Last week, I asked: “Are we really stacking?” The response was immediate, thank you to everyone who joined the conversation. What emerged was a shared recognition that while microcredentials and short-form learning are booming, and the idea of “stackability” is gaining traction, our systems still assume learners will follow the paths we've laid out - not chart their own.
Today, learners collect skills from all directions, bootcamps, MOOCs, workplace training, community volunteering, short courses, and yet we expect them to present this as a neat, linear story. Most credentials still sit in silos, disconnected from each other, lacking context and recognition.
A few themes stood out:
Stackability needs direction - without constraint
Stuart Allan asked: “Stackable into what?” Many institutions assume the destination is a degree, but as Moises Salinas Fleitman and others reflected, learners are building their own maps. The Lego metaphor came up again and again. The question isn’t whether we’re giving people bricks. The question is whether we’re helping them build something that makes sense to them.
Maybe it’s time to focus on the connectors, the common sizing, the alignment frameworks, the guides that allow different types of learning to snap together. Not a fixed set of instructions, but a system that supports endless reconfiguration.
Systemic friction is real
Sasha Thackaberry-Voinovich highlighted how even within the same institution, internal governance and siloed systems make it difficult to stack credentials. Melanie Booth reminded us of the cultural undercurrent: “If I didn’t teach it, it doesn’t count.”
How do we shift a system that still sees education as a pyramid, with degrees at the top? That model works well for some. But not everyone needs, or should feel compelled, to reach the apex to be successful. I’ve always liked the phrase “just-in-time education,” but maybe “just enough education” captures what many learners actually need.
There are too many people with full degrees in jobs that don’t require them—and too many people with skills and partial qualifications who remain locked out because the system still preferences the macro credential.
Credentials need context
Several commenters, including Christie DeCarolis, noted that microcredentials often fail to reflect the person behind the achievement. What if learners could annotate their credentials or enrich them with personal reflections, artefacts, or feedback? What if the story mattered as much as the standard?
I heard a phrase at a conference last year that stuck with me: “I am more than my metadata.” I still believe in the critical importance of machine-readable metadata, but we also need space for learners to show up as more than a data profile.
Could AI help pull it all together?
AI’s role in education is moving well beyond content generation or personalised quizzes. It’s beginning to show real potential in stitching together learning pathways, especially where complexity, fragmentation, and scale have historically made this difficult. Some emerging use cases include:
Interpreting and aligning credentials to frameworks
AI can compare learning outcomes, standards, and competencies across jurisdictions and sectors in minutes, a task that used to take days or weeks. As a test, I recently asked ChatGPT to compare how “communication” is described in European, Australian, and US frameworks. The comparative summary was surprisingly detailed and immediate (I’ve included the output at the end of this article - note, has not been fact checked).
Helping learners narrate their stories
I’ve experimented with uploading my resume and LinkedIn profile into an AI tool and asking it to write speaker bios or help refresh my website copy (I know many have already done this, but if you haven’t it’s worth a try). It rewrote my profile and then offered to recommend jobs and learning pathways. While the results weren’t always accurate (apparently I’ve held a few jobs I don’t remember!), the reflective process was genuinely useful.
Translating experience into employer-recognised signals
Salesforce recently launched Career Connect, an internal AI-powered platform that helps employees build personalised career paths based on skills, not roles. It’s designed to uncover hidden talent and promote mobility by matching people to opportunities they might otherwise miss.
The Potholes in the Potential
If AI can help individuals design their own stack of credentials, tailored to their goals and grounded in evidence, the potential benefits are real. But so are the risks.
Access is not evenly distributed. Many of the learners who stand to benefit the most from personalised guidance often have inconsistent access to the internet, devices, or the digital literacy to navigate AI-powered systems. AI can also replicate existing biases. If the data used to train these systems is skewed, based on historical hiring patterns, for instance, then the recommendations may simply reinforce existing stereotypes.
Another concern is oversimplification. Reducing a learner to a "profile" or a set of inferred skills may miss the nuance of who they are, what they value, and what drives them. The richness of motivation, struggle, and growth can easily get lost in translation. Someone motivated by international career opportunities might take a very different career path than someone motivated by the desire to give back to their community, or someone motivated by environmental outcomes.
And of course, privacy. Who controls the data? Who has access to it? And how do we ensure it isn’t used to profile people and monitor them, all in the name of support?
What We Need to Do
Design for inclusion: access across devices, languages, and bandwidths.
Audit for bias: diverse data, regular testing, and correction and continually add new data on new frameworks and models.
Promote transparency: learners must know how AI is shaping their experience.
Keep humans in the loop: mentors, coaches, and educators remain essential.
Let learners override AI: support, not automation, should guide the journey, they should be able to validate and correct any assumptions or findings AI presents.
As AI continues to evolve, so will the ways it can support lifelong learning and learner agency. But if we want to build something learners can trust and use, we need to design it as if they are the architects, not just the passengers. I’d be fascinated to hear from others how you have used AI to support or narrate your lifelong learning.
Next week, I’ll be diving into how we design learning systems that recognise life’s twists and turns - where stepping away doesn’t mean starting over. From re-entry points to flexible recognition, we’ll explore what it takes to build a lifelong learning ecosystem that genuinely supports second chances.
And as promised above ChatGPT’s verbatim comparison of Communication across skills frameworks
Wendy said:
ChatGPT, please compare communication across European, Australian and US skills frameworks and list your sources
ChatGPT said:
Communication is a fundamental skill emphasized across various educational and professional frameworks globally. While the core principles remain consistent—such as effective speaking, listening, and writing—different regions integrate and prioritize these skills uniquely within their respective frameworks. Here's a comparative overview of how communication is represented in the European, Australian, and United States skills frameworks:
🇪🇺 European Frameworks
ESCO (European Skills, Competences, Qualifications and Occupations): The ESCO framework classifies communication as a transversal skill essential across occupations. It encompasses abilities like active listening, clear articulation, and effective interpersonal interactions, recognizing their importance in various job roles. Employment, Social Affairs and Inclusion+6Language selection+6EUR-Lex+6
Common European Framework of Reference for Languages (CEFR): CEFR provides a detailed schema for language proficiency, focusing on four modes of communication: reception (listening and reading), production (speaking and writing), interaction, and mediation. This framework is widely used to assess and develop language and communication skills across Europe. Portal
🇦🇺 Australian Frameworks
Australian Core Skills Framework (ACSF): The ACSF identifies "Oral Communication" as one of its five core skills, detailing performance levels from basic to advanced. It emphasizes the ability to listen, speak, and engage in effective dialogue across various contexts, including personal, community, and workplace settings. VOCEDplus+2Employment and Workplace Relations+2Intar+2
Victorian Essential Learning Standards (VELS): Although superseded by the Australian Curriculum, VELS highlighted communication as a key domain, focusing on listening, viewing, and presenting. It aimed to develop students' abilities to interpret and convey information effectively. Wikipedia
🇺🇸 United States Frameworks
Employability Skills Framework: Developed by the U.S. Department of Education, this framework categorizes communication under "Effective Relationships." It outlines skills such as active listening, clear articulation, and the ability to interpret and convey information effectively, emphasizing their importance in workplace settings. LINCS
Partnership for 21st Century Skills (P21): P21 identifies communication as one of the "Four Cs" essential for success in the 21st century, alongside critical thinking, collaboration, and creativity. It stresses the ability to articulate thoughts and ideas effectively using oral, written, and nonverbal communication skills in various contexts. Wikipedia
Comparative Summary
While communication is universally acknowledged as essential, the emphasis and implementation vary across regions. Europe focuses on multilingual proficiency and standardized descriptors, Australia emphasizes contextual oral communication skills, and the United States integrates communication within broader employability and 21st-century skill sets.