The Intentional Learning Framework
Specification Papers
Your system was built to manage learners.
It's time it helped them learn.
Seven papers. More than 500 pages. A decade of research, encoded so your team and the AI in your product can build learning that works by design, not by accident.
Expert-grounded, for the people building and choosing learning technology.
Your system was never built to help people learn.
Now AI is filling that gap.
Think about what a learning management system does by default. It enrols, tracks, records and reports. Every one of those things is done to a learner, or for them. None of them is the same as helping someone actually learn.
That gap has always been there, and good learning design quietly falls through it. Not every educator is a learning designer, yet most are asked to do design work that was never their craft, with no time to do it, so they fill the gaps with guesswork and good practice fails to make it into the room.
Into that gap step the quick-fix vendors, selling shiny tools that look like good learning design and aren't. And now AI fills the same gap, faster and at scale, with a polished surface that hides what's missing. Point it at "build the course" and it won't tell you where its expertise ends. Used poorly, it scales the weaknesses we already had.
The cost isn't abstract. Learners disengage and quietly leave, often before the teaching even starts. Educators carry the remediation. And an organisation that runs a tool built on none of this inherits the weakness at scale, with its name on it.
In my own doctoral research, this gap was observed to be universal. 100% of educators reported a gap between the practice they believe in and the practice they manage to deliver. The barrier is rarely will. It's time, workload, and a craft that shifts with every context, more than any one educator can be expected to hold. And often it's the system itself, built to manage learners rather than help teach them, that they work around rather than work with.
Get everything behind the Intentional Learning Framework, made buildable
The Framework is the map. These papers are everything behind it: a decade of hands-on research with real educators and adult learners, across vocational education, higher education and workplace training, turned into the tested strategies, exemplars, pitfalls and expert-grounded prompts that actually move outcomes.
Seven papers, more than 500 pages, written so your build team and your AI can work from them directly. Not a stack of academic reading to interpret, but the research and the why behind it.
The point is to reason from good practice, not imitate it. A system that imitates good practice reproduces its surface. It looks right. It sounds like a good educator. But point it at a situation nobody scripted, and it has nothing underneath to draw on. A system that reasons from good practice can make the right call in a moment the manual never named. That distinction is the whole product.
And good design does not mean keeping everyone on the screen. Sometimes the best thing a system built on good learning design can do is send the learner away from the system itself: trigger the pen-and-paper retrieval task, prompt the practice that has to happen offline, then get out of the way.
These papers specify good practice wherever it belongs, in the system and out of it. A tool that measures its success by screen time has already misunderstood the job.
Not the soft option. The measurable one.
"Better learning design" can sound soft, like something nice to have when there's time. So here are some numbers. Every strategy in the Framework goes through trials, control groups and qualitative validation before it earns its place.
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One strategy raised first-unit completion from 53% to 84%.
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One authentic rewrite of a single assessment question lifted correct responses from 17% to 88%, same learners, same day. In focus groups, learners rated the rewrite as harder, not easier.
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One strategy drove resubmissions down an average of 72% across nine sites.
Every one of those numbers is also a cost. Resubmissions are staff hours. Non-completions are lost funding and lost outcomes. A learner who can't show what they know is a qualification that means less than it should.
That second result is the one to sit with, because it's the thing the quick-fix tools get backwards. Good design is not the same as 'easy'. The rewritten question tested as more demanding, and it unlocked capability the original wording had suppressed. This is productive friction: the right difficulty in the right place. A tool that chases engagement by stripping out effort is optimising for the wrong thing.
These are examples, not a scoreboard. Good learning design isn't the soft option. It's the measurable one, and it's exactly the judgement the papers encode.
From enrolment to completion, not just the teaching moments, not just the learning content
Most learning design obsesses over the teaching and the assessment, as though those are the only parts that count.
But by the time a learner reaches the actual teaching, you've often already lost the ones who couldn't find the course, never quite started, or drifted away in the first week. The drop-off happens long before the part everyone designs for.
When you hand that whole journey to a system, or the AI inside it, it needs to understand every stage, not just the two in the middle. Otherwise it optimises the teaching and quietly loses everyone who never got that far.
The Intentional Foundations paper sits underneath all of it: the principles every stage is built on, including how a system decides what to build, what to help an educator do well, and what stays human craft it can only point toward.
Then the six stages:
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Explore +Enrol. They find you, weigh it up, and decide it's worth their time.
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Welcome +Orient. They arrive, feel genuinely welcomed, and find their feet.
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Roadmap +Expectations. They know where they're going, what's expected, and why it matters.
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Active Learning. They do the work, build the skills, and stay engaged.
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Assessment +Feedback. They show what they can do, and learn from how they went.
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Closure +Next Steps. They finish well, and know what comes next.
Seven papers in all: the six stages, plus Foundations. Each stands on its own, and each is sharper for the ones around it.
Built to be built from
Every paper follows the same spine, so your team always knows where to look:
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What the research shows, and the why beneath it, so a decision can be reasoned, not guessed.
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The tested strategies, the concrete changes that move the numbers.
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Common pitfalls, named directly, including the ones AI systems reliably fall into.
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What good looks like, written to the team doing the building.
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Exemplars and templates, fresh and genericised, never lifted from real-world applications.
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Expert-grounded AI prompts, built as components to run inside your product under human oversight, not text for a chat window. Review prompts return a clear pass or flag and an overall call, and never quietly rewrite the work.
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A dual checklist: a quick test your team can apply, and discrete rules your system can be built on.
That last item is one artifact serving two readers at once. The same checklist is a build spec for the team making a system, and an evaluation test for the organisation choosing one.
For technology teams and AI developers
You can build the product. What you can't reverse-engineer is a decade of sector-specific learning research. And from a demo, a buyer often can't tell whether your AI reasons from real expertise or only imitates its surface, which is why buyers and regulators are increasingly asking suppliers to show the expertise behind their outputs. A tool that looks useful but undermines learning is a risk you don't want surfacing after the sale.
The papers give you components your product works from every time: the why, the strategies, the pitfalls, what good looks like, and prompts built as platform building blocks. They also give your buyers a standard to evaluate you against, which is a stronger place to stand than hoping they don't ask. Licensed for your build.
Be the product with the expertise behind the surface, not the shiny one that only looks like it.
For education providers
AI is already in your teaching and assessment, and the systems you run were built to manage learners, not to help them learn. You're accountable for whether what you adopt is good for learning, and "it came from a supplier" was never a defense here.
Adopt the wrong tool and the harm lands on your learners (and your name).
The papers give you an independent standard for what good learning design looks like, so you can hold any tool or AI product against it before you let it near your learners, and so your own system steers your educators toward good practice instead of leaving it to chance.
However your design capability is set up (no dedicated designers, too few, or a whole team not yet working from one playbook) the system holds every build to that same standard, so good practice is consistent across every course, not just the ones a specialist had time to touch.
Durable because it's technology-independent
The papers specify what a system must do, and why, never how to build it in any particular product. They name capability and behaviour, not tools, vendors or features. That's why they age as your stack evolves, and it's the same discipline the papers ask of you: decide what good learning looks like first, then choose the technology to serve it. Never the reverse. We practise what we specify.
AI is arriving in your system whether you plan for it or not. The day it does, it needs to know what 'good' looks like, or it will scale what does not. AI scales whatever you give it. Give it good practice. This is the layer you install before the AI, not after.
Access is perpetual. What you license, you keep.
Research you can build on
You're trusting these papers to shape what your product or your organisation does with learners, so it's fair to ask who stands behind them.
Dr Deniese Cox holds a PhD in online education and a Masters in learning and development, is an award-winning researcher, and is a top-rated presenter with an independently verified rating of 4.8 out of 5 across hundreds of participant reviews.
She has also been a university lecturer (teaching online pedagogy at bachelors level, and strategic learning design at masters level), a VET trainer, and a workplace educator, so the research is grounded in also knowing first-hand what it's like to juggle those roles and still fight for the best outcomes for learners.
I do the hands-on research, so your team doesn't have to.
These papers make AI good for learning.
There's a companion for making it defensible.
The Specification Papers are one half of adopting AI you can stand behind: the half that makes it genuinely good for learning. The other half is making it defensible inside a heavily regulated sector, and that's the work of the wider Expert-Grounded AI initiative, run alongside Phill Bevan.
Through EGAI you can also reach:
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The EGAI Adoption Pack. A 100+ page expert reference that turns the tangle of privacy and education-sector obligations into plain answers, updated as regulations evolve. Plus ready-to-use governance instruments, a register, approval trails, risk and privacy assessments, supplier checks and human-oversight controls, that turn your intent into a record.
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EGAI Intelligence Packs. The AI-facing layer: education-specific limits, escalation rules and quality checks that keep high-stakes decisions under human control and leave a trail you can follow.
Informing the human. Powering the AI.
Find the right fit
The papers are licensed by organisation type and scale, with separate tiers for technology providers and education providers. Tell us where you sit and what you're building or running, and we'll find the right fit.
Build the system your learners needed all along: one that helps them learn, not just one that manages them.