Writing That Matters

Handwritten notes from a conference attended by the author. 
The messy, human beginning of a published blog post.

I have been writing my blog since 2008 and with over 200 posts, my writing process is sacrosanct to me. Writing is not only one of my hobbies, it is also essentially something that contributes deeply to my sense of self. It is a core activity that helps me make sense of my experiences, my work, and my reflections. I value the process of writing as much as the output.

Even though my blog posts may appear finished, I have developed an internal capacity to make space for ambiguity where I am still making space for unresolved thoughts. There is also an evolution of sorts or a continuity of sense-making over the last two decades.

The process of writing has given me many opportunities to reflect on my biases, assumptions, contradictions and values. For me writing is not purely for information, it is also for critique, judgement, reflection and growth.

Writing is meaningful for me because I enjoy working through the ambiguity and unresolved thoughts. I am okay with sitting with the unfinished and very appreciative of the wrestle that gets me to the finish line.

I do wonder about how a lot of writing is being produced today, what kind of writing still requires a human, what kind of writing still matters. 

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"Writing that matters will be writing that can still function as evidence of human deliberation." - James O’Sullivan 

More: https://psyche.co/ideas/well-soon-find-out-what-is-truly-special-about-human-writing

"Writing isn’t just the production of sentences – it’s the training of endurance by way of sustained attention. It’s a way of learning what one thinks by attempting to say it. An LLM can reproduce the appearance of that activity, but it can’t replace it, because the value lies not only in the object produced but in the transformation that occurs during its making." - Micah Nathan, an MIT writing professor on his students using AI 

More: https://www.theguardian.com/us-news/ng-interactive/2026/may/10/fiction-writing-professor-ai

"Cognitive ownership lives in the process of making the thing, in the wrestle that happens before the piece is finished. It is what you accumulate when you struggle with a sentence, choose the wrong word, change your mind, and end up somewhere you did not expect to be. It is the residue of having been there. You can produce a polished piece of work without it, and you can produce a flawed piece of work that is saturated with it, and the difference between the two is invisible until someone asks you a question and you have to answer from inside the thinking." - Yen Anderson on AI-assisted work and the ownership we are losing without noticing

More: https://yenanderson.substack.com/p/the-author-was-absent

CredX2026: Making Recognition a Verb

Voices from CredX2026 - Word Cloud by Taruna Goel

Over the last two weeks, I have been reflecting on my experience at CredX2026 and trying to make sense of it all in terms of what was said, what I heard, what we discussed, and what needs to be done next.

A core part of my work sits at the intersection of systems that do not always speak the same language, yet are all working toward shared outcomes around competence, credentials, and workforce mobility. I am talking about industry, post-secondary, and government. CredX2026 brought all of these systems and people into the same room. 

As we intermingled, it made me realize how the challenge is not just about translating across these systems. It is about building trust between them. 

As Chad Doerksen from the Ministry of Post-Secondary Education and Future Skills shared at our table, “credentials are a proxy for trust.” If that is true, then Recognition of Prior Learning (RPL) is not simply an assessment mechanism or an alternative pathway to credentials. It is a way of making trust visible, portable, defensible, and valuable. 

Which is why Julie Klein’s framing of “recognition as a verb” stayed with me. It shifts RPL from being a static system or policy into something active, relational, and ongoing, It is something we do constantly and intentionally, not something we design once and implement.

As I listened across sessions and conversations, I was struck by how much innovation already exists in the system. Jako Olivier from the Commonwealth of Learning presented the microcredential framework already working across the commonwealth but also reminded us that "while we call them microcredentials, there is nothing micro about the work", especially when situated within a broader lifelong learning context. Robert Luke from eCampus Ontario pointed to the growing importance of making skills visible through tools like Skills Finder and LMI-enabled curriculum stacks. Susan Forseille from Thompson Rivers University highlighted both the scale of PLAR in practice and future-facing ideas such as a provincial credit bank, along with important work on decolonizing and Indigenizing assessment methodologies. Sonia Hall from the Ministry of Social Development and Poverty Reduction shared BC’s current initiatives, policies, and regional innovations in credentialing. 

And yet, alongside all this progress, there was a quiet but persistent tension. Jackie Pichette from RBC Thought Leadership captured it well: “Change often starts at the margins. The problem is when it stays there.” RPL, microcredentials, and work-integrated learning are no longer fringe ideas, and yet they are still not fully embedded in how systems operate. As Jeremy McQuigge noted, it is time we reclaim these invisible systems. Because the challenge now is less about innovation and more about integration.

That tension becomes even sharper when viewed through current labour market realities. Tricia Williams, Future Skills framed the challenge within the realities of mid‑career disruption due to automation. Jeff Griffiths, leading the Alberta Talent Pipeline Management Initiative, noted that the system itself has changed, that the lack of entry-level jobs is a structural issue and how "we cannot credential our way out of it". At the same time, Rob Goehring from AI in BC observed, how skills are decaying faster than degrees are being updated. Nan Travers from Credential As You Go added another layer, reminding us that the quality and value of a credential are experienced differently by learners and institutions, and that while "AI may help us achieve efficiencies, we must remain intentional about effectiveness". 

In that context, RPL begins to look less like an alternative pathway and more like essential infrastructure. A big part of that infrastructure is the "language of competencies" and the value of  engaging industry extensively, not peripherally as highlighted by Dan McFaull. Margo Griffiths spoke about how behind every trusted credential is the data standard, governance and interoperability infrastructure. But the infrastructure here is not just technical; it is deeply relational. Jennifer Beale and Katie Fitzmaurice from Invest Vancouver spoke about the importance of “relational infrastructure” and the role of "neutral conveners in connecting systems". Joanna Jagger from WORTH Association emphasized “community over credentials,” while Darion from Teqare and Lynn White from ACCESS highlighted the value of empowering communities through training, achievement and self-sufficiency. Jodi Tavares from MyCreds Network reminded us that a "healthy ecosystem is connected, not competitive, and must remain focused on the mobility of the credential holder". 

These are not just ideas; they are conditions required for trust to exist and scale.

Throughout the two days, I found myself returning to the book, Thinking, Fast and Slow. Much of the world around us is pushing us into fast, reactive thinking. We are reacting to AI, economic shifts, and global uncertainty. But CredX2026 created space for something different. It provide space to think about and share ideas for a slower, more deliberate, and more collaborative response rather than simply another reaction. And that shift in pace felt important, because systems change does not happen at the speed of reaction; it happens at the speed of alignment.

Which brings me to what feels like the most important takeaway. If recognition is truly a verb, if it is about continuously validating and translating learning across contexts, then we need to normalize it. David Porter’s call to “make RPL normal” may sound simple, but it carries tremendous weight. It challenges us to move beyond pilots and pockets of innovation toward something more embedded and systemic. We need to understand that RPL is not an exception or a shortcut. Recognition is simply about creating pathways that meet people where they are, not where the system expects them to start. 

In a system where skills are changing faster than credentials, and where industry, post-secondary, and government are all working toward shared outcomes, RPL is no longer an “alternative pathway."

This feels like a real opportunity particularly here in British Columbia for us to move from conversation to coordination. We are doing important work across sectors, but too often in parallel.  Gregory W. Stone from BC Colleges noted how opportunities exist to re-vision and re-purpose what we have and how the most important question  is "What is going to happen next?"

Here's my call to action that I want to advocate for, support and be a part of:

The good? Industry, post-secondary, and government are all solving the same problem.

The what can be better? We just are not solving it together yet.

There is space for a cross-system RPL Community of Practice that brings together employers, post-secondary institutions, government, and other intermediaries (neutral conveners) for ongoing dialogue grounded in practice. So, not just to participate in conferences to share ideas, but to work together to build a shared language, toolkits and guidelines, competency frameworks and recognition-based credentialing models. 

I am imagining a kind of a living network of practitioners and partners, of people and institutions, who are actively engaged in this work so that the connections transcend beyond LinkedIn and conferences like CredX into real work, pilots, shared projects and cross-system collaboration. That is what will help move credentials, recognition and validation from the margins to the centre. 

Thanks to all the speakers, and to Tannis Morgan Adrian Lipsett, Erin, Hal and team, BCcampus Tracy Roberts Britt Dzioba, M.Ed. Helena Prins Gwen Nguyen Kelsey Kilbey, Future Skills Centre and the rest of the #CredX2026 team.

CredX2026 helped frame this important work and I hope we can move beyond episodic conversations and toward something more sustained.

I went into CredX2026 thinking about credentials. I left thinking about recognition and trust. 

Recognition is not just a process. Not just a noun. It is a verb. Recognition is how trust moves across systems.

Lifelong Unlearning

I was talking to a colleague the other day and we were discussing how our formal systems always assume that learning is additive. We are always talking about more courses, more skills, more knowledge. But in practice, growth also comes from subtracting. Anyone who has worked hard to drop a bad habit knows how tough it is to let go. Whether it is a habit, our assumptions and biases, or even poor work practices and ways of working, "unlearning" is hard to do but has nothing to show. There is no concrete evidence of unlearning or any artifacts or portfolio to show what and how much we have unlearned. We don't give out certificates for what people have stopped doing!

Unlearning is subtle, but it is powerful. After unlearning my fair share, I know that it has made space for better judgment, greater adaptability, and more thoughtful practice.


I wrote about this previously, in 2012, in my blog post titled:
Emptying Your Cup: Unlearning to Learn.

The point I am making is that not all growth comes from learning something new. Sometimes, it comes from letting something go. And we have to be intentional about it.

What did you let go of recently that contributed to your growth? How do you encourage others to unlearn? How do we design "learning" systems that also enable and support unlearning?

Thoughts?

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Additional reading:

• Chapter 15: Learning, relearning and unlearning
  
  November 2024 Open Books and Proceedings
  DOI:10.38140/obp1-2024-15
  License CC BY 4.0
  Authors:
  Rosemary Akinyi Menya-Olendo
  Lucy Mawang
  Kenyatta University

https://www.researchgate.net/publication/387466020_Chapter_15_Learning_relearning_and_unlearning

In this chapter, the authors describe the following key terms:

Learning is contextualised as the continuous ability to acquire competencies relevant for the 21st century. This form of learning enables people to continuously improve their performance, expand their horizons, and equip themselves for the future. It further allows individuals to grow and develop personally and professionally throughout their lifespan. Turk (2023) observes that this type of learning occurs in various contexts, such as formal education, informal learning, and experiential learning. 

Unlearning means leaving behind old, outdated, and obsolete knowledge that is deemed inefficient in ad-dressing current challenges. It therefore entails questioning one’s assumptions and beliefs, and opening up to new perspectives that can help solve present problems. No wonder Turk (2023) postulates that the unlearning process may be challenging, as it necessitates confronting personal biases and preconceptions.

Relearning is the process of learning something again, often in a new or different way. It involves building on previous knowledge and experiences to gain a deeper understanding of a subject or skill. Relearning is important because it allows individuals to update their knowledge and skills in response to new information and changing circumstances (Turk, 2023).

• It turns out that unlearning isn't just a human challenge. AI and LLMs are wrestling with the same problem. A paper submitted to ICLR 2026, Lifelong Unlearning for Multimodal Large Language Models (Li et al.), tackles the problem of teaching AI systems to selectively forget outdated or problematic information. 

"Multimodal large language models (MLLMs) are trained on massive multimodal data, making data unlearning increasingly important as data owners may request the removal of specific content. In practice, these requests often arrive sequentially over time, creating the problem of MLLM Lifelong Unlearning."