Our school was facing a familiar challenge. Questions around AI and academic integrity were growing, and the DP teams wanted clarity. Students were unsure what counted as acceptable use. Teachers were interpreting boundaries differently. The result was inconsistency, frustration, and a sense that we were always reacting rather than leading.
The instinct in situations like this is to move quickly toward a solution. Draft a policy. Share it. Implement it.
That is where I started.
I began by reviewing existing IB guidance, pulling together case studies that outlined different uses of AI and whether they were acceptable. At our collaborative planning meeting, I brought these to the team. Together, we sorted them. Some felt relevant but needed adjustment. Others did not fit our context at all.
From there, I drafted a presentation built around the case studies we had agreed on. I brought it back to the team, invited feedback, and revised it again. The final version was something everyone felt comfortable standing behind. When we shared it with the DP coordinator, the response was clear: this approach should extend across departments so students experience consistency in every subject.
On the surface, this might look like a straightforward initiative. Identify a problem, design a solution, roll it out.
But that is not what made it work.
What mattered was how the work was done.
From the beginning, this was not about delivering a finished product to teachers. It was about building understanding together. The case studies were not just content; they were a tool for discussion. Teachers were not asked to comply with a policy. They were invited to shape it.
That shift is subtle, but it changes everything.
Schools often operate in an initiative-driven model. A need is identified, a solution is designed, and teachers are expected to implement it. Even when the idea is strong, this approach can fall flat. It positions change as something external, something to adopt rather than something to understand.
An inquiry-driven model works differently.
Instead of starting with answers, it starts with questions:
- What is actually happening in our classrooms?
- Where are the inconsistencies?
- What do students understand, and where are they confused?
From there, teams investigate, test, and refine.
This is where structured approaches like Plan–Do–Study–Act (PDSA) cycles become useful. Originating in improvement science, PDSA emphasizes iterative testing in real contexts rather than large-scale implementation based on assumptions (Deming, 1986). The cycle creates a disciplined approach to inquiry:
- Plan: Identify a specific problem or gap
- Do: Try a targeted approach
- Study: Examine the impact using real evidence
- Act: Adjust based on what was learned
The goal is not to get it right the first time. The goal is to get better over time. Research on improvement science in education reinforces that small, iterative cycles grounded in evidence are more likely to lead to meaningful and sustained change than top-down implementation alone (Tichnor-Wagner et al., 2017).
In the case of our AI work, we were not formally running a PDSA cycle, but the principles were there. We identified a gap in clarity. We tested an approach through case studies. We gathered feedback. We refined. What emerged was not just a stronger product, but stronger shared understanding.
This connects directly to the purpose of professional learning communities. The focus is not on completing tasks or rolling out initiatives. It is on collective inquiry into student learning. When teams anchor their work in questions rather than directives, the conversation shifts. It becomes less about “what do we have to do?” and more about “what are we noticing, and what should we try next?” Research on PLCs consistently highlights collaborative inquiry and a focus on evidence of student learning as key drivers of improved teaching practice and student outcomes (Vescio et al., 2008; Stoll et al., 2006).
That shift also changes the role of leadership.
In an initiative-driven model, leadership is about direction and implementation. In an inquiry-driven model, leadership is about creating the conditions for meaningful investigation. It means building structures where teachers can test ideas, examine evidence, and refine their practice without feeling like they need to get it perfect immediately.
It also builds something more durable: professional agency. When teachers are actively involved in shaping change, they are more likely to develop a sense of efficacy and ownership over their practice (Bandura, 1997; Goddard et al., 2004). This matters. Collective teacher efficacy has been identified as one of the strongest school-based influences on student achievement.
The practical implication is straightforward but not easy.
Instead of mandating a strategy, create space for teams to test it. Instead of rolling out a policy, involve teachers in shaping it. Instead of expecting immediate consistency, allow for iteration.
This does not mean lowering expectations. It means changing how those expectations are reached.
The AI policy work succeeded not because it was perfectly designed from the start, but because it was built through shared inquiry. The clarity we achieved came from the process, not just the final product.
There is a tendency in schools to feel pressure to move quickly, to solve problems efficiently, to demonstrate action. But sustainable change rarely comes from speed alone. It comes from understanding.
When teachers see an approach working in their own classrooms, when they have been part of testing and refining it, the change does not feel imposed. It feels earned.
That is when it sticks.
References (APA 7)
Bandura, A. (1997). Self efficacy: The exercise of control. W. H. Freeman.
Deming, W. E. (2000). Out of the Crisis. MIT Press.
Goddard, R. D., Hoy, W. K., & Hoy, A. W. (2004). Collective Efficacy Beliefs:Theoretical Developments, Empirical Evidence, and Future Directions. Educational Researcher, 33(3), 3–13. https://doi.org/10.3102/0013189X033003003
Stoll, L., Bolam, R., McMahon, A., Wallace, M., & Thomas, S. (2006). Professional Learning Communities: A Review of the Literature. Journal of Educational Change, 7(4), 221–258. https://doi.org/10.1007/s10833-006-0001-8
Tichnor-Wagner, A., Wachen, J., Cannata, M., & Cohen-Vogel, L. (2017). Continuous improvement in the public school context: Understanding how educators respond to plan–do–study–act cycles. Journal of Educational Change, 18(4), 465–494. https://doi.org/10.1007/s10833-017-9301-4
Vescio, V., Ross, D., & Adams, A. (2008). A review of research on the impact of professional learning communities on teaching practice and student learning. Teaching and Teacher Education, 24(1), 80–91. https://doi.org/10.1016/j.tate.2007.01.004
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