A meta-analysis landed last week and my first reaction was: so what? Then, looking further, it does get interesting.
Ninety two randomised trials found that ethics and moral education works better when people talk to each other than when you push information at them. Pause for “so-what” reaction… Okay, we have known that for decades, e.g. 70/20/10.
Then I came back to it, and I think a lot of us are about to make the same mistake with it.
What the study actually found
Published in Nature Human Behaviour on 26 August, a team including researchers at the University of Queensland pooled the randomised evidence on interventions designed to shift ethical and moral outcomes.
A pooled effect of g = 0.65 (effect size = “how big”) (95% CI [0.49, 0.81], p < 0.001). Geeky, I know, I will explain. Just know that this number is large by education standards.
The moderator is the interesting bit. Interventions with added student discussion were more effective than those “relying solely on unidirectional or passive information transfer”.
What moved was moral sensitivity, judgement and motivation. Character development was inconclusive.
Caveat – (before you put this in a business case) only one of the 92 studies was rated low risk of bias, and the population is students, not employees. Transfer to a workplace compliance audience is a reasonable inference, not a finding.
Here is what changed my mind.
First, this is not a 70/20/10 finding.
70/20/10 is a claim about where development comes from across a career: experience > then social > then formal. It has always been on soft evidential ground anyway, having come out of retrospective survey work with executives in the late 1980s rather than anything controlled.
This meta-analysis sits entirely inside the formal 10.
Discussion here is not the 20 beating the 10. It is a design feature within a structured intervention, and the finding is that the structured intervention works at g = 0.65 (effect size) when it includes discussion.
That says formal learning design is worth funding – yet the solo asynchronous module is the weak build of it.
Second, the number is the news, not the direction.
Most of us have believed this for years and could not price it. Believing something and being able to put a defensible figure next to it in a proposal are very different positions to negotiate from.
Third, belief has not changed practice.
Everybody in this industry “knows” discussion works. The industry still ships passive compliance modules by the million, every year, in every sector. I build them all the time. When a belief has been widely held for twenty years and has not changed what gets built, the constraint was never the belief. It was that nobody could justify the cost of the better build against a cheaper one that ticks the same box.
Evidence that reprices the alternative is useful precisely because of that.
If you hold the budget, two things follow:
You are probably buying the weaker build of something that works. Compliance and ethics is the highest volume category in corporate learning, and it is almost always commissioned as the exact intervention this study says underperforms: a self paced module that transfers information, followed by a knowledge check. Not because anyone thinks it is best, but because it is procurable, auditable and cheap per head.
Your reporting cannot see the thing that moved. The outcomes that improved were sensitivity and judgement. Completion rates and multiple choice scores do not measure that. If your compliance dashboard is green and your conduct incidents are not falling, that is not a mystery. You are measuring the wrong end of the intervention.
Learning Leaders – what to do about it:
WHAT
HOW
Pick one high stakes program.
Most likely conduct, safety or ethics, and re-scope the next build. Same budget, different split: less production polish, more facilitated discussion, even if that means a 45 minute team conversation instead of a 20 minute module.
Add one judgement measure before you rebuild anything.
Two ambiguous scenarios, “what would you do and why”, scored on the reasoning. You need the baseline more than you need the new module.
Change what you ask vendors for.
If you ask for a module, you will get a module. Specify the outcome and the discussion mechanism, and see who can actually build it.
Push back on completion as a headline metric.
Do it with your own executive. This study gives you the language to do it without sounding like you are avoiding accountability.
Forinstructional designer: If you build the thing.
Design the e-learningas the stimulus. The module carries the case, the ambiguity, the consequence. The conversation carries the judgement work.
Build for the facilitator as much as the learner. Discussion guides, debrief structures and observation rubrics are the products almost nobody makes well.
Ask people to commit before revealing the answer – then display how peers responded and the reasoning behind the minority position. You preserve the cognitive tension that makes discussion effective, but at scale and without a facilitator.
The trap in all of this is concluding that discussion means facilitation = a person in a room billing hours. That is the expensive read, and it is the one that gets the idea rejected. The design problem worth solving is how to manufacture the conversation without needing to be in it.
Basarkod, G., Cahill, L. S., Burston, A., Barnett, D., Mahoney, J., Griffith, S., Swaryandini, G., Bradshaw, E. L., Devine, E. K., Akhtaruzzaman, M., Dicke, T., Wilks, M., Slattery, P., Saeri, A. K., Grundy, E. A. C., Lonsdale, C., Marsh, H. W., Chambers, S. K., & Noetel, M. (2026). Educational interventions are effective in improving students’ ethical and moral outcomes: A systematic review and meta-analysis. Nature Human Behaviour. Advance online publication. https://doi.org/10.1038/s41562-026-02456-x
Every year, a significant amount of money is spent on bringing firefighters from all over the country to train in specialised learning facilities of the Australian Rescue Firefighters Services (ARFFS) in Melbourne. With Airservices Australia revising their structure and budget, one of the initiatives I was lucky to participate in was an augmented reality training programme, developed mostly by our in-house coding guru, Ray.
All the risks, none of the danger.
Whenever your training needs involve a situation that is either very expensive or dangerous, extended reality (XR) training is a strong candidate solution, e.g., virtual or augmented reality. In our case, it was both expensive and dangerous. Setting aeroplanes on fire or exploding engines is not something the organisation would look forward to – especially training new staff on a regular basis.
Enter Augmented Reality (AR) simulations.
With training built in a 3D environment, you get to set any number of aeroplanes on fire, explode any engines, load any aircraft model in the simulation, etc. The other and better alternative is learning how to safely extinguish the fire, not let the engine explode and save the virtual people, which is your ultimate goal.
Why does it work so well?
There are several ways to answer that question. Besides the obvious cost savings and health safety, learners engage more and learn better and faster than with any other medium.
→ A cool explanation (based on research, neuroscience and psychology): Work by Mel Slater and Maria V. Sanchez‑Vives and others shows the brain does not fully distinguish between real and virtual bodies when sensory cues are aligned. This supports the idea that the “thinking brain” (prefrontal, reflective systems) can know it is simulated, but subcortical and sensory systems (amygdala, insula, somatosensory cortex) still respond as if it were real, driving emotion, arousal, and encoding into memory.
→ (In English, please) That means, even though you consciously know the training is a fake simulation, your sensory brain still gets you the full experience. On well-designed trainings, you are there! The situation must be addressed. And if you succeed, you will not only learn but also remember what you had to do and how it made you feel.
And the stats keep coming, showing XR works well.
A PwC study on VR soft‑skills training found VR learners were up to 4 times faster to train than classroom learners, more emotionally connected to content, and reported 275% higher confidence in applying skills.
Case reports from fire and safety training providers using XR show higher motivation, repeated voluntary practice, and better recall of life‑saving procedures compared with traditional fire safety instruction.
We’ve all been there: another round of feedback, a set of minuscule changes, the deadline looming… In learning, as with any industry, the pursuit of perfection often becomes a double-edged sword. While “perfect is the enemy of good” might sound cliché, it’s a mantra worth remembering. How do I know that? I was guilty of this sin.
Remember, the goal is to create effective learning that delivers real results. Sometimes, that means launching a module that’s at 90% rather than endlessly pursuing that elusive 100%.
The hidden costs of perfectionism
Let’s talk about the elephant in the room: over-testing.
How many times have you found yourself in an endless cycle of reviews, where each stakeholder adds their layer of scrutiny? What started as a straightforward module becomes bogged down in multiple rounds of testing, each applying increasingly strict criteria that weren’t part of the original scope.
Consider this scenario:
Your team has developed a compliance training module. The content is solid, the interactions are engaging, and the learning objectives are met. Yet, the project is three weeks behind schedule because:
Legal wants another review of every screen
The compliance team has new scenarios to add
The brand team needs all colours to be exactly 2% darker
Someone spotted a full stop that should be a semicolon
Sound familiar?
Striking the Right Balance
1. Define “Good Enough” Early
Work with stakeholders to establish clear acceptance criteria at the project’s outset. Document:
Essential compliance requirements
Minimum technical specifications
Core learning outcomes
Acceptable quality thresholds
2. Implement a Staged Review Process
Rather than waiting for everything to be perfect:
Conduct early prototype reviews
Use rapid development cycles
Get stakeholder sign-off on content before visual design
Lock down feedback stages with clear deadlines
3. Focus on Learning Impact
Ask yourself:
Will this change significantly improve learning outcomes?
Is this feedback addressing a genuine learning need?
Could this time be better spent on other aspects of the project?
Ask the team:
Are we testing the right things?
Does this feedback cycle add value?
What’s the cost of delay vs. the benefit of changes?
How will learners benefit from these revisions?
4. Adopt Agile Principles
Even in traditional waterfall environments, you can:
Release minimum viable modules
Gather learner feedback early
Plan for post-launch improvements
Track and measure actual usage patterns
5. Build Quality into the Process
Instead of endless testing:
Create robust design templates
Develop style guides and standards
Use automated quality checks where possible
Implement peer review systems
For Learning Designers
Set Clear Boundaries
Establish feedback deadlines
Limit review rounds
Document scope changes
Communicate the impact on timelines (Do it!)
Prioritise Feedback
Critical → affects learning outcomes or compliance, e.g. accuracy
Important → impacts user experience
Nice-to-have → aesthetic preferences
Document Trade-offs. When pushing back on perfectionism, highlight:
Budget implications
Timeline impacts
Opportunity costs
Learner benefits
Focus on Continuous Improvement
Plan for version updates
Track user feedback
Monitor completion rates
Measure actual performance impact
Quality in learning design is about impact.
By establishing clear standards, implementing efficient processes, and maintaining focus on learner outcomes, we can create high-quality solutions without falling into the perfectionism trap.
The next time you find yourself in the endless review cycle, remember: sometimes good enough is better than perfect, especially when “perfect” means missing deadlines, exceeding budgets, or losing sight of what matters: helping people learn effectively.
Just recently, I finished a course on LinkedIn Learning on responsible AI (RAI), and the role it plays in the workplace. This triggered me to look further, so today I’m exploring some thoughts on the impact this could have on the market, on jobs, and the learning design industry.
We know the increasing integration of AI into the global economy is transforming the job market, creating new opportunities while simultaneously displacing some traditional roles.
This shift alone calls for upskilling and retraining the workforce, especially leaders.
This trend holds significant implications for the learning industry.
AI is driving change at an unprecedented pace: The use of generative AI has almost doubled in the last six months, with 75% of global knowledge workers currently using it, highlighting the urgency for employees to acquire or improve their AI skills.
Leaders recognise the importance of AI but struggle with implementation: While 79% of leaders believe AI adoption is crucial for competitiveness, 59% are concerned about quantifying AI’s productivity gains, leading to implementation delays.
AI is not just for technical roles: Non-technical professionals like project managers, architects, and administrative assistants are increasingly seeking AI skills. Myself included.
So, we have this discrepancy between leaders agreeing GenAI can increase both the quality and the speed of work, yet have no idea on how to measure the gains. Fearing being left behind, employees want to use AI at work, and they won’t wait on leaders or organisations to catch up.
Increased demand for AI training: With 76% of professionals believing AI skills are necessary for career competitiveness, the demand for training on AI tools like ChatGPT, Claude and Copilot will continue to rise. This presents a significant opportunity for learning designers to develop and deliver targeted training programs.
Shift in skills emphasis: AI is good at automating routine tasks, yet uniquely human skills like management, relationship building, negotiation, and critical thinking will become more valuable. Learning should adapt programs to focus on cultivating these essential skills.
Emergence of new roles: The rapid evolution of AI is leading to the creation of new roles like “Head of AI”, a position that has tripled in the past five years. (LinkedIn). Learning designers will play a crucial role in defining the skills and knowledge required for these emerging positions and designing training to prepare the workforce.
Looking ahead, I see a lot of work for learning to do. Think governance, compliance, fairness, digital resilience etc. Likely, another section in the Code of Conduct and Cyber Security by next year. That is, without mentioning any new training on tools, procedures, and likely roles that haven’t been invented yet.
What can you do to stay ahead?
Embrace experimentation: Actively explore different AI tools and applications. This hands-on experience will provide valuable insights for designing effective learning programs. I’ve been using pretty much every major large language model (LLM) since they became publicly available and find great value in doing so even when they fail in providing a decent answer—at times, miserably. But that’s the soul of learning, right?
Develop AI aptitude: Invest in upskilling yourself on AI tools and technologies. Leverage resources like LinkedIn Learning courses, which have seen a 160% increase in usage among non-technical professionals. As a learning experience designer (LXD), I find copywriting an unsung superpower in this field. To endure the time constraints and the drive for quality products, I have no trouble turning to AI and leveraging their capability to generate ideas in seconds. From there, I can tailor it to my audience and needs, making the content my own.
Focus on the “why” and “how”: Help learners understand the strategic value of AI for their roles and the organisation as a whole. Develop training that goes beyond basic functionality and focuses on practical application, demonstrating how AI can drive growth, manage costs, and improve customer value. For instance, many GenAI apps will own the rights to what they produce; other times, they will unitedly yield IP-protected content, as if they were just made for you. Note, there are great apps for plagiarism too.
Promote a culture of continuous learning: Encourage ongoing AI skill development within organisations. Design learning programs that are flexible and modular. With the speed of this technologies, I believe many of us will have to reinvent ourselves in shorter periods of time
Stay informed: Keep abreast of the latest trends and developments in the AI landscape.
Understanding the implications of AI’s impact on employment and proactively adapting to the evolving needs of the workforce will empower us to play a crucial role in ensuring individuals and organisations thrive in an AI-powered world.
References:
“AI at Work Is Here. Now Comes the Hard Part“, Microsoft, 8/05/2024
OECD AI Principles overview – Adopted in May 2019, they set standards for AI that are practical and flexible enough to stand the test of time.
The Reality of Responsible AI – Jeanne Kwong Bickford, Katharina Hefter, Steven Mills, and Tad Roselund, BCG
The AI Index report, 7th ed. 2024, Measuring trends in AI
Technology Trust Ethics Preparing the workforce for ethical, responsible, and trustworthy AI: C-suite perspectives (Deloitte)
Cisco Principles for Responsible Artificial Intelligence
McKinsey Quantum Black AI: Responsible AI (RAI) Principles
