Beyond Artificial Intelligence: Making AI "Care" Better?

Throwback Thursday: A year ago, I wrote about “work fingerprints”, the unique blend of skills, experiences, and insights that define us as humans in what is becoming a world that is increasingly being shaped by AI.

 
Fast-forward to today: As a part of my morning reading ritual, I took a deep dive into some recent articles and discussions on what makes us human and what the pathways are to enhance human connection.

Some ideas that stood out for me were:

"The challenge is designing AI systems that enhance human interaction rather than erode it."

"True intelligence isn’t the ability to generate information. True intelligence is the ability to generate emotion. The future of AI won't be about how fast it computes — but how deeply it connects."

"If, as we anticipate, human goals and preferences become increasingly co-constructed through interaction with AI systems, rather than arising separately from them, then AI safety requires paying as much attention to the psychology of human-AI relationships as the wider societal factors and technical methods of alignment. We now highlight core ingredients of this emergent psychological ecosystem: humans as social animals and AI systems as increasingly capable social agents."

This conversation has only become more urgent. I don't think the real risk of AI is replacement. I think it is the erasure of our unique fingerprints. It is the things that each of us leaves behind as breadcrumbs that highlight the mix of who we are through our choices, decisions, and connections with other humans.

In my work designing RPL pathways and competency-based frameworks, I have seen how often human potential goes unrecognized; not because it is missing, but because 'systems' overlook the stories behind it.

Given that, how do we ensure that technology enriches, not erodes, our fingerprints and human connections? If the next revolution is emotional, not technical, how do we make sure we are building a human-first AI ecosystem that doesn't overlook the human?

Well, the ‘godfather of AI’, Geoffrey Hinton, presented an intriguing solution in an interview with CNN where he suggested we build "maternal instincts” into AI models, so “they really care about people”. He goes on to say:

“Evolution obviously made a pretty good job with mothers. AI developers have been focusing on making these things more intelligent. But intelligence is just one part of a being. We need to make them have empathy towards us. And we don’t know how to do that yet. But evolution managed and we should be able to do it too.”

So, the challenge ahead is not (just) about making AI think better, but to help it "care" better?

https://www.hec.edu/en/knowledge/articles/ai-divide-human-not-technological

https://www.forbes.com/sites/dangardner-1/2025/05/12/the-next-ai-revolution-isnt-technical-its-emotional/

https://www.forbes.com/sites/pialauritzen/2025/08/14/geoffrey-hinton-says-ai-needs-maternal-instincts-heres-what-it-takes/

https://edition.cnn.com/2025/08/13/tech/ai-geoffrey-hinton

#WorkFingerprints #FingerprintsInAnAIWorld #HumanFirstAI #MotherAI #EmotionalIntelligence #DigitalEthics #AIEthics

Recognition of Prior Learning (RPL/PLAR) is Opening Doors for Many

One of the most common misconceptions I often hear about Recognition of Prior Learning (RPL) or Prior Learning Assessment and Recognition (PLAR) is that it's a shortcut. That it's the easy way out that lowers the standards.

But it's not.

Ask RPL Advisors and Mentors about the kind of investment that's required to help applicants understand the competency standards and collect and document evidence that maps meaningfully to those standards.

Ask successful RPL/PLAR graduates about the kind of time and effort (and emotions) it takes to prepare for a structured RPL Assessment.

Ask Assessors how much training and mindset-shifting it takes to move from checking boxes to 'assuming competence first', and then doing assessments with fairness and intention.

And ask the RPL experts and consultants, like me, who work behind the scenes with subject matter experts, industry committees, sector advisory groups and regulators to build the competency frameworks and assessment tools, develop RPL requirements and procedures, and ensure that the process and tools are valid and defensible. It takes months of consultation, alignment, and consensus-building across the industry to make sure the bar is high, transparent, and trusted.

None of it is easy. 

In fact, sometimes "RPL-ing" a requirement can take longer and it often demands more reflection than attending a formal education program.

RPL doesn’t lower the standards bar, it simply focuses on various ways that people can demonstrate that they meet the standards.

Instead of forcing everyone through the same gates in the same sequence, RPL opens multiple doors by recognizing that learning happens in diverse and deeply valuable ways on the job, in life, through community, through life challenges. None of the doors are shortcuts but they do offer diversity, inclusion, fairness, relevance, and rigour.

Image by Arek Socha from Pixabay

RPL/PLAR is all about creating pathways that meet people where they are, not where the system expects them to start. 

The bar stays high. The route and assessment becomes more human-centred.


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Recognition of Prior Learning (RPL) is known by many names in different countries. See: Wikipedia.
For more information on RPL/PLAR in Canada: CAPLA - https://capla.ca/

The Canadian Association for Prior Learning Assessment (CAPLA) is the national
voice for the recognition of prior learning (RPL) in Canada. CAPLA is a
Membership-based Association that offers learning and professional development
resources for RPL/PLAR practitioners.


#RPL #PLAR #VPL #CPL #RecognitionFirst #HumanPotential #LearningAndDevelopment #LifelongLearning #WorkplaceLearning #AlternatePathways

Reclaiming the 'Learning' in RPL/PLAR

Thank you to the Canadian Association for Prior Learning Assessment (CAPLA), the Community of Learning (CoL) team and CanCred.ca 🇨🇦 for the kind recognition following my recent session:

"Reclaiming the 'L' in RPL/PLAR – Why Experience Alone Isn’t Enough."

https://passport.cancred.ca/app/badge/info/33457

It was meaningful to revisit the core principles and foundations of RPL/PLAR and focus on what lies at its heart: the recognition of real, reflective learning, not just accumulated experience. 

I set out to peel back the layers of RPL and my session covered: 

- The key differences between experience and learning

- Three common “experience traps” we fall into

- The consequences of mistaking experience for learning for equity, quality, and growth

- And my practical, best practices for surfacing learning that is often hidden, informal, or unspoken

It was especially rewarding to see meaningful conversations unfold in the breakout rooms from experienced practitioners and those just entering the field. Everyone shared generously and reminded me how powerful any community can be when we stay grounded in principles that matter.

hashtag#RPL hashtag#PLAR hashtag#VPL hashtag#Recognition hashtag#PriorLearningAndAssessment hashtag#RecognitionOfPriorLearning hashtag#LearningOverExperience hashtag#ReclaimingLearning hashtag#CAPLA hashtag#CAPLACOL hashtag#Community hashtag#RPLPractice

You can find the event content including my Presentation deck and the session recording in the Badge details. https://passport.cancred.ca/app/badge/info/33457

Recognition Before Reskilling

There’s a theory, "tabula rasa", which says that we are born as blank slates. That knowledge is only gained through experience and that we start from nothing and build up from there.

But is that really true?

As newborns, we instinctively root, grasp, and respond to voices. We are born with reflexes, rhythms and preferences. There is knowledge already encoded in our bodies that is passed through our DNA. New research also suggests that the knowledge of our culture, survival, pain and trauma is perhaps also stored in our cells.

And yet… in most of our learning and talent development systems, we still treat people as if they are starting from scratch. Before we acknowledge their expertise, we create plans to train, reskill, upskill, and make people 'fit' for the job.

- What if we stopped assuming blank slates and asking people to start from zero and, instead, recognized the wealth of learning and skills they bring to the table, from day one? What if Bootcamp Day 1 is all about recognition of learning and validation of skills?

- What kind of conversations can we have in our teams, organizations and industry sectors, that can allow us to acknowledge, recognize and validate this prior learning and skills and meet people where they are?

- How can we use a strength-based lens and recognition-first mindset? What can help us reframe our thinking and our work?

The answers? I hope you will find some in the following podcast! 

"Recognition before reskilling." - I hope that's the message you will get from listening to the latest episode of the TIDES Podcast. I am so grateful to Shveta Malhan for inviting me as a guest, for asking such thoughtful and pragmatic questions and for creating the space for such an important conversation.

I have been working in the areas of learning and recognition for over 25 years. But what I have found is that we often rush to retrain, reskill, or retool without pausing to ask, what do our people already know. What do we value and recognize and therefore, what are we failing to see, name, and value?

In a world chasing efficiency and automation, I am all about scaling our humanity :) So, if you're curious about how we can use recognition of prior learning as a mindset to center human fingerprints in AI-powered workplaces, give this episode a listen.

Listen to the episode here https://lnkd.in/gudtsw83

I'd love to hear your thoughts on how a recognition-first mindset has helped surface skills in your work and how recognizing lived learning can change lives.

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Shveta's Original Posting on LinkedIn: https://www.linkedin.com/posts/shvetamalhan_tidespodcast-recognitionofpriorlearning-humanizingai

Canadian Association for Prior Learning Assessment

hashtag#RecognitionOfPriorLearning hashtag#RPL hashtag#PLAR hashtag#PLA hashtag#APL hashtag#RCC hashtag#PriorLearningAsssessmentandRecognition hashtag#HumanizingLearning hashtag#RecognitionBeforeReskilling hashtag#WorkBasedRecognition hashtag#WorkFingerprints hashtag#VPL hashtag#AI hashtag#SkillsRecognition hashtag#WorkplaceLearning hashtag#HumanCenteredDesign

From Fear to Fluency: Rethinking AI in Post-Secondary Education

Image by Manfred Steger from Pixabay

I am writing this post as a follow-up to a recent panel discussion I participated in at the annual BEAC/BCCAT meeting. The Business Education Articulation Committee (BEAC) is part of the BC Council on Admissions and Transfer (BCCAT), bringing together educators to explore key issues in post-secondary education. The panel, titled “Getting Comfortable with AI,” was organized by BEAC Chair, Garima Kamboj and moderated by Vice-Chair, Jennifer Duffy.

I had the pleasure of sharing the virtual space with thoughtful colleagues Gwen Nguyen and Fuat Ramazanov as we discussed the evolving role of Artificial Intelligence (AI) in post-secondary teaching and learning.

Below are some of the key questions we explored and the reflections I shared during the session.

"If there was one mindset shift you would recommend to post-secondary educators about AI, what would it be?"

One Mindset Shift: From Answers to Questions

A video of Steve Jobs from 1983 recently shifted my own mindset. In it, he imagines a world where machines can hold and convey the spirit of thinkers like Aristotle allowing us to ask them questions even after they’re gone. That future is here. 

So, if there’s one mindset shift I’d recommend to educators, it’s this: Let’s stop treating AI as a shortcut to answers, and start seeing it as a way to deepen our questions.

The magic of education has never been about having the right answers. It’s about learning how to ask better ones. I don’t see AI as replacing educators. I see it redefining our role from content providers to social architects helping learners make meaningful connections with themselves, others, their work, and the world. In that world, AI becomes Augmented Intelligence, not Artificial Intelligence.

"Should academic integrity policies explicitly include guidance on AI use? If so, what might that look like in a way that is fair, inclusive, and educative?"

Academic Integrity: From Policing to Partnership

Yes, academic integrity policies should explicitly address AI. But not in a way that is rigid or punitive. Instead, I advocate for policies that are transparent, contextual, inclusive, and educative.

Here’s what that can look like:

- Make expectations visible. Clearly articulate when and how AI can be used and align AI use with course learning outcomes/objectives or the context of teaching.
- Prioritize disclosure over detection. Encourage statements like: “I used AI to brainstorm ideas” or “I checked clarity using a tool and revised it myself.” These aren’t confessions, they’re reflections.
- Avoid the binary. Move beyond “allowed vs. banned” and toward a continuum of use from “no AI” to “AI as co-creator.”
- Include learners. Involve students in setting norms. Ask them what responsible AI use looks like in your course context. Let policies be co-created, not just imposed.
- Model teaching with integrity rather than teaching for integrity. 

In my experience, people rise to clarity. Students grow when they understand not just what is expected, but why it matters.

"What does digital fluency around AI look like for both instructors and students?"

Digital Fluency: From Tool Use to Learning Agility

Digital fluency isn’t just about knowing how to use AI tools. It’s about knowing when, why, and to what extent to use them and when not to.

For instructors, this includes:

- Enhancing human agency in teaching and learning
- Developing judgment around the technical, ethical and pedagogical dimensions of AI
- Practicing with real-world cases of integrating AI, not just introductory demos of AI tools
- Leveraging AI for driving their own lifelong professional development

It is important that instructors and educators apply frameworks like UNESCO’s AI Competency Framework for Teachers into their teaching practice.

Educators don’t need to become AI experts. But we do need to become AI-literate facilitators and be  able to guide students in a landscape filled with both promise and peril.

For students, AI literacy must include:

- Learning to co-create with AI
- Critically evaluating outputs
- Practicing prompt engineering
- Understanding bias and ethical considerations

Both instructors and students need learning agility and develop their ability to adapt to rapidly evolving tools and systems. Our expectations must shift from mastery of a fixed set of AI tools to the ability to learn, unlearn, and relearn how we work with AI.

“What is one actionable step you recommend educators take next week to deepen their comfort with AI?” 

One Action to Take Next Week

I invite everyone to try a "Plus One" mindset. This is a concept I love from Universal Design for Learning.

Think of one challenge you or your students consistently face. Then ask: Can AI help reduce or remove this barrier?

For example, maybe students often struggle with understanding assignment instructions. This leads to confusion, low-quality submissions, or a flood of emails. Could you use AI to rewrite your instructions in plain language? Or perhaps your students have trouble grasping abstract concepts. Could AI help generate analogies, visuals, or real-world examples at different levels of complexity giving students multiple ways to connect with the content?

You don’t need to overhaul your teaching. Just take one meaningful step. That’s the heart of Plus One thinking.

Getting comfortable with AI isn’t a sprint, it’s a marathon. So, we have to keep building our stamina and strengthening our muscles. And like any good workout, it helps to run alongside others. Let's stay connected and keep the conversation going!

Resources:

UNESCO AI Literacy Competencies – An overview: https://www.unesco.org/en/articles/what-you-need-know-about-unescos-new-ai-competency-frameworks-students-and-teachers?hub=32618

UNESCO AI Literacy Competencies for Teachers: https://www.unesco.org/en/articles/ai-competency-framework-teachers

UNESCO AI Literacy Competencies for Students: https://unesdoc.unesco.org/ark:/48223/pf0000391105

GenAI in Teaching and Learning Toolkit for Educators: https://opentextbc.ca/teachingandlearningwithai/front-matter/introduction/