From Assessment to AI: Are We Rediscovering the Expert Learner?

In this reflective piece, Christine revisits the original concept of the Expert Learner and explores how its principles connect to best practice in today’s post-16 education. She covers a range of topics, from formative assessment and learner agency to AI-enabled, sustainable learning.

Back in 2008, the Quality Improvement Agency (QIA) launched a programme called Developing the Expert Learner. Its ideas were simple but powerful: when learners understand how they learn, take ownership of their progress and engage in meaningful learning conversations, outcomes improve, not just in achievement, but in confidence, curiosity and motivation.

Those ideas still resonate deeply today. Yet the landscape has changed. The post-16 education system is now navigating AI, digital transformation and an increasingly diverse learner population. So what might it look like to revisit the concept of the expert learner in an AI-enabled and sustainability-focused world?

Reframing Assessment for Learning in the Age of AI 

The Developing the Expert Learner resource described assessment for learning as “the process of seeking and interpreting evidence that helps teachers assess progress and helps learners to monitor themselves.” That definition remains timeless. What has evolved is how evidence is gathered, interpreted and acted upon.

As the Assessment Reform Group (2002) first highlighted, formative assessment is most powerful when learners understand where they are going, where they are now, and how to close the gap. Two decades later, the Education Endowment Foundation (2023) continues to identify metacognition and self-regulation as among the most cost-effective strategies for improving learning outcomes. These principles remain central to how we think about adaptive teaching and personalised learning.

Where once we relied on observation, discussion and written feedback alone, we now have access to digital tools that can capture and interpret learning data in real time. AI-powered platforms can prompt reflection, suggest next steps and even analyse patterns in learner responses to identify misconceptions early.

But the principle hasn’t changed. Assessment for learning was never about data for data’s sake, it was about dialogue, ownership and growth. The same applies to AI in education: its value lies not in automation, but in amplification. It gives us more time and insight to focus on what really matters, helping learners to think, question and apply knowledge with confidence.

This rebalancing of technology and reflection also helps us learn sustainably, developing habits of thinking that reduce wasteful repetition, target effort where it has the most impact, and build self-reliance over dependency.

The “Expert Learner” as Co-Pilot in the Learning Journey 

The original QIA framework described expert learners as self-regulating, reflective and proactive individuals who understand what good learning looks like and how to achieve it. They are curious, analytical and capable of giving and receiving feedback constructively.

In 2025, we might describe this as AI-supported metacognition. AI tools such as Copilot and ChatGPT can help learners to plan, review and self-assess, mirroring the kind of reflective dialogue once only possible in face-to-face sessions. For example, a learner could ask an AI tool to help identify which parts of their assignment most clearly demonstrate a criterion; or to generate questions that test their own understanding before submission.

The teacher’s role remains pivotal. It’s the teacher who designs the framework for reflection, models how to critique constructively and interprets feedback meaningfully. But the learner becomes an active partner in that process, a co-pilot, not a passenger.

Decades of evidence support this shift. John Hattie’s Visible Learning research (2009; 2023) places self-assessment, feedback and metacognitive regulation among the most influential factors on achievement. Dylan Wiliam reminds us that formative assessment works best when feedback is something done with learners, not to them, an idea that aligns seamlessly with AI-supported learning partnerships.

This partnership approach mirrors the wider sustainability principle of shared responsibility: the idea that change happens through co-agency, when both teacher and learner take accountability for progress and impact.

How Feedback and Scaffolding Can Build An Expert Learner

Lev Vygotsky’s (1978) concept of the Zone of Proximal Development reminds us that learners thrive when challenge and support are balanced, when tasks stretch their thinking just beyond what they can do independently, but not so far that they experience failure or anxiety.

Contemporary interpretations, from Sweller’s Cognitive Load Theory (1988) to Rosenshine’s Principles of Instruction (2012), echo this same message: learning sticks when it’s well sequenced, scaffolded and revisited. These are the foundations of effective curriculum design and they’re just as relevant to AI-assisted teaching as to traditional delivery.

AI tools can extend this “zone” by offering adaptive feedback, personalised practice and scaffolded steps that meet learners exactly where they are.

A well-designed AI-supported curriculum can guide learners through complex tasks in small, confidence-building increments, which is particularly valuable for adults returning to learning, or for those with Maths, English or Digital Skills anxiety.

But technology alone doesn’t make learning inclusive. What does is intentional design, thoughtful sequencing, responsive teaching, and a culture of feedback that values progress over perfection. When we combine those elements, we nurture learners who are not only skilled but self-aware learners who know how to learn, adapt and apply their knowledge in new contexts.

Those are also the habits that make learning sustainable. When learners understand how to transfer their skills across subjects, jobs and life stages, their learning continues to pay dividends long after the course has ended.

The Expert Learner and Sustainable Learning for a Sustainable Future

If we want learners to build a sustainable future, we must also teach them to learn sustainably.

That means fostering habits of reflection, curiosity and critical thinking that can evolve over a lifetime. The expert learner doesn’t just acquire skills, they understand how those skills connect to real-world challenges: budgeting, reducing waste, analysing data or using digital tools responsibly.

When we teach learners to think systemically, to see the relationship between cause and effect, effort and impact, we develop not just academic capability, but social responsibility.
This is sustainability in its truest sense: learning that empowers people to make better choices for themselves, their communities and their planet.

UNESCO’s Education for Sustainable Development (ESD) framework (2020) calls for learning that equips people with “knowledge, skills, values and attitudes to act for the future of our planet.” In this sense, developing the expert learner is also an act of sustainability, empowering individuals to keep learning, adapting and contributing to social and environmental wellbeing.

As we prepare for initiatives such as Numeracy for a Sustainable Tomorrow, it’s worth remembering that sustainable thinking starts with mindset, in how we teach, how we assess, and how we empower learners to own their progress.

Why Rediscovering the Expert Learner Matters Now

As providers prepare for the new agenda for the Post 16 sector under Skills England and the growing focus on lifelong learning, the call for “expert learners” feels newly urgent. The future workforce will need adaptability, critical thinking and digital confidence, all underpinned by solid literacy, numeracy and problem-solving skills.

Recent reports from the Learning and Work Institute (2024) and the Edge Foundation (2024) emphasise that adaptability, critical thinking and digital confidence are now core employability outcomes essential to both economic productivity and social inclusion.

Revisiting the principles of assessment for learning and expert learner development offers a framework for achieving this.

By integrating AI ethically and purposefully and embedding sustainability as both context and outcome, we can move from assessment for learning to AI for learning, using intelligent tools not to replace professional judgement, but to extend it.

This is what powerful pedagogy looks like in practice: evidence-informed, inclusive and forward-looking.

And it starts with a single question, not “What do my learners know?” but “How do they learn best, and “How can I help them to know that?”

Reflective Prompt: Encouraging the Expert Learner Mindset

What could you do next week to help your learners become more expert in how they learn, not just what they learn?

Try building a short reflection into your next session:

• Ask learners to identify one strategy that helped them make progress.
• Discuss how they might use it elsewhere.
• If you use AI tools, invite them to ask the AI how else that strategy could apply to their goals.
• Link it to sustainability by asking: How might this way of learning support you to make better decisions: at work, at home, or for the planet?

That’s the start of creating expert learners: confident, conscious and capable of building a sustainable tomorrow.