Idrott och hälsa is, at its best, noisy, practical and pupil-led. You’re watching movement quality, teamwork, effort, safety awareness and decision-making in real time. Yet the after-work can feel like a desk job: lesson plans, progression notes, reasonable adjustments, risk assessments, incident logs, and evidence that links what pupils did to what they learned. LGR22 doesn’t ask you to replace physical learning with paperwork, but it does expect coherence: clear intentions, a thought-through progression, and fair assessment grounded in observable evidence.
What tends to trip teachers up is not the teaching, but the translation. A brilliant Brännboll lesson becomes a few rushed sentences. A carefully scaffolded orienteering sequence becomes a generic plan with missing adaptations. AI can help here, not by “doing the thinking”, but by turning your quick, practical notes into structured drafts you can refine. If you’re building staff-wide routines, it’s worth aligning this with a sensible policy refresh so everyone shares the same boundaries and language; the annual AI acceptable use policy refresh checklist is a helpful starting point.
A useful way to stay LGR22-ready is to treat documentation as a map with four lanes: objectives, progression, inclusion and assessment evidence. You can run the same map for every unit, whether it’s ball games, outdoor education, swimming, or health education.
Objectives are your “why this lesson exists” statements, written in pupil-facing language and teacher-facing detail. Progression is what changes over time: increasing complexity, independence, accuracy, or tactical understanding. Inclusion is the planned access route for pupils with different needs, including temporary injuries, sensory needs, anxiety, language barriers, or low confidence. Assessment evidence is what you can actually point to afterwards: observation notes, peer-feedback protocols, self-assessment prompts, or short reflective tasks that don’t steal time from movement.
AI works well when you feed it the smallest possible “teacher truth” and ask it to draft into this map. If you want a broader inclusion lens for the whole school, the idea of a “minimum viable inclusion stack” from this accessibility guide translates neatly into PE: predictable routines, clear visuals, multiple ways to show learning, and proactive sensory considerations.
Brännboll is a classic example: pupils love it, but the rules, roles and safety expectations are easy to miscommunicate, and inclusion needs are often handled on the fly. A strong “sport explainer” document gives you three wins: consistent instruction, transparent learning focus, and pre-planned adaptations.
Start with your own rough notes, then use AI to draft. Your input might be: class age, space, equipment, key rules you use, the learning focus (for example, tactical choices and fair play), and known barriers (hearing protection, low confidence with striking, mobility needs).
Ask the AI to produce a one-page explainer with: rules in plain Swedish, role cards (batting, fielding, base judge), a learning focus paragraph, and accessibility adaptations. You can also request a “transparency note” box that states what the AI did and what you decided. That matters, because it keeps professional judgement visible.
In practice, an accessibility adaptation might look like this in the final document: a larger bat or tee option; a “choice of strike” rule (hit, roll, or kick); a quieter waiting zone for pupils who dysregulate in crowds; visual role cards; and a rotating “coach role” for pupils who cannot run that day. You keep the core physical experience, but you stop improvising the same adjustments every lesson.
To make the explainer stick, borrow display thinking from classroom practice: simple icons, key verbs, and short retrieval prompts. The approach in AI inclusive classroom displays adapts well to a sports hall wall: “scan”, “signal”, “support”, “recover”, with pictures and a single example.
Transparency notes to include at the bottom are straightforward: “Draft generated with AI from teacher notes on date X. Rules checked against the lesson format used in this class. Adaptations reviewed for safety and fairness. Final decisions: teacher.”
Orienteering is perfect for a timed plan because transitions and safety checks matter. For an 80-minute lesson, your AI prompt should specify: location boundaries, map type, groupings, equipment, and your non-negotiables (buddy system, check-in points, whistle signals). Then ask for a timed breakdown, tiered objectives, and differentiation.
A solid breakdown might include a brisk starter that rehearses map symbols with movement, a short skills demo, then a progressive main task: first a “line course” with obvious controls, then a choice-based course where pupils select challenge level. Tiered objectives should read like “all/most/some” but grounded in observable actions: “All pupils can orient the map to match the environment and follow a simple route with a partner; most can use clear handrails and collect controls independently within boundaries; some can plan a route that balances speed and accuracy, explaining their choices.”
Differentiation in orienteering is often about cognitive load rather than fitness. You can offer fewer controls, larger symbols, colour-coded routes, or a role split where one pupil navigates and the other time-keeps and checks safety. AI can draft these options quickly, but you decide what is realistic in your terrain and with your pupils.
This is also where a small “teacher decision log” becomes powerful. If you’re new to building AI micro-routines, the habit of writing down your decision points is similar to what’s suggested in an ECT/NQT AI operating manual: use AI for drafts, then record what you changed and why. In PE, that “why” is often safety, inclusion, or local context.
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Risk assessments can become copy-and-paste theatre unless they are genuinely tied to your lesson design. AI can help you produce a structured first draft for two common high-attention contexts: orienteering (off-site or on-site boundaries) and swimming at a municipal pool. The key is to provide the minimum necessary detail and to insist on roles, controls, and sign-off lines.
For orienteering, hazards might include: pupils leaving boundaries, slips and trips, weather exposure, tick bites, and panic if separated. Controls include: clear mapped boundaries, buddy system, timed check-ins, whistle protocol, first aid kit location, staff positioning, and a “return-to-base” rule. For swimming, hazards often include: drowning risk, poolside slips, asthma events, panic, and changing-room safeguarding. Controls include: lifeguard coordination, group registers, lane allocation, clear entry and exit routines, medical plan access, and supervision ratios aligned to your setting’s policy.
Ask the AI to produce a table with columns for hazard, who might be harmed, controls, residual risk, and responsible adult. Then add a “roles and communication” section: who leads, who sweeps, who holds the register, who contacts reception, and what the emergency signals are. Finally, include a sign-off box: teacher, line manager (if required), date, and review date.
If your school is aiming for an auditable trail, it’s worth aligning your PE paperwork with broader reporting and documentation practices. The audit-trail thinking in report writing AI assistants compared applies here too: keep versions, record sources, and make the “human decision” visible.
An AI draft is only useful if it passes a few simple gates. First is fact-checking: rules, timings, and safety steps must match your actual practice and site. Second is an inclusion check: do the adaptations preserve dignity and meaningful participation, rather than sidelining pupils? Third is the teacher decision log: a short paragraph capturing what you accepted, what you rejected, and what you changed.
These gates do not need to be bureaucratic. A workable pattern is to add three lines at the end of each document: “Checked for accuracy”, “Checked for inclusion”, “Teacher decisions recorded”. Over time, you build consistency and reduce rework.
PE and health education can involve sensitive personal data: medical needs, safeguarding concerns, injuries, and family context. Your safest approach is a minimum-data pattern. Use anonymised, generic descriptors (“a pupil with asthma”, “a pupil with anxiety about changing rooms”) and keep identifiable information out of AI tools entirely unless your platform is explicitly approved for that data.
As a rule, never paste names, personal numbers, medical diagnoses, incident narratives, or safeguarding disclosures into a public AI chat. Don’t upload registers, EHCP/IEP documents, or detailed incident reports. If you need AI support, rewrite the scenario so it becomes a design problem rather than a personal record: “How can I design a warm-up that reduces asthma triggers?” rather than “How do I support X who had an asthma attack last week?” For voice tools in particular, safeguarding and consent matter; the boundary framework in voice AI in schools is a useful reference even if you only use text.
A pipeline becomes sustainable when you can reuse prompts and checklists. Below are three short templates you can paste into your approved AI tool and adapt.
Use this prompt for a “lesson-to-documentation” draft: “Draft LGR22-aligned documentation for an Idrott och hälsa lesson. Context: [age/year], [space], [equipment], [focus]. Output: objectives (pupil and teacher), progression link to prior/next lesson, inclusion adaptations (at least 6, including temporary injury), assessment evidence (observable), and a short reflection prompt. Use plain Swedish for pupil-facing parts. Add a transparency note and a teacher decision log section.”
Use this checklist to quality-gate any AI draft: accuracy checked against site and equipment; safety routines match local policy; adaptations preserve participation and dignity; language is clear for pupils; assessment evidence is observable; no personal data included; version saved with date.
Finally, keep a one-page LGR22 alignment record for each unit: unit focus, key concepts and skills, progression statement, inclusion principles, evidence sources, and a short note on how you ensure fairness. If you’re rolling this out across staff, you may find it easier to start with three micro-routines and a 30-day review cycle, similar to the approach in this INSET day AI workshop plan.
May your next unit feel lighter on paperwork and richer in purposeful movement. The Automated Education Team
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