With assessment tasks set to begin submission early next week, I wanted to capture my reflections before my views are coloured by final student outputs.
I started this term with a big question: how can an AI-infused, flipped learning model do more and be more? I wanted my students to engage deeply with history, not just learn about it. I also wanted to explore how AI could shift my role as a teacher—not to replace me, but to enhance my ability to guide, challenge, and inspire.
Now, as the term wraps up, I find myself reflecting on what worked, what didn’t, and what I need to keep thinking about for next term!
The Seasonal Structure & AI: What Worked and What Didn’t
My flipped learning approach follows a seasonal structure, where different phases of learning organically flow across the term. In theory, this model should create more space for student-driven inquiry and historical thinking. In practice, it did – but with some caveats.
What worked:
- Front-loading flipped content early in the term meant more time later for deep engagement. Because of significant front loading. Students had space to interrogate sources, work like historians, and collaborate meaningfully.
- AI integrated seamlessly into this structure – when properly scaffolded. Scaffolding of student AI use was most effective in the form of ‘working routines’ such as the “Two I-Statements and a Verb“, the “Plus 3” and the “Plussy” RAG approaches. When students used these approaches after initial content mastery and historical thinking activities (scaffolded themselves with thinking routines like ADAMANT), they could use AI to deepen, rather than shortcut, their thinking.
- AI use required explicit scaffolding, and this worked effectively when students were given structured ways of working rather than being left to “figure it out” (the ‘Nike Principle’). When provided with clear strategies, AI became a thinking partner rather than just another tool.
- Students were willing and able to embrace ways of working with AI that we described in class as ‘a HI + AI hybrid’ or ‘a cyborg’ way of working. Students resonated with France Huang’s observation that ‘B-grade’ results were the new “new baseline”… and that the ways to A-grade results was through applying their own HI (human intelligence) to problems alongside AI.
- Younger students initially needed reassurance in an in-flipped model, but once they saw the benefits – more collaborative classroom time, responsive face-to-face mini-lectures for sense-making – their trust in the model grew. A number have also expressed confidence in concept mapping – using stylus in their OneNote – as a form of notetaking. This is a powerful metacognition strategy.
What didn’t:
- The moment high-stakes assessment loomed, some students defaulted to grade-chasing behaviours. I had to work hard to keep the focus on authentic learning rather than performative success. The ongoing challenge of performativity in schools that is reinforced by an ‘assessment culture’ will likely be a feature of my reflections for some time.
Further Challenge Ahead: Expanding Agency in the Final Phase
While personal agency is definitely growing, I see suspect that the finals weeks of the term might be an opportunity for further development of these skills.
I want to create more explicit opportunities for students to explore their civic and global agency. I want to ensure students extend their historical thinking beyond the classroom. This means designing learning activities that position students as active participants in shaping historical narratives and future possibilities – considering how history informs their roles in society, governance, and global citizenship. I’d like students to engage in a study of history that can be seen as reparative and generative.
I’d like to find enhanced ways to challenge students to view history as a dynamic force in shaping the future.
AI as a Thinking Partner, Not an Answer Machine
One of the biggest concerns in education right now is whether AI will lead to shallow learning – a loss of critical thinking skills. That was not my experience. When used well, AI didn’t replace thinking – it amplified it.
I saw this most clearly when students:
- Crafted detailed prompts after completing their own historical source analysis. AI became a tutor, extending their thinking, not dictating it.
- Engaged in multi-turn dialogues rather than accepting the first AI-generated response. When students realised they could shape AI outputs, they began using it more strategically in ways that enriched their understanding and learning.
- Blended human intelligence (HI) with AI – the real win. The goal isn’t to let AI think for them but to develop a HI + AI approach that extends their own analytical abilities.
The Changing Role of the Teacher
With AI handling some cognitive load, I’ve noticed a shift in my teaching practice – but not in the way some might expect.
What’s changed?
- More direct instruction on AI literacy – teaching students how to use AI effectively, critically, and ethically.
- More facilitation, less ‘sage-on-the-stage’ teaching – students spent more time doing, and I spent more time coaching, guiding, and questioning.
- More focus on metacognition – getting students to reflect on how they were learning, not just what they were learning.
- More human connection – perhaps surprisingly, using AI freed up more time for meaningful human classroom conversations about big ideas – for example, about values, democracy, dictatorship, art, and music.
Oddly enough, despite working harder than ever, I feel more present, more in tune with my students. It’s a paradox – AI is changing the way I teach, but instead of displacing me, it’s making me feel that my role is more important than ever.
What’s Next? Refining the Model
This term confirmed a few things:
- Flipping extensively early, with powerful, regular, and varied checks for understanding, is key – The more I can effectively front-load flipped content, the more time we’ll have later for higher-order historical inquiry.
- AI independence will take time – Students still need structured guidance and reassurance before becoming truly effective at self-directing their AI use.
- AI reflection is the missing piece – Students should be thinking about how AI influences their learning, not just using it. The conversation should not be about whether AI is accurate or not. It is increasingly accurate and powerful. The conversations in class should be on how, when, where, and why it is appropriate to use AI and how best to maintain student voice.
- Scaffolding is very important for school students when they use AI – If AI is going to be a thinking partner (a co-intelligence), students need their teachers to guide them in their early steps. Students are still, largely, novice users, and will benefit from explicit models, strategies, and thinking routines that guide their AI use.
This work is ongoing. I don’t have all the answers, but one thing is clear: AI isn’t making history teaching less important. It’s making it matter more than ever.
Final Thought: AI and the Future of Historical Thinking
If AI can provide answers in seconds, what’s left for history teachers?
A lot.
Our job isn’t to deliver content – it’s to teach students how to interrogate it, challenge it, and shape it into something meaningful. AI doesn’t diminish historical thinking. When used well, it can supercharge it, helping students not only engage critically with the past but also imagine possible futures.
History is not just about understanding what was – it’s about questioning what could be. If we want history to ‘do more’ and ‘be more’, we need to empower students with the tools to think expansively, ethically, and creatively.
AI, when integrated with strong historical thinking practices, becomes a tool for fostering agency, values-driven inquiry, and forward-thinking historical perspectives.
The challenge is making sure we teach students how to use AI thoughtfully – so that it serves their curiosity, agency, and humanity, rather than dulling it.
AI should not just be a tool for efficiency; it should be a catalyst for deeper inquiry, creative exploration, and intellectual autonomy. When students learn to shape AI as an intellectual partner, rather than passively consume its outputs, they develop a richer, more nuanced understanding of history – and of their own capacity to think critically in an AI-augmented world.
This term has been a step forward in that journey. Next term, I’ll push it further.
Disrupted History 