Experience Alone Does Not Guarantee Learning

Image by Gerd Altmann from Pixabay

Over the past few days, I have been mulling over a very simple concept: learning doesn’t automatically result from experience. But John Dewey (1933) said it better:

"We do not learn from experience. We learn from reflecting on experience."

John Dewey believed that our experiences shape us, and when reflective practice is part of learning, meaning and relevancy is created, which initiates growth and change (Dewey, 1933). 

So, the process of learning is really about reflecting on experience and the amount and level of learning depends on how a person processes their experiences. Two people may go through the same experience but walk away with very different levels of understanding or skill development based on how they engage with that experience, their mindset, or even their prior knowledge.

Same Experience; Different Learning

Think of two employees in a company who are tasked with leading a project for the first time. One employee approaches project management as a series of tasks to be completed and focuses on the outcome. They gain new knowledge about project management and gather some experience in managing people and projects. 

The project is delivered on time and on budget. At the end of the project, they move to the next one.

The other employee also approaches project management as a series of tasks to be completed but focuses on both the outcome and the process. They spend time thinking about the challenges they are facing. They seek feedback from seniors and assess what might work better in their project context. They think about their role as the manager and their leadership and communication skills. 

The project is delivered on time and on budget. At the end of the project, they review the project noting both successes and failures. They ask themselves questions like: How did my leadership style impact the team’s performance? How could I have handled scope changes better?, etc.

If we assume that external conditions such as the organizational settings, deliverables, deadlines, team composition, challenges, etc. were the same - then in both cases, the employees had the same experience - that of managing a project. But the second employee likely learned more actively by reflecting on what they were learning. Their learning was embedded throughout the experience. And in the process, they developed new skills. 

Experience alone doesn’t guarantee learning—what matters is how individuals engage with and reflect upon their experiences. 

In the context of Recognition of Prior Learning (RPL) and Prior Learning and Recognition (PLAR), this difference is crucial. 

Why This Difference Matters in RPL/PLAR

• RPL/PLAR is not about simply acknowledging that someone has spent time in a particular job or role. Just because someone has worked in a particular role for years doesn’t automatically mean they have developed the competencies that can be recognized. 
  
  
• RPL/PLAR is about assessing the specific knowledge, skills, and competencies that a person has gained from those experiences and determining whether these align with the required standards. Even if someone has had many relevant experiences, the learning must align with specific workplace or professional competency standards and the learning must match the competencies or outcomes defined by professional bodies, regulatory standards, or academic programs.
  
  
• RPL/PLAR focuses on assessing what individuals have learned from their experiences, not just having had the experience itself. It’s the demonstration of this learning through evidence such as reflections, portfolios, assessments, or practical tasks—that proves their competencies. 

Canadian Association for Prior Learning and Assessment (CAPLA) states this as:

"The Golden Rule of PLAR is credit for learning, not experience.”

The Scottish Credit and Qualifications Framework (SCQF) Handbook describes this as:

"It is the content, currency and amount of learning that a learner has which is subject to recognition, not the experiences themselves (these are simply the medium through which learning is acquired)."

In CAEL’S 10 Standards for Assessing Prior Learning, the first standard states that:
"Credit or competencies are awarded only for evidence of learning, not for experience or time spent."

All of this to say that the same experience can lead to different levels of learning depending on how actively one engages with the process. Therefore, learning differs from person to person. 

By emphasizing learning over experience, RPL/PLAR ensures that individuals are assessed on their demonstrated skills and knowledge, making the process a meaningful validation of their competencies.

That’s why it’s called Recognition of Prior Learning, not Recognition of Prior Experience—because it’s the learning, not the mere fact of having had an experience, that truly counts.

Work Fingerprints: The Human Touch in an AI World

Image by Gerd Altmann from Pixabay

A few days ago, I read the work being done towards creating a standard for Canada called the “Accessible and Equitable Artificial Intelligence Systems.” Among other things, the proposed framework highlights how: 

"We must speak up to make sure AI addresses the voices of those on the edges, for whom the “typical” will never work. Without intervention, we risk AI creating a kind of amplified, fractal echo-chamber, advancing homogenization and being capable of enacting more discrimination at a faster rate." 

And then, over on LinkedIn, Michelle Ockers, shared a reflection on her use of genAI. She acknowledged how it can help her be more efficient, but shared her concern about feeling guilty about using it, especially when others claim they've created something without the help of AI.  

I’ve been pondering similar concerns about AI and the human-AI loop. It is inevitable that we will all increasingly use more of AI and in many different ways. But doing so rapidly, and in the absence of specific and usable frameworks, standards, laws, and protection, is concerning. In many ways, we have to embrace how it is becoming an extension of our own skills. That's perhaps why I prefer calling it Augmented Intelligence rather than Artificial Intelligence. 

Personally, when using AI to support my work, I am hyper aware about how AI can favour/ compute/ recommend the "default" or the "standard" and potentially overshadow my individual perspective. For me, AI is a tool, not the product. So, I consciously and intently use and infuse my voice into my work - the voice that is shaped by my unique human experiences. 

I call it my "work fingerprint" and people who work with me look for it, recognize it, and value it. 

My work fingerprint is made of everything that I am made of - my experiences, perspectives, ideas, insights, creativity, lessons learned, fears, and concerns but also all other aspects of intersectionality that I bring to the table including my nature, nurture, education, background, ethnicity, family, friends, immigration journey and community service. 

I'd like to believe that my work fingerprint is unique to me or at least is distinctive enough that it reflects a certain way of thinking, problem-solving and decision-making. So, I want to do everything to preserve it and nurture it - now more than ever - and especially while augmenting my intelligence with AI tools. I want to retain and preserve the human touch in an AI world.