AI: to do things better, or to do better things?
The growth of AI in education can be viewed as part of a broader philosophical debate about the purpose of education itself.
The 2024 report by the US Department of Education guiding school leaders on the implementation of artificial intelligence offers a deceptively simple but deeply profound piece of advice: strike a balance between doing things better (productivity) and doing better things (transformation). This guidance captures a tension in education’s engagement with technology and automation.
On the surface, the message seems pragmatic. AI promises the potential to alleviate long-standing pressures in schools: excessive teacher workload, time-consuming administrative tasks, and the emotional toll of constant accountability. Yet, beneath this lies a more complex question about the very purpose of technological innovation in education. Should AI merely make the system run more efficiently, or should it open up possibilities for richer, more human learning?
This dichotomy sits within a long-standing philosophical debate about the role of tools in education. Automation can make existing processes smoother and faster, but transformation requires something more: a reconsideration of what counts as good learning. In many contemporary education systems influenced by the global education reform movement, “better education” is equated with higher performance on standardised tests and more measurable outputs. Within that paradigm, improvement becomes synonymous with optimisation, not deepened connection or understanding.
The challenge, then, lies in how educational leaders can embrace AI without being drawn into the gravitational pull of performance metrics. A truly transformative approach to technology would mean using the time and insight that automation affords to cultivate relationships, foster inquiry, and create learning experiences that honour the human dimensions of education. But expecting individual school leaders to sustain such a balance without system-wide support is, in practice, a tall order.
Schools do not operate in a vacuum. Across many nations, including the UK and the US, education has become increasingly marketised through mechanisms such as league tables, academisation, and charter schools. These reforms, often justified under the umbrella of public-private partnership and new public management, have imposed quasi-market dynamics that reward competition rather than collaboration. In such an environment, the institutional incentive is often to deploy technology to boost quantifiable performance rather than to enrich the life of the school community.
This is not to say that the balance advised by the report is misplaced. Rather, it highlights the need for a broader conception of partnerships and governance. If AI is to serve both productivity and transformation, then policymakers, school leaders, and technology partners must align around a shared vision that values human flourishing alongside organisational efficiency.
Educational leadership, in this sense, becomes a collective endeavour situated within a framework of trust and systemic coherence. The future of AI in education should not be defined merely by how well it enhances data analytics or administrative precision, but by whether it enables schools to become more humane, relational, and reflective spaces. Balancing doing things better with doing better things must therefore be seen not solely as a leadership ambition, but as a societal commitment.