A recent report from The Walton Foundation in the US claims that teachers using AI can save up to “six weeks” of time over a school year. But what does “time saved” actually mean in education? And should we be basing conversations about technology on this obscure, often misleading metric?
The Walton Foundation report is an example of a constant through-line in discussions about AI in the workplace: how much more efficient can we make our daily processes? Education is, of course, not immune to discussions of efficiency, but these conversations can be particularly harmful in a sector where human relationships – not time saved – should be our focus.
I won’t go into the report in this article, but for a very thorough reading you should definitely check out Marc Watkin’s recent post Who Owns the AI Dividend? Watkins writes:
I’m worried too. I have written before about AI and teacher workload, and why using AI for “lesson planning” and similar tasks won’t solve the global crisis of teacher burnout and attrition. And yet, the low-hanging fruit of “time-saved on X, Y and Z” continues to be the preferred measurement for tech companies marketing their products into K-12 and Higher Education.
Closer to home, a recent article from Microsoft leads with the headline: “Brisbane Catholic Education boosts agency and efficiency with Microsoft 365 Copilot“. Like Watkin’s reflections on the Walton Foundation report, I’m interested in the language used throughout the Microsoft piece.
It begins in suitably militant fashion, stating that the purpose of the pilot is “To combat staff workload”. Like a a crack commando unit, AI is “deployed” into the system to solve a raft of workload problems, most of which are framed as “administrative and compliance demands”. AI, we are told, “seemed like a natural solution to address many of these issues.” To put this plan into action, staff “underwent Microsoft 365 Copilot simulation training with a focus on safe-use.”
By this point in the article, I’m picturing the training scene from the Matrix.
Eventually, after reading a lot about students “driving their own learning” with AI, we get to the numbers.
Significant reductions in educator workload were noted across the BCE schools participating in the pilot, saving up to 9.5 hours per week on administrative duties and planning. Meanwhile, Copilot supported principal workflows with legislative scanning, trend analysis, and rapid curriculum mapping.
It’s unclear from the article what any of these tasks actually entail. But what stands out to me is that, much like the Walton Foundation report, Microsoft is very keen to attach a number to time-saved. Despite all of the rhetoric throughout the article about “personalised learning” and “improved cognition”, the main conclusion is this: using AI will make you more efficient.
A Different Measurement
Let’s be clear: I’m not anti-AI. I have been outspoken about both the harms of (generative) AI, and the ways in which I find it helpful. But I have hardly ever found that AI saves me time, or makes me more efficient. And that’s something reflected in many conversations I’ve had with K-12 and Higher Education teachers using AI for the past few years.
Measuring time-saved is a narrow, unhelpful approach to understanding the potential positives of AI in education. Teacher’s don’t want to make tedious administrative processes more efficient: they want them gone. Using AI to speed up the process of writing arbitrary reports is still an arbitrary task. Sending meaningless emails using Copilot is still meaningless. Jumping through hoops to document lessons in a format designed to appease compliance processes is still hoop jumping, even if it is marginally faster with a chatbot.
So what should we be measuring?
A few years ago (he writes, realising it was in fact a decade ago), I studied teacher burnout and attrition through the perspective of teacher professionalism, autonomy, and curriculum expertise. My M.Ed. culminated in a research project to determine whether quality professional development and increased teacher agency could improve the wellbeing of staff in a faculty.
Of course, there is no simple answer to improving teacher wellbeing or reducing attrition and burnout, but from years of research and my own findings there are a few things we could measure and improve upon that take us beyond simplistic ideas like “number of hours per week saved on paperwork.”
Here are a few:
1. Design work that is achievable and well-resourced.
Teachers stay when the daily job feels achievable. Studies demonstrate that realistic workloads, adequate materials and mentoring are linked to lower burnout and attrition and higher job satisfaction. Actions like protecting planning time and improving classroom support reduce the factors driving many to leave, especially early career teachers.
2. Hand genuine professional autonomy back to teachers.
Giving educators latitude to choose methods, sequence content and shape assessment raises self-efficacy, intrinsic motivation and mental health outcomes, while mitigating stress. Being empowered to make decisions can counter the fatigue that comes from feeling micro-managed and is a consistent predictor of both retention and engagement.
3. Keep work meaningful and purpose-driven.
Meaningfulness can act as a “protective factor”, reducing emotional exhaustion, stress and intent-to-leave. Aligning roles with teachers’ values nurtures this sense of purpose, and many teachers report better outcomes when they are teaching in their field of expertise and training in a supportive environment such as an effective faculty.
4. Build individual and collective self-efficacy through targeted feedback and growth.
“Mastery experiences” – successfully tackling challenging teaching tasks – together with specific, positive feedback strengthen teachers’ belief in their competence, which in turn fuels job satisfaction and commitment. Mentoring or coaching, appropriate (not surveilling) peer observation and effective leadership of projects support these more challenging but rewarding moments.
5. Develop a supportive, trusting professional culture.
Collegial relationships, authentic leadership and a strong professional identity amplify wellbeing and resilience, while poor school climate magnifies workload stress. Investing in collaborative decision-making, professional learning communities, self-directed professional learning, and meaningful behaviour frameworks can create a “staffroom culture” that acts as a buffer rather than a stressor.
6. Replace punitive accountability with trust-based models.
High-stakes testing regimes and standardised programs erode autonomy, heighten stress and push teachers out. Moving towards formative, peer-led evaluation and broader measures of impact restores professionalism and reduces workload associated with compliance
In the next post, I’ll explore ways GenAI could potentially be used to support these processes. The examples come from the work I’ve been doing alongside teachers and school leaders for the past three years conducting our own pilots of AI and teacher workload. Throughout these pilot projects, we never once attempted to measure how much time we had saved.
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