Half-term is when many of us realise differentiation has slipped into two unhelpful extremes. Either we produce ‘three versions’ of everything and burn out, or we stick to one version and hope support in the room can bridge the gap. Under LGR22, a more sustainable stance is ‘same destination, different paths’: keep the learning goals stable, then deliberately vary the access points so more pupils can reach them.
What’s changed for me this half-term is treating differentiation as a workflow that can be repeated and audited. When it’s a workflow, it becomes teachable to colleagues, easier to improve, and easier to evidence. It also makes AI useful without letting it take over. If you’re mapping where AI genuinely saves time (and where it adds risk), the thinking here sits alongside LGR22: from gap to tool map, because the point is not ‘more tools’. It’s fewer tools, used consistently.
The sequence below uses four AI micro-tools in a fixed order. Each tool produces a small, reviewable output. Together, they create multiple access points across difficulty, reading demand, accessibility, and language—without lowering expectations.
The non-negotiables matter more than the tools.
Teacher-in-the-loop means you decide the learning intention, success criteria, and what ‘good’ looks like. AI drafts; you approve.
Transparency means you can explain what changed and why. That protects pupils and protects you.
High expectations means the goal stays put. We adjust the ladder, not the height of the wall.
If you want a broader sense of how ‘micro-tools’ can be inspection-ready and cross-curricular, this LGR22 throughline piece complements the approach here: short, consistent routines beat occasional big reinventions.
Here the micro-tool takes one ‘standard’ task and produces two adjacent variants: one that reduces complexity (DOWN) and one that increases it (UP). Crucially, the learning goal remains identical.
Learning goal: explain how photosynthesis and cellular respiration are connected in a plant.
STANDARD (core task)
Explain, in a paragraph, how photosynthesis and cellular respiration are linked in plants. Use these words: glucose, oxygen, carbon dioxide, water, energy. Include one simple equation (a word equation is fine).
DOWN (same goal, more support)
Complete the paragraph using the word bank, then add one sentence of your own.
Word bank: glucose, oxygen, carbon dioxide, water, energy, makes, uses, releases, stores.
Sentence starters: ‘Photosynthesis happens in the leaves and it ___ glucose.’ ‘Cellular respiration happens in cells and it ___ glucose to release ___.’
Finish: Write one sentence explaining why both processes matter for growth.
UP (same goal, more depth)
Explain the link between photosynthesis and cellular respiration, then evaluate this claim: ‘Plants only respire at night.’ Use evidence and include a brief correction. If you can, connect your explanation to how stomata affect gas exchange.
What changed is the cognitive load, not the destination. DOWN reduces the working memory demand through sentence starters and a word bank, while still requiring an independent final sentence to show understanding. UP adds evaluation and a common misconception, so pupils must apply the same science to a new claim.
The ‘must not’ list is your safeguard against quiet lowering. The key vocabulary stays. The causal link stays (‘photosynthesis stores energy in glucose; respiration releases energy from glucose’). DOWN must not become ‘label a diagram only’ if the goal is explanation. UP must not drift into unrelated extension content.
This micro-tool varies reading demand while keeping the conceptual target and the assessment signal aligned. It is particularly useful when you want to maintain a shared lesson conversation but provide different entry points to the text.
Topic: climate change impacts and adaptation choices.
Option A: 300-word narrative + literal questions
A short narrative from the perspective of a coastal resident describing higher tides, saltwater in gardens, and a community meeting about sea walls. Questions focus on retrieving information, sequencing events, and identifying stated reasons.
Option B: 1,000-word expository text + analytical questions
A structured explanation covering sea-level rise, storm surge risk, managed retreat, cost–benefit trade-offs, and equity. Questions require comparing strategies, weighing evidence, and justifying a recommendation for a council plan.
Both texts target the same big ideas: impacts, adaptation choices, and trade-offs. The questions are different in demand, but they point to the same assessment signal you care about: can the pupil explain an impact and justify an adaptation choice using evidence from a source?
A practical trick is to make the final response format identical for everyone. For example: ‘Write a recommendation with two reasons and one limitation.’ Pupils can draw from either text, but the goal and success criteria remain shared. If you’re building classroom routines around visible vocabulary and retrieval prompts, AI-inclusive displays and vocabulary walls has ideas that pair well with this approach.
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This micro-tool is not ‘make three lessons’. It audits one lesson plan for barriers and suggests targeted adjustments. That distinction matters, because inclusion often fails when it becomes duplication.
Take a single lesson plan (starter, input, guided practice, independent task, exit ticket) and ask the tool to flag barriers for dyslexia, autism, and A2 newly arrived students. Then choose a small number of adjustments that help multiple pupils at once.
In practice, that might look like tightening slide text to one idea per slide; adding icons and consistent colour-coding for key processes; providing a worked example with annotations; pre-teaching five critical words with images; and offering a ‘choice of output’ that still evidences the same thinking (a spoken explanation recorded, a labelled flowchart with connectors, or a paragraph).
For dyslexia, you might reduce visual crowding, increase spacing, and ensure reading is not the only route to the goal. For autistic pupils, you might add predictable timings, explicit transitions, and clarify what ‘good’ looks like with an exemplar. For A2 newly arrived students, you might keep sentence structures clear, avoid idioms, and provide bilingual keyword support—without simplifying the underlying concept.
If you’re building a ‘minimum viable inclusion stack’ of tools and routines, this accessibility consolidation guide is a useful companion, because it helps you choose adjustments that are sustainable, not performative. For spoken alternatives or fluency supports, voice AI in schools adds a safeguarding-aware lens.
This micro-tool generates bilingual bridging lists for key terms, used as a precision support rather than a full translation of the lesson. The goal is conceptual accuracy, not speed.
Choose 8–12 terms that carry meaning in your topic (for photosynthesis: glucose, chlorophyll, carbon dioxide, oxygen, energy, respiration, stomata, gas exchange). Ask for a bilingual list with: the term, a pupil-friendly definition in English, a translation, and a ‘watch out’ note for false friends or everyday meanings.
The ‘watch out’ note is where quality lives. Words like ‘respiration’ can be mistranslated as ‘breathing’ only, which narrows the science. ‘Adaptation’ in Geography or Biology can be confused with ‘adoption’ or with ‘getting used to something’ in everyday speech. You can also ask the tool to provide an example sentence that matches your curriculum phrasing, so pupils hear consistent language in explanations and in writing.
If you’re working in multilingual contexts and want workflows that respect LGR22 without turning teachers into translators, minimum viable AI workflows for modersmål offers a broader set of routines.
A workflow is only as good as its checks. Before anything reaches pupils, run four quick reviews.
Accuracy: verify facts, equations, and definitions against your trusted source. In science, check that simplification has not introduced error.
Cognitive demand: confirm the goal and success criteria are unchanged across versions. Look for ‘quiet lowering’, such as replacing explanation with naming.
Bias and representation: scan narratives and examples. Who is portrayed as knowledgeable, vulnerable, or responsible? Adjust to avoid stereotypes.
Clarity and accessibility: ensure fonts, spacing, and layout are readable, and that instructions are unambiguous. If a pupil misses the task because the wording is unclear, that is not differentiation.
If differentiation is auditable, you need light-touch artefacts that show intent and impact without creating paperwork for its own sake.
Save the three versions of the task (STANDARD/DOWN/UP) with a short note stating the unchanged learning goal and what was varied. Keep the reading options with the shared final response prompt highlighted. Store the accessibility audit output alongside the revised lesson plan, with two or three chosen adjustments marked. Keep the bilingual bridging list with your ‘watch out’ notes.
Versioning can be as simple as a date stamp in the filename and a short change log at the top of the document. A reflection log for next half-term only needs three prompts: ‘What barrier did we remove?’, ‘What evidence shows pupils reached the goal?’, and ‘What will I keep/change next time?’ If you’re tightening your wider governance around AI use, the annual acceptable use policy refresh checklist can help you align classroom practice with whole-school expectations.
May your next half-term bring clearer goals and calmer differentiation. The Automated Education Team
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