A year into LGR22, many teachers have the same practical question: what do I do with the kursplan on Monday morning? You can understand the intent, agree with the values, and still spend hours translating “aim”, “centralt innehåll”, and “betygskriterier” into tasks, questions, and resources that actually work with your class.
This series starts from that reality. Rather than treating AI as a shortcut or a threat, we’ll treat it as a “translation layer” you control: a way to move from syllabus language to classroom language without losing your professional judgement. If you’re new to using AI for planning, you may want to pair this with a simple routine for prompting for better lesson resources so your outputs stay consistent.
LGR22 is not just a list of topics. It sits inside a broader mission: schooling shaped by democratic values, equity, and a responsibility to develop knowledge and Bildung together. In practice, teachers meet LGR22 through the kursplan. That’s where the “what” and “why” become teachable: the aim, the content to be covered, and what quality looks like at different levels.
What LGR22 is not is a script. It does not tell you which text to use, which activity to run, or how to pace your lessons week by week. That freedom is powerful, but it also creates workload. The same freedom is why AI can help: it can draft options quickly, while you remain accountable for the final choices and the way they land with your pupils.
The shift from LGR11 to LGR22 is often felt as a change in emphasis rather than a total rewrite. Many teachers notice a stronger focus on knowledge and clarity, alongside revised syllabuses and updated grading criteria. In History, that can show up as more explicit expectations about reasoning, use of sources, and the kinds of historical concepts pupils should handle.
If you are used to “working backwards” from assessment, the changes can feel like both a relief and a challenge: clearer wording can help you plan, but it can also expose gaps in existing resources. AI is particularly useful here because it can help you reframe older activities so they create better evidence for E/C/A—provided you keep the checks tight. For a wider view of how AI can support planning without taking over, see AI lesson planning that still feels like you.
The fastest way to read the kursplan is to treat it like a mapping exercise. You are trying to connect three things: what pupils should develop (aim), what they should encounter (centralt innehåll by stage), and what quality looks like (betygskriterier E/C/A). Once those are mapped, lesson design becomes a matter of choosing activities that generate the right kind of evidence.
A simple template helps. You can keep it in a shared department document and reuse it for each unit.
The key is not to overfill the template. A tight map is easier to teach from and easier to quality-check. If you already use structured planning formats, you can adapt that approach from unit planning with AI so the AI drafts fit your existing workflow.
AI should not be your authority on LGR22. It should be your fast drafter and rephraser, turning syllabus statements into classroom-ready materials you can judge. To keep that relationship healthy, run every output through three checks.
First, syllabus alignment: does the activity clearly link to the centralt innehåll you selected, and does it create opportunities for E/C/A evidence? Second, factual accuracy: especially in History, AI can sound confident while being wrong. Third, age-appropriateness: language level, sensitive topics, and the cognitive load of tasks must fit your group.
This mirrors good practice in any AI-supported resource creation. If you want a simple way to teach pupils and colleagues what “checking” looks like, it can help to borrow routines from AI literacy in the classroom, even when the AI use is teacher-facing.
Here is a teacher-in-the-loop workflow for a single lesson: “Vikingarna, årskurs 5, 60 minuter”. The aim is speed without losing control.
Start with a prompt that forces structure and references your mapping. For example:
“Act as a History lesson planner for åk 5. Create a 60-minute lesson on ‘Vikingarna’ aligned to the following centralt innehåll lines: [paste]. Include: learning goal in pupil-friendly Swedish, key vocabulary, 10-minute retrieval starter, short teacher input (max 8 minutes), guided practice, independent task with success criteria, exit ticket, and adaptations for two support levels and one stretch. Provide a short list of sources you used or would recommend. Do not invent facts; flag uncertainty.”
A useful output structure usually includes a lesson narrative, then printable elements. You might ask the AI to draft a one-page pupil sheet, a vocabulary box, and an exit ticket. If you use AI to generate worksheets, keep them deliberately plain so you can add your own visuals and local examples later. You can also align the tasks with your preferred questioning style, as described in AI for questioning and retrieval.
Your edits are where professionalism lives. In a Viking lesson, you might tighten any vague claims (“Vikings did X”) into careful phrasing (“Some Norse communities…”), add a local museum link or a class novel connection, and adjust the independent task so it produces better E/C/A evidence. For instance, you might replace a generic “write a paragraph” with a structured explanation: “Explain two reasons people travelled, using one source detail and one historical concept.”
Finally, make it print-ready. Ask the AI to reformat the pupil sheet with clear headings, wide spacing, and a short checklist; then proofread and simplify the Swedish classroom language.
For an 8-lesson unit, AI becomes most valuable when it helps you sequence knowledge and revisit it through retrieval and varied tasks. Consider “Forntiden och antiken” (åk 4). Your mapping template should list the relevant centralt innehåll lines and the kinds of reasoning you want to see by the end.
Prompt for a unit plan that includes lesson-by-lesson focus, retrieval links between lessons, and low-stakes assessment opportunities. Ask for differentiation notes that are realistic: sentence stems, vocabulary support, and optional challenge tasks that deepen thinking rather than simply adding more writing.
A strong AI draft will propose a sequence such as: an introduction to time and chronology; everyday life comparisons; key developments (farming, tools, trade); early civilisations; and a final synthesis lesson. Your teacher edits should ensure the content is not overloaded and that the unit avoids a “tour of facts”. In practice, you might reduce the number of civilisations covered, then add more time for pupils to practise explanations using concepts like change, continuity, cause, and evidence.
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The fastest way to raise the quality of pupils’ thinking is often to stabilise the language. A “concept explainer” workflow creates a prerequisite terms table you can reuse across lessons, displays, and support sheets.
Ask the AI for a table with: term, simple definition in Swedish, an example, a non-example, a common misconception, and a suggested teacher question. For History, include both content terms (for example, “runsten”) and thinking terms (for example, “orsak”, “källa”, “tolkning”). Add a final column for “classroom phrase”, so pupils have sentence starters such as “En möjlig orsak är…” or “Källan visar… men vi vet inte…”.
This workflow pairs well with any approach to scaffolding and can reduce barriers for multilingual learners. If you want to systematise that support, you can adapt ideas from AI for differentiation while keeping the same syllabus mapping at the centre.
Quality assurance is what stops AI from quietly misrepresenting the syllabus or narrowing your curriculum. Before you teach, do a quick check that is short enough to be used every time.
Confirm that your planned activities genuinely cover the selected centralt innehåll lines, rather than only mentioning them. Look for explicit opportunities to gather E/C/A evidence: where can pupils demonstrate factual knowledge, reasoning, and communication? Then check source integrity: if the AI suggested sources, verify they exist and are appropriate; if it included “facts”, cross-check at least the key claims. Finally, scan for bias and representation. In History, that includes whose perspective is centred, what language is used about groups, and whether complexity is reduced into stereotypes.
If you want a consistent way to run these checks as a team, it helps to borrow a lightweight review routine from AI policy and classroom safeguards, even if your school is still developing formal guidance.
The real time-saving comes after the first run. Build a reusable prompt library for your department: one prompt for a 60-minute lesson, one for an 8-lesson unit, and one for concept tables. Keep them short, but always include the mapping fields and the three checks. Then adopt a simple versioning routine: date-stamp each plan, note what you changed after teaching it, and keep a “next time” line at the top. Over a term, you’ll build a bank of resources that are both syllabus-aligned and shaped by real classroom experience.
Used this way, AI does not replace the craft of teaching LGR22. It reduces the friction between the kursplan and the classroom, so your expertise goes into the decisions that matter: what to emphasise, how to explain it, and how to help pupils show what they know.
May your next LGR22 plan feel lighter—and more teachable.
The Automated Education Team
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