When Producing the Thing Is No Longer Proof of Competence

Harvard psychologist and education professor Howard Gardner recently shared his view on how AI could change education. He believes cognitive tasks will become optional for humans, freeing people to pursue their interests. The actual interview between Rick Hess and Howard Gardner is certainly more nuanced than just the statement and makes for an interesting read. 

https://www.edweek.org/technology/opinion-howard-gardner-how-ai-will-force-us-to-rethink-education/2026/08

While I can't say I agree with his bold statement of cognitive tasks becoming optional for humans, what is true is that when all our lives, humans have defined their intelligence based on cognitive tasks, and then, AI takes over some of those cognitive tasks, we are suddenly left thinking more deeply about what we thought was exclusive to us... our intelligence!

But I think the real leap in this discussion is to stop making these lists of "things human can do that AI can't" (because, realistically, that list is shrinking by the hour). The more important task is to start questioning systems that were designed with the assumption that producing something is the way to demonstrate intelligence. I mean systems of education, training, assessment in school and at work that rely on writing an essay, submitting a portfolio, completing a presentation slide deck, making a report, etc. 

In L&D, we have always done this. We use course completion, knowledge tests, work samples and portfolios as evidence that learning has occurred or that someone is ready to perform. But we know all too well that completing training isn't the same as being able to perform at work, just like producing the right answer isn't necessarily evidence of understanding. 

The artefact was never the actual competency yet we believed for so long that it was. But when the artefact can be produced by AI, it becomes an even weaker proxy for learning, intelligence, and competence.

Having worked in the areas of competency development and assessment for close to 3 decades, I know too well that competence is messy. It involves recognizing what is happening, deciding what matters, and applying information in various contexts. Then there is exercising judgment, adapting when circumstances change and taking responsibility for the outcome. The artifact may provide useful evidence of those things, but it isn't 'the thing' itself. 

The artefact as the evidence of intelligence is losing ground quickly. Especially given the recent development of AI watermarking, where Anthropic has begun watermarking text generated by Claude so that the content can be machine-identified as AI-generated.

So, where does the value of expertise sit when AI can produce so much of the work we have traditionally been paid to produce? We are all realizing what was always true but hidden in plain sight that expertise is beyond outputs and is generally more upstream. 

In my field of instructional design, this is becoming increasingly obvious. AI can produce storyboards, graphics, video, narration, activities and assessments. It can turn a reasonably well-defined brief into a fairly complete learning product very quickly. 

What is the value of an instructional designer? I don't think the answer is simply "in knowing how to use AI." I think it is increasingly in everything that happens before we ask AI to produce something.

I am glad I work with clients and teams who get this. I am currently working on an organization-wide performance improvement initiative where we are taking an end-to-end approach to needs analysis, looking carefully at the system rather than responding to a request for training. We are examining the work, people, processes, tools, expectations, resources and environment, and working through the findings collaboratively. We are so focused on not hurrying towards the output and it is taking some discipline and intention to do it this way. I mean, when we can produce learning in minutes, it is rather tempting to start producing! But the more capable the tool, the more important is the role of the human to spend time figuring out what actually needs to be produced.

Because AI makes the “doing” easier, the value of human capability is more upstream in knowing what to ask, what to pursue and why it matters.

Perhaps we need to get much better at assessing the things that are harder to produce on demand and are certainly harder to outsource to AI. 

Perhaps we have to redefine what now constitutes human intelligence and what makes us competent. 

Perhaps it is about things like understanding the context, tapping into the lived experience, human judgment, and being responsible and accountable for our choices and actions. 

This binary view of AI vs. Human just won't cut it anymore. This isn't as simple as AI on one side and human expertise on the other. The opportunity staring at us is in knowing how to combine the two. The shift from producing answers to identifying what problems to solve is the real discussion. Not the how but the why. 

But it was always about the why, wasn't it? 

I Had Fun. But Am I Ready to Do It Again?

Author's First Attempt at Lino Cut Printing

Storytime! This past weekend, I took a beginner-level, three-hour lino cut printing workshop.

The instructor was an experienced printmaker who shared some beautiful examples of her work and gave a brief demonstration before we got started. 

Our project? A reduction lino print.

If you know anything about lino printing, you will know that a reduction print is not exactly the simplest place to begin. Of course, when I started the workshop, I did not know this :) 

News flash! I had never seen or used lino blocks, carving tools, or basic printing equipment before. But it was a beginner-level workshop, right? We were asked to draw something simple on paper, transfer it to the lino block, and then carve four different layers that would allow for four different colours to be printed. I was not sure what I was getting into...but I had fun, and I got some multi-colour prints to prove it!

The lino print workshop certainly made for an experience. I discovered how awkward it feels to hold the carving tools, how tough it is to carve tiny details or curvy lines, how easy it is to remove too much material, but how satisfying it is to pull one decent-looking print while attempting to have 10+ options! The workshop created good conditions for exploration, and I became familiar with some things. 

For a beginner's workshop, it had plenty of challenges. Fifteen of us were all trying to create our first print, and one instructor was trying to walk around the room, respond to our questions (if we asked), troubleshoot problems (individually), and keep everyone moving. I loved being challenged. And as a trainer and facilitator, I know and appreciate how struggle is often where learning happens. But as much as I loved the challenge, I was also frustrated in the moments between the struggles. 

When my print did not turn out as expected, I wanted to understand why. Was the pressure wrong? Was there too much or too little ink? Was the roller not coated well or did it have too much ink? Was my carving technique the problem? Was my paper placement incorrect? Did I not use the right tip to carve the lino? I couldn't figure out what all was going wrong! I had too many questions! 

After the workshop, I found myself asking myself, What did I actually learn? And more importantly, could I do it again on my own? What would I intentionally do differently next time?

I can't say that I didn't learn anything! I learned plenty. But it was not the kind of learning that would allow me to independently tackle another lino cut print tomorrow. 

My mistakes taught me that carving lino requires patience, planning, and a very different way of thinking about creating an image. After I saw the prints, I realized I had to think about positive and negative spaces before I even started carving. I also learned the hard way that mistakes are easy to make and impossible to undo once you have carved the lino :)

Soon enough, the instructional designer and trainer in me took over. I left asking myself what if, before I did anything with a lino block, I had received some orientation around some big ideas about lino cutting and printing? Perhaps, even provided with a simpler drawing for beginners? Or instead of starting with a reduction linocut print, what if we started with a basic, single-layer carving print and then moved to multiple layers and multiple colours?

The questions made me think about how sometimes it is easy to confuse an interactive and fun experience with learning, especially for beginners. 

As someone who works in learning and development, I suspect this resonates with a broader issue we have all seen in workplace learning. 

We often celebrate fun and interaction and plan and design for it. At the end of the workshop, we ask whether participants enjoyed it, whether they were involved, and whether they stayed interested. Yes, fun counts. However…

Having an interactive and fun experience is not the same as learning in a way that transfers to future performance. 

At the end of the workshop, I was left thinking about what knowledge, skills, or insights I will carry into my next attempt. What principles have I extracted from the experience that I can apply? I know that my questions were getting at learning transfer, not just participation. 

Because...

Learning isn't simply about what happened during an activity; it's what happens after it, or rather what changes because of it. 

Moving the Needle with RPL: How the language of RPL is a Strategic Lever

Published on the occasion of the first CAPLA International Day of Recognition, 09 June 2026

"Moving the RPL Needle" by Taruna Goel, Slide 13, CAPLA Conference Presentation 

For years, I believed that if I could just explain RPL clearly enough, the process, the rigour, the evidence requirements, everyone would see its value. I was wrong. Not about RPL's value but about what "clearly" actually means to different people.

The VU Meter Problem

The expression "move the needle" comes from analog VU meters used in sound recording. If the sound was too quiet, the needle simply didn't move, nothing was registered even though recording was technically happening. The sound had to be loud enough, and in the right frequency range, for it to register.

Across Canada and around the world, we are doing high-quality, rigorous, impactful recognition work. But if the language we use doesn't resonate with the systems and key players we are speaking to, the needle may not move. Our message won't be loud enough to register. And when nothing is registered, even important work becomes invisible. 

One Practice, Different Conversations

One of the occupational hazards of working in RPL is that we become very attached to our own terminology :) Terms like prior learning assessment, portfolio review, challenge process, and credit assessment all make sense to those of us who live in this field. But these terms describe the mechanism, not the value. And people don't engage with mechanisms, they engage with outcomes that align with their needs.

After nearly three decades working at the intersection of adult learning, competency recognition, and workforce development, I have come to understand that the RPL ecosystem has several distinct audiences. While there are genuine areas of overlap, most players care about quality, credibility, and outcomes, each one is primarily listening through the lens of their own priorities and needs. 

• Learners and Candidates are not asking "How rigorous is the assessment?" They are asking: "Does my experience count? Can I skip what I already know? Will this help me get where I'm going faster?" Candidates are listening for signals that their existing knowledge, skills, and experience are recognized and valued. They use emotionally charged language, recognition, validation, confidence, opportunity, belonging. They talk about being seen. What resonates most with this group is language about progress and momentum, not process and protocol. When we lead with evidence requirements or assessment procedures, we may be communicating accurately but we are not communicating value from the learner's perspective. 
  
  
• Post-Secondary Institutions are listening through a lens of quality assurance and academic integrity. When RPL is framed as an exception, it creates resistance. When it is framed as a structured assessment process that measures achievement of the same learning outcomes expected of all students, just through a different pathway, the conversation shifts from risk to rigour. The question they are really asking is: "How do we maintain standards while creating flexible pathways?"
  
  
• Employers and Industry generally don't wake up thinking about RPL. They think about workforce capability. Words like credit equivalency and advanced standing carry little weight in an industry context. What resonates is time-to-competence, reduced retraining, and confidence that skills have been verified. The employer question is: "How quickly can this person contribute, and how confident can we be in their competency?" I have seen employer engagement increase dramatically when RPL is described as a way to confirm what workers already know and can do rather than as an academic exercise.
  
  
• Government and Regulatory Bodies operate at a systems level. They are not primarily focused on individual advancement or organizational productivity. Their concern is whether the recognition system is fair, consistent, transparent, and defensible and how it impacts the labour market and economic growth. RPL gains traction at this level when it is framed as a system-level enabler: improving workforce responsiveness, supporting labour mobility, advancing equity, and increasing the efficiency of public training investments. The question they are asking is: "Can this system be trusted to produce consistent, defensible outcomes?"

We Are Translators of Value

Here is the insight that has most changed how I approach this work. We are not actually changing the RPL practice from one audience to another, we are translating its value proposition. The assessment methodology, the evidence standards, the quality frameworks, these remain the same. What shifts is the language we use to make that value visible to each player.

When we rely on one-size-fits-all RPL language, we almost always miss somebody. It can sound too academic in workforce contexts, too operational in academic ones, and too abstract in policy conversations. This doesn't mean the practice lacks value. It means its value hasn't been translated.

Most successful RPL initiatives are not necessarily those with the most sophisticated assessment tools or the most comprehensive policies. They are often the ones that have become intentionally fluent in the right language for each audience. 

They know that (mostly):

• Learners are listening for opportunity and progress
• Institutions are listening for academic integrity and learning pathways
• Employers are listening for skills validation and job readiness
• Regulators are listening for standards and defensibility

When we communicate through those different lenses, the conversation shifts from explaining the process to demonstrating the value.

To become an effective RPL Practitioner, we have to become translators of value, not just administrators of process.

Final Thoughts

As I was preparing this session for CAPLA's first International Day of Recognition, I found myself sitting with a question and I want to leave it with you too. 

If your RPL practice disappeared tomorrow, who would notice? Would employers notice? Would learners? Would institutions? Would policymakers?

And perhaps more importantly, would they have the language to explain what went missing?

As skills shortages deepen, career pathways become less linear, and learning increasingly happens outside formal educational settings, the need for trusted recognition systems will only grow.

The opportunity before us is not simply to improve our RPL assessment practices. It is to build recognition systems that are understood, trusted, and valued across the entire ecosystem. And that starts with using the right language.

Taruna Goel is an RPL Strategist with North Pacific Metrics in Vancouver, Canada, and a recognized expert in competency assessment, prior learning recognition, and workforce credentialing. This article is adapted from her session presented at CAPLA's inaugural International Day of Recognition on June 09, 2026.

Connect on LinkedIn: linkedin.com/in/tarunagoel

#RPL #PLAR #RecognitionOfPriorLearning #CAPLA #SkillsRecognition #LifelongLearning #WorkforceDevelopment #InternationalDayOfRecognition