By Matthew Esterman and Dr Phil Lambert
There’s an elephant in the classroom. It has been there for quite some time, but it is now even more obvious—particularly as the solution to this age-old problem is staring us all in the face, largely thanks to a digital upgrade.
The “elephant”
The elephant refers to what we know about learners, learning and teaching in the complex context and composition of students in any class group—and what has been the incapability of schooling systems and schools to appropriately support the learning progress of each student. In other words, it is the gap between what our students need, and what we currently offer them.
Learners
We know that from the very start of schooling, there are significant variations in the capabilities of children across cognitive, physical, behavioural, social and emotional domains. At the extremes, this can mean a child with the vocabulary and characteristics of a toddler is in the same class group as a child already reading their second Harry Potter novel.
While the range within a class may not always be this stark, the challenge of meeting such disparate needs is often described by teachers—and in the research literature—as daunting, if not impossible, given the constraints of time, resources and curriculum demands.
As observed by Masters (2013):
“In any given year of school the most advanced learners…can be at least five and six years ahead of the least advanced learners.” (p. 3)
This gap—the elephant in the classroom—follows students from year to year, grade to grade.
Learning
We also know that to broaden and deepen knowledge, skills and dispositions, teachers must pitch content at just the right level. It must connect with prior learning in order for growth to occur.
If the pitch is too high—beyond the student’s current ability—gaps widen as content becomes obscure, complex and abstract. Literacy and numeracy are prime examples where this mismatch has lasting consequences across all subject areas. For many students, this leads to disengagement, frustration, and falling further behind.
If the pitch is too low—revisiting material already mastered—students quickly become bored and disengaged. School loses relevance, limits potential, and learning in class pales in comparison to what is engaging and accessible outside school.
It follows that sustained instructional methodologies, whether explicit or otherwise, are far more effective when targeted at individual levels of need rather than at a “one size fits all” whole-class approach.
Yet as things stand, the majority of students simply find ways to cope, while others quietly or overtly disengage—either dropping out completely, or just going through the motions. As Anderson and Winthrop (2025) put it, many become “passengers” rather than active, enthusiastic learners. They survive on memorisation and replication rather than genuine understanding.
Teaching
To even begin addressing such a wide range of learning needs, teachers must be highly skilled, well-resourced, and given the flexibility and professional agency to adapt the curriculum.
Most teachers care deeply about their students’ progress and wellbeing, but they are frustrated by the “elephant” in the room. The call for personalised learning, tailored to individual needs, has influenced policy internationally since the early 2000s, beginning in the UK health sector and later spreading to education policy in the UK, USA and Australia (Lambert, 2022).
As the UK Department for Education and Skills (2004) promoted:
“Personalised learning is an idea that is capturing the imagination of teachers, children and young people across the country. It has its roots in the best practices of the teaching profession, and it has the potential to make every young person’s learning experience stretching, creative, fun and successful.”
But strong policy rhetoric has not always translated into classroom practice. Courcier (2007) reported teachers found personalised learning “very difficult to implement…although the idea is very good” (p. 78). Nearly a decade later, Maguire et al. (2013) found no evidence of personalised learning in practice in case study schools.
The reality is that even with strong policy backing, teachers remain constrained by highly standardised, industrialised schooling models that leave little room for true personalisation.
The solution
Teachers continue to do the best they can, but the elephant remains. As Goss et al. (2015) note:
“Despite heroic efforts by many teachers, our most advanced students are not adequately stretched while our least advanced are not properly supported. Many fall behind over time.” (p. 1)
Improving teacher training is important but not sufficient. The real breakthrough lies in the tools now at our fingertips. Generative artificial intelligence (AI), already transforming many industries, offers schools and teachers a way to tailor learning and assessment to individual students’ needs.
This does not mean replacing teachers. Rather, AI can serve as an ally—helping teachers design personalised lesson plans, differentiated activities, and formative assessments that would once have been impossible to prepare at scale. Teachers remain the facilitators, mentors and human connection in learning.
Those already experimenting with AI describe it as a “sparring partner for the mind,” a patient tutor, and a provocateur to deepen thinking. Frontier teachers are exploring ways to radically personalise learning experiences that were unimaginable just a few years ago.
Instead of asking AI for a single lesson plan, teachers can request thirty versions tailored to each student’s learning journey. They can embed specific physical, cognitive or emotional needs, and receive creative, targeted activities alongside aligned assessment tasks.
This is only the beginning. Some teachers are now asking: how might AI act as a genuine classroom assistant? When should it step in to support learning, and when should it deliberately step back? These are the professional questions that will shape the next phase of education.
Avoiding the elephant in the classroom is no longer an option. The schools that find the right balance—leveraging AI while maintaining teacher and student agency—will thrive. Those that cannot or will not adapt risk being replaced by those that can.
References
Anderson, J. & Winthrop, R. (2025). The disengaged teen: Helping kids learn better, feel better and live better. Crown Publishing, New York, USA.
Aranha, R. (2025). Teacher perspectives on contemporary challenges to differentiated instruction: A case study. Educational Planning, 32(2).
Courcier, I. (2007). Teachers’ perceptions of personalised learning. Research in Education, 20(2), 59–80.
Goss, P., Hunter, J., Romanes, D. & Parsonage, H. (2015). Targeted teaching: How better use of data can improve student learning. Grattan Institute, Carlton, Victoria.
Lambert, P. (2022). The knowing and caring profession. Austin Macauley, London.
Langelaan, B., Gaikhorst, L., Smets, W. & Oostadam, R. (2024). Differentiating instruction: Understanding the key elements for successful teacher preparation and development. Teaching and Teacher Education, 140, 1–14.
Maguire, M., Ball, S. & Braun, A. (2013). What ever happened to…? ‘Personalised learning’ as a case of policy dissipation. Journal of Education Policy, 28(3), 322–338.
Masters, G. (2013). Towards a growth mindset in assessment. Australian Council of Education Research (ACER), Camberwell, Victoria.
Schwab, S. & Woltran, F. (2023). Obstacles to differentiated instruction (DI): Reviewing factors outside the classroom that contribute to successful DI implementation. In V. Letzel-Alt & M. Pozas (Eds.), Differentiated instruction around the world: A global inclusive insight (pp. 103–114). Waxmann Verlag, Germany.
UK Department for Education and Skills. (2004). A national conversation about personalised learning. London, UK.