Subject playbook

AI for Science Teachers

Use AI to draft practicals, data sets, explanations and depth-study scaffolds for NSW Science — while you verify every fact, every number and every safety instruction.

Modules

Each module: clear learning outcomes → short, accurate Science input → a hands-on activity using the Lessio generator → interactive knowledge checks. Grounded in the real NSW Science outcomes and Working Scientifically, and mapped to the Australian Professional Standards for Teachers. The 'verify facts, data AND safety' discipline runs through every module.

Click a module to read it.

1. 1

   Where AI actually helps in Science — and where it fails

   The high-value Science use cases (practical scenarios, data sets, models, CER, depth studies, exam questions) — and the four failure modes that are uniquely dangerous in Science: confident pseudo-science, fabricated data, wrong units/equations, and unsafe practical instructions.~50 min

   By the end of this module you'll be able to:

   • Identify the high-value AI use cases in Science and tie each to Working Scientifically.
   • Name the four Science-specific failure modes — confident factual/pseudo-science error, fabricated data, wrong units/equations, and unsafe practical instructions — and recognise each in an output.
   • Apply the rule that AI is never trusted for a fact, a data set or a safety/risk decision without a qualified check.

   Standards2.1 Content and teaching strategies of the teaching area2.6 Information and Communication Technology (ICT)4.4 Maintain student safety

   Start here: Science is the subject where confident AI is most dangerous

   You've done the flagship course — you know AI is a fluent next-word predictor, that plausible isn't true, and that the teacher stays in the loop. Science raises the stakes on every one of those points. A wrong adjective in an English draft is a style note. A wrong number in a Science draft is a misconception you teach. A wrong chemical instruction in a Science draft can hurt a child. So this playbook keeps one discipline in front of everything: verify the facts, verify the data, and never trust AI for safety.

   Where AI genuinely helps a Science teacher

   Used as a drafting assistant, AI is a real time-saver across the Science workflow. The high-value cases:

   Use case	What AI drafts	Your job
   Practical scenarios	A first-draft experiment context, method outline and a DRAFT risk-assessment starter	Verify the method works and is safe; you complete the formal risk assessment
   Data sets to analyse	A realistic, contextualised data set students process and graph (labelled as synthetic)	Sanity-check the numbers, units and trend; confirm it's fit for the outcome
   Diagrams, models & analogies	Text descriptions of models, labelled-diagram plans, and analogies for abstract ideas	Check the model isn't misleading; fix the analogy's limits
   Explanations & CER scaffolds	Plain-English explanations and claim–evidence–reasoning writing frames	Verify the science; adjust the reasoning to your cohort
   Differentiating dense texts	A scientific passage rewritten for reading level, with a key-term glossary	Confirm no key science was lost in simplification
   Depth-study / investigation scaffolds (senior)	Planning frames, variable tables, method and analysis scaffolds	Keep it the student's investigation; check rigour
   Exam-style questions	Graduated questions with mark schemes in NESA-style command terms	Verify every answer and every mark allocation

   Notice what runs through the right-hand column: Working Scientifically. In the NSW Science 7–10 syllabus, Working scientifically is the skills strand that threads through every focus area — questioning and predicting, planning and conducting investigations, processing and analysing data, problem-solving, and communicating (Stage 4 outcomes SC4-WS-01 to SC4-WS-08; Stage 5 SC5-WS-01 to SC5-WS-08). AI can scaffold all five — if you keep the rigour. AI cannot do the investigating; it can build the frame the student investigates inside.

   The four failure modes that are uniquely dangerous in Science

   General models fail in Science in ways that don't show up in other subjects. Know all four by name — you'll hunt for them for the rest of the course.

   1. Confident factual error and pseudo-science. A model will state that "heavier objects fall faster", that deoxygenated blood "is blue", or repeat folk-science and debunked claims — in the same calm, authoritative tone it uses for correct physics. It has no truth-meter. It cannot tell good science from confident nonsense, so you must.

   2. Fabricated data and results. Ask for "results from a titration" or "data from a pendulum experiment" and it will invent a suspiciously clean table — perfect trends, no scatter, an R² of 0.999. Fabricated data is useful only when you label it as synthetic teaching data and you've checked it's physically reasonable. Never present AI-invented numbers as real experimental results, and never let a student think they are.

   3. Wrong units, quantities and equations. Models routinely slip on the things Science lives or dies by: mixing grams and moles, dropping a factor of 1000, quoting g = 10 in one line and 9.8 in the next, rearranging an equation incorrectly, or attaching the wrong unit (J vs W, mol vs g·mol⁻¹). A plausible-looking calculation can be quietly, completely wrong. Re-do every calculation yourself.

   4. UNSAFE practical instructions. This is the one that makes Science different from every other subject. An AI asked for a "fun reactive demonstration" can produce instructions that are genuinely hazardous — incompatible chemical combinations, missing PPE, no ventilation note, wrong concentrations, or a reaction that's exothermic-and-then-some. It will write it confidently and it will look like a normal method.

   The hard line for Science: never use an AI-generated practical, demonstration or risk assessment without checking it against your school's chemical management and safety guidance, and completing your own formal risk assessment. AI drafts the scenario; a qualified human signs off the safety.

   This is APST 4.4 — Maintain student safety in the most literal sense. It is also why, throughout this course, the AI only ever produces a DRAFT risk-assessment starter — a prompt for your thinking — never an authority you act on.

   Why a grounded engine changes the odds (but not the duty)

   A general chatbot writes Science from general text about Science. Lessio drafts to the verbatim NESA Science outcomes and the DoE program template, which sharply reduces the syllabus-accuracy errors (wrong outcome codes, wrong focus area, off-stage content). It does not remove your duty to verify facts, data and safety — no tool does. Grounding improves the draft; the discipline is still yours.

   Activity — map your week onto the grid (12 min)

   Open the Lessio generator and pick a Science focus area you teach this term (say Stage 5 Energy, SC5-EGY-01). Generate a short resource for it. Now mark it against the seven-row use-case grid above: which cell is this? Then deliberately stress-test it for the four failure modes — find (or rule out) a factual slip, any invented data, a unit/equation error, and anything you'd never run without a safety check. Write down the one thing you'd verify first before this touched a class.

   Knowledge check

   1Name the four Science-specific failure modes of general AI.

   Show answer

   2An AI gives you a clean titration data table with a perfect straight-line trend. How should you treat it?

   Show answer

   3Why can you never use an AI-generated practical or risk assessment as-is?

   Show answer

   Mark this module complete
2. 2

   Subject-specific prompt craft for Science

   Grounding prompts in real NSW Science outcomes, the Science pitfalls that need built-in self-checks (units, data, safety), and a Science prompt library you can use Monday.~50 min

   By the end of this module you'll be able to:

   • Anchor a Science prompt to a real NSW outcome and to Working Scientifically, using the RICE structure.
   • Build the Science-specific self-checks (verify facts, flag invented data, re-do calculations, never finalise safety) into the prompt itself.
   • Select and adapt a prompt from the Science prompt library for a real task.

   Standards2.1 Content and teaching strategies of the teaching area2.5 Literacy and numeracy strategies2.6 Information and Communication Technology (ICT)

   RICE, with a Science accent

   You learned RICE in the flagship course — Role, Intent, Constraints, Examples. The structure doesn't change for Science; what changes is what you put in each slot, because Science has its own anchors and its own traps.

   • R — Role & context. Name the Science role and the de-identified cohort. "You are an experienced NSW Stage 5 Science teacher planning for a mixed-ability Year 10 class with several EAL/D learners."
   • I — Intent (task + the syllabus anchor). This is where Science prompting gets its power: anchor to a real outcome and the relevant Working Scientifically skill. "Draft a claim–evidence–reasoning scaffold for the Disease focus area (SC5-DIS-01), building 'analyses data … and draws conclusions' (SC5-WS-06)."
   • C — Constraints & format. Reading level, Australian spelling, the structure you actually use — and the Science guard-rails: "use correct SI units throughout", "show every step of any calculation", "do not invent experimental data unless you label it clearly as synthetic".
   • E — Examples & evaluation. Show your style, and demand the Science self-check: "At the end, list (a) any fact or figure I must independently verify, and (b) — if there is any practical element — state plainly that I, the teacher, must complete the risk assessment before use."

   The Science self-check is not optional

   In other subjects the closing self-check is good hygiene. In Science it's a safety control. Build these into every Science prompt:

   "Flag anything I should verify. Re-state any equation and show each step. Mark any data you generate as synthetic. If anything involves a practical, demonstration or chemical, say explicitly that the teacher must complete the risk assessment — do not present safety as settled."

   This turns the model's three worst Science habits — confident error, fabricated data, unsafe instructions — into a checklist it hands you.

   Ground it, don't just ask it

   A weak Science prompt produces generic, sometimes-wrong science. A grounded one produces something you can actually verify quickly because it's tied to the syllabus.

   Weak prompt	Strong, Science-grounded prompt
   "Make a worksheet on energy."	"You are a NSW Stage 5 Science teacher. Create a one-page resource on the law of conservation of energy for the Energy focus area (SC5-EGY-01), for a mixed-ability Year 10 class. Include a plain-English explanation, one fully-worked efficiency calculation with correct SI units and every step shown, and three graduated questions with an answer key. Flag anything I should verify and mark any data as synthetic."
   "Give me a prac on reaction rates."	"You are a NSW Stage 5 Science teacher. Draft a scenario and method outline for a student investigation into a factor affecting reaction rate (SC5-RXN-02), building 'designs … investigations' (SC5-WS-03). Identify the independent, dependent and controlled variables. Provide a DRAFT risk-assessment starter only — then state clearly that I must complete the formal risk assessment against my school's chemical safety guidance before this is used."

   The strong prompts are longer because they carry the real outcome, the Working Scientifically skill, the units/working/data guard-rails, and the safety hand-back. In Lessio, the syllabus anchor is built in — you select the real Science outcomes and it grounds the draft for you, so your verifying is faster.

   Literacy and numeracy live in here too

   Science is a literacy and numeracy subject (APST 2.5). Prompt craft is where you act on that: ask for a glossary of tier-3 science vocabulary, for CER frames that scaffold scientific writing (SC5-WS-08, communicating arguments with evidence), and for worked numerical examples that model unit-handling and significant figures. Good Science prompts produce both the science and the language-and-number scaffolds students need to access it.

   Activity — rebuild a Science prompt, then ground it in Lessio (15 min)

   Take a Science task you'd hand to AI this week. Write your instinctive one-liner. Rebuild it with RICE: add the Science role, a real outcome code, the Working Scientifically skill, the units/data/safety guard-rails, and the Science self-check. Run it. Then build the same task in Lessio and notice how much of the outcome-anchoring is already done — leaving you to verify facts, numbers and safety rather than chase the syllabus.

   Knowledge check

   1What three Science-specific guard-rails belong in the Constraints slot of a Science prompt?

   Show answer

   2Why anchor a Science prompt to both a focus-area outcome and a Working Scientifically outcome?

   Show answer

   3What is the single most important line to add to any Science prompt that touches a practical?

   Show answer

   Mark this module complete
3. 3

   Planning & resources for Science with Lessio

   Generate a connected Science set — scope & sequence → program → resources — grounded in the real NSW syllabus, then verify and own it. Worked example: a Stage 5 Energy program.~50 min

   By the end of this module you'll be able to:

   • Produce a syllabus-aligned Science scope & sequence, program and resources as one connected set in Lessio.
   • Verify a generated Science program against the real NSW outcomes, Working Scientifically coverage, and practical/safety requirements.
   • Apply review-before-use to Science drafts so the edits are visibly your professional judgement.

   Standards2.2 Content selection and organisation2.3 Curriculum, assessment and reporting3.4 Select and use resources

   Plan Science as a connected chain

   Faculty Science planning is a chain, not a pile of lessons: scope & sequence (the year) → program / unit (the weeks) → resources (the materials) → assessment (the measure) — all pointing at the same NSW outcomes. Lessio is built to generate exactly these artefacts, grounded in the verbatim NESA Science syllabus and the DoE program template. (Assessment is module 4; here we focus on the first three.)

   Worked example — a Stage 5 Energy program

   Say you're writing Energy for Year 10. In Lessio you select the real outcomes — SC5-EGY-01 (evaluates current and alternative energy use based on ethical and sustainability considerations) plus the Working Scientifically skills the focus area carries — SC5-WS-01, SC5-WS-04, SC5-WS-07 — and it drafts a program against the DoE's five-column teaching-and-learning sequence:

   Outcomes & content · Teaching and learning activities · Evidence of learning · Differentiation & adjustments · Registration & evaluation

   A grounded draft will sketch the conceptual flow — law of conservation of energy → efficiency → energy sources and their evaluation — with activities, a place for a practical, and differentiation under the UDL headings. That's a strong starting point. Now your judgement takes over.

   Review-before-use — the Science edition

   Run every Science draft through this before it becomes a real document:

   1. Codes — every outcome code is real and current (e.g. SC5-EGY-01, not an invented "SC5-EGY-02"), and matched to the right focus area and stage. AI can fabricate codes that look perfectly real.
   2. Content accuracy — the science is correct. Conservation of energy, efficiency, the behaviour of energy sources — check the explanations, not just the structure.
   3. Working Scientifically coverage — the program actually builds the WS skills it claims (planning, processing/analysing data, problem-solving, communicating), not just lists them.
   4. Practical & safety — every prac or demo named is one you will risk-assess; no method is treated as ready. Equipment and chemicals are ones your school actually has and permits.
   5. Sequence & numeracy — the order builds knowledge sensibly for your students; any quantitative work (efficiency calculations, units) is correct and appropriately scaffolded.
   6. Inclusion — language, contexts and examples are inclusive and culturally safe, including Aboriginal and Torres Strait Islander perspectives where authentic (and don't have AI fabricate cultural content).

   If you couldn't defend the program in a faculty meeting or a registration audit, it isn't ready. The edits you make are the proof of your professional judgement — that's what the teacher-in-the-loop principle looks like for a Science program.

   Across the K–12 Science span

   The same loop holds wherever you teach:

   • Science and Technology K–6 — generate units against the primary focus areas (observing and questioning, investigating change, systems and energy, sustainable solutions and design), then verify the science and keep any hands-on activity teacher-checked.
   • Science 7–10 — Stage 4 (e.g. Observing the Universe SC4-OTU-01, Forces SC4-FOR-01, Periodic table and atomic structure SC4-PRT-01) and Stage 5 (e.g. Disease SC5-DIS-01, Reactions SC5-RXN-01/02, Genetics and evolutionary change SC5-GEV-01/02).
   • Senior sciences — Biology, Chemistry, Physics, Earth and Environmental Science, Investigating Science and Science Extension: generate module resources and the scaffolds for the depth study, which is a mandatory component of these courses (more in module 4).

   Activity — generate a Science unit, then verify it (15 min)

   In Lessio, generate a program / unit of work for a Science focus area you teach next term (try Stage 5 Energy, SC5-EGY-01, or a Stage 4 area). Then do the three edits that make it yours: (1) verify two outcome codes against the official NESA Science syllabus; (2) check one science explanation for accuracy and fix it; and (3) flag every practical in the unit as 'risk assessment required — mine to complete'. Those three edits are the visible record of your professional judgement.

   Knowledge check

   1What are the connected Science artefacts Lessio generates, in order?

   Show answer

   2Beyond checking outcome codes, what two Science-specific checks does review-before-use add?

   Show answer

   3A generated Stage 5 program cites 'SC5-EGY-02' for energy sources. What do you do?

   Show answer

   Mark this module complete
4. 4

   Assessment, feedback & integrity in Science

   Build valid Science assessments and depth-study / scientific-investigation scaffolds with marking guidelines; give CER-shaped feedback; and assure authorship the way NESA expects — by design, not detectors.~50 min

   By the end of this module you'll be able to:

   • Draft a valid Science assessment with marking guidelines in NESA command terms, then verify every answer and mark.
   • Scaffold a depth study / scientific investigation with AI while keeping the inquiry the student's own.
   • Assure authorship and integrity in Science the way NESA expects — by task design and process checkpoints, not 'AI detectors'.

   Standards2.3 Curriculum, assessment and reporting5.1 Assess student learning5.2 Provide feedback to students on their learning

   Assessment AI can draft — and what you must verify

   AI is genuinely useful for drafting Science assessment: exam-style questions across Bloom levels, data-response items, practical-report tasks, and marking guidelines in NESA command terms (identify, describe, explain, analyse, evaluate). But Science assessment is where the failure modes bite hardest, so the verification is non-negotiable:

   • Every answer re-worked by you. A marking guide with a wrong worked answer or a mis-stated unit will mis-grade a whole class.
   • Every data set sanity-checked. If a data-response question uses generated data, confirm the numbers are physically reasonable and the 'correct' trend is actually what the data shows.
   • Marks that match NESA expectations. Check the mark allocation and that the command term matches the cognitive demand (an "evaluate" worth one mark is a red flag).
   • Validity — the task measures the intended outcome (content and the Working Scientifically skill), not just recall.

   Use AI to draft the assessment and the marking guidelines; use your subject expertise to make it correct, valid and fair (APST 5.1).

   Depth studies & scientific investigations — scaffold, don't author

   The senior sciences (Biology, Chemistry, Physics, Earth and Environmental Science, Investigating Science) include a mandatory depth study, and Working Scientifically investigations run K–10. AI is a strong scaffolding partner here — and a real integrity hazard if misused. The line:

   AI may help build the frame — a question-development prompt, a variables table (independent / dependent / controlled), a method-planning checklist, a data-analysis or CER scaffold, a marking rubric. AI must not do the inquiry — not the student's question, not their data, not their analysis, not their conclusion. The whole point of a depth study is the student's own investigation; an AI-written one is both worthless and malpractice.

   A safe use: generate a depth-study scaffold and rubric for the class, then teach students to plan their own study inside it — and design the checkpoints (below) that keep it theirs.

   Feedback — CER frames from AI, judgement from you

   Science writing has a backbone: claim – evidence – reasoning. AI drafts CER-shaped feedback fast — claim addressed? evidence sufficient and accurate? reasoning links evidence to claim using correct science? — against your success criteria (building SC5-WS-08, communicating scientific arguments with evidence). Then you make it specific, scientifically accurate and kind for the actual student. Never paste an identifiable student's work into a general tool (flagship Module 2) — de-identify, or use your school's approved secured environment. And check the AI hasn't 'corrected' a student toward a wrong science point — verify the feedback's science before it goes back.

   Integrity in Science — by design, not detectors

   Schools decide whether AI is permitted for a given task, and you uphold HSC and RoSA integrity. In Science, design for authorship rather than chasing it with unreliable "AI detectors":

   • Make process visible — log-book or journal entries, planning at checkpoints, data collected in class, draft-and-redraft of analysis.
   • Anchor to first-hand data — a task built on the student's own experimental results is hard to fake and easy to discuss.
   • Talk to students about their thinking — a short viva on method and results ("why this control? what does this anomaly mean?") confirms authorship far better than any detector.
   • Teach disclosed, ethical AI use — students may use AI as a tutor they critique and cite, not a ghost-writer for their investigation.

   Activity — build and verify a Science assessment (15 min)

   In Lessio, generate a short assessment with marking guidelines for a focus area you teach (e.g. a data-response task for Stage 5 Disease, SC5-DIS-01, building SC5-WS-06). Then do the Science verification: re-work every answer, sanity-check any data and units, confirm each mark allocation matches its command term, and add one authorship checkpoint (a checkpoint, a viva question, or an in-class data step) that makes the task hard to outsource.

   Knowledge check

   1What must you verify on an AI-drafted Science marking guideline before you use it?

   Show answer

   2Where exactly is the line for AI use in a depth study or scientific investigation?

   Show answer

   3How does NESA expect you to assure authorship in Science — and what should you not rely on?

   Show answer

   Mark this module complete
5. 5

   Capstone — build a real Science resource and critique it

   Build a connected, defensible Science set with Lessio — program + resource + assessment with a depth-study or practical element — then verify facts, data and safety, critique it, and log it as PD.~50 min

   By the end of this module you'll be able to:

   • Build a connected Science program, resource and assessment with Lessio, end to end.
   • Critique and improve it against the real NSW syllabus — verifying every fact, data set, equation and safety element.
   • Self-assess against the Science Ethical-Use Checklist, reflect, and record the hours as Standards-relevant PD.

   Standards2.1 Content and teaching strategies of the teaching area6.2 Engage in professional learning7.1 Meet professional ethics and responsibilities

   The task — a real, connected, defensible Science set

   Choose a Science topic you'll teach next term. Using Lessio, build and then critique:

   1. A program / unit of work for one focus area (e.g. Stage 5 Energy SC5-EGY-01, or a senior module).
   2. A resource within it — a contextualised data set for analysis, a CER scaffold, an explanation-with-analogy, or a DRAFT practical scenario (with a risk-assessment starter you will complete).
   3. An assessment with marking guidelines — including, if senior, a depth-study scaffold and rubric.

   Then make it yours — the Science verification pass

   Improve each artefact with your professional judgement. For Science, that specifically means:

   • Codes & coverage — every outcome code real and current; the right focus area and stage; Working Scientifically genuinely built.
   • Facts — every scientific statement checked; no confident pseudo-science survives.
   • Data & numbers — every data set sanity-checked and labelled if synthetic; every calculation re-worked; every unit correct.
   • Safety — every practical, demonstration or chemical element is one you have risk-assessed against your school's chemical/safety guidance; no AI-generated practical is used without that qualified check.
   • Inclusion & fairness — inclusive, culturally safe contexts; the assessment valid and fair, with reasonable adjustments.

   A connected, syllabus-accurate Science set you'd actually use — drafted by AI, unmistakably shaped, verified and owned by you. Your edits — and especially your safety checks — are the evidence of your professional judgement.

   Self-assessment — the Science Ethical-Use Checklist

   Run your capstone against all five checklist items on this page (below the modules). Every box should be honestly tickable — including "every fact, data set and equation verified" and "no AI-generated practical used without a qualified safety/risk check". If one isn't tickable, fix the artefact. That is the learning.

   Reflection — write a short response

   • What did AI genuinely save you time on in Science, and what did you have to fix (a fact, a number, a method, a safety gap)?
   • Where did your subject expertise change the output?
   • What's the one Science-specific rule you'll keep for using AI responsibly — and does it put safety first?

   Log it as professional learning

   This module is your assessment: a complete, critiqued Science set plus your ethical-use reflection — keep it as evidence of practice. Because NESA removed the Accredited/Elective PD distinction in 2024, Standards-relevant learning like this counts toward your 100 maintenance hours — record it in your eTAMS PD log against the Standards it addresses (especially Standard 2 — including 2.1 content and teaching strategies of the teaching area — plus 4.4 student safety, 4.5, 5, 6 and 7). Your faculty can also run this playbook as part of its professional-learning plan or a staff development day.

   Knowledge check

   1What turns an AI-generated Science unit into defensible professional work?

   Show answer

   2Which two checklist items are the Science non-negotiables in your capstone self-assessment?

   Show answer

   3How does this playbook count toward your NESA professional-development hours?

   Show answer

   Mark this module complete

Take-away prompt library

Ready, RICE-shaped prompts for common NSW teaching jobs (Module 3). De-identified — copy one, swap in your details, and use it today.

You are a NSW [stage] Science teacher. Create a realistic, contextualised data set (8–12 data points) for students to process and analyse for [focus area / outcome code], building 'processes and analyses data' (e.g. SC5-WS-06). Give it an Australian context, sensible variables and correct SI units. Clearly label the data as SYNTHETIC teaching data. Then state the trend students should find, and flag anything I should sanity-check before use.

You are a NSW [stage] Science teacher. Build a claim–evidence–reasoning (CER) scaffold for a [de-identified class] writing about [concept/outcome], building 'communicates scientific arguments with evidence' (e.g. SC5-WS-08). Include sentence starters for each of claim, evidence and reasoning, and one worked exemplar paragraph. Use correct science and plain English; flag any scientific claim I should verify before giving this to students.

You are a NSW [stage] Science teacher. Explain [concept] in three ways for a [de-identified class]: a plain-English explanation, a concrete real-world analogy (Australian context, and state the analogy's limits), and a labelled-diagram description. Then name one common misconception to pre-empt and how to address it. Flag anything I should double-check for scientific accuracy.

You are a NSW Science teacher supporting a mixed-ability [de-identified class] including EAL/D learners. Rewrite the passage below at roughly a [reading level], keeping ALL key science and tier-3 vocabulary (add a short glossary of any term you simplify). Use short sentences and active voice. Then list any scientific nuance you dropped so I can decide whether to add it back.

[paste passage]

You are a NSW [stage] Science teacher. Write eight questions on [topic/outcome] for a [de-identified class], graduated from recall to analysis, using NESA command terms, with a full answer key and mark allocations. Show every step of any calculation and use correct SI units. Flag any question where the answer could be ambiguous, and list every fact or figure I must independently verify before use.

You are a NSW [stage] Science teacher. Draft a student investigation SCENARIO and method OUTLINE for [factor/phenomenon, e.g. a factor affecting reaction rate], building 'designs safe, ethical, valid and reliable investigations' (e.g. SC5-WS-03). Identify the independent, dependent and controlled variables. Provide only a DRAFT risk-assessment STARTER (hazards to consider, PPE prompts). Do NOT present this as ready to run: state explicitly that I, the teacher, must complete the formal risk assessment against my school's chemical and safety guidance, and have it checked, before any student uses it.

Standards-relevant professional learning, mapped to the APST · content verified against national and NSW frameworks, June 2026 · self-log the hours in eTAMS.