up. AI literacy is often delivered as static content — slides, videos, and quizzes — which struggles to build the capability leaders actually need. The disconnect is visible in the data: in one 2026 study, 77% of executives believed their managers were prepared for AI-related skills conversations, while only 34% of those managers felt prepared, and just 9% of individual contributors agreed.

Leadership relies on qualities that are hard to measure but essential for helping teams succeed. Immersive training is an effective way to build experiential skills like self-awareness and empathy, though delivering it comes with its own challenges.

Stay updated with the latest news from We Are Learning

Resources

Articles

Built on We Are: How Dorothy Bouldrick is creating behavior-changing training in healthcare

A reality check of AI hype in learning design from 100+ practitioners and two experts

6 immersive training gaps in leadership and how to close them

Built on We Are: How Fabio Gatto and NT Group use We Are to turn store staff into confident advisors

Built on We Are: How three professionals reimagined immersive learning

Frontline training: Why it fails and how to fix it?

Deskless by design: 6 principles to make frontline training stick

AI in learning design: Where AI succeeds and what are its limits

How immersive learning prepares your staff for better customer experience

Immersive Learning ROI: Why retention is the metric that actually matters

Pedagogy meets performance: Speaking the language of the business

Built in Norway, scaling globally: We Are Learning raises €2.3M to grow in the US and beyond

We Are Learning is recognized as a 2025 Training Magazine Network Choice Awards winner in the Authoring Tools category

Captivate and motivate learners like a pro: Expert engagement strategies

5 webinars to empower instructional designers in 2025

Unlocking the future of learning: How AI is empowering instructional designers

Becoming a next-generation instructional designer

Immersive learning: What is it and how to bring it to life

We Are Learning recognized for educational technology innovation in 2024 - EdTech Breakthrough Awards Program

EdTech company We Are Learning joins forces with Dreamcraft Ventures, and gears up for international expansion