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← AI for Science Teachers

Facilitator guide · staff development day

AI for Science Teachers

Everything you need to run this course with your staff — 5 sessions, ~4 hours total.

How to run it

Run this with your Science faculty as a twilight, half a staff development day, or KLA-meeting-by-meeting — it's ~4 hours. It assumes staff have done the flagship 'Teaching with AI' course (or will): this playbook is the Science-specific layer, so don't re-litigate general ethics — point back to it and go deep into Science. Each module has a session plan, three discussion prompts and a 'watch for'. Before you start: make sure everyone can log in to Lessio and to NSWEduChat; ask each teacher to bring one real, de-identified Science task from their own teaching (a prac they run, a unit they're about to write, a data set they wish they had); and — this is the Science non-negotiable — have your school's chemical management / risk-assessment process and your CLEAPSS-equivalent or DoE chemical safety guidance open in the room, because the safety message only lands if it's concrete. Capture two things across the cohort: a faculty rule for 'how we use AI in Science' (with the safety line written in bold), and each teacher's capstone reflection for their eTAMS PD record.

Session plans

  1. 1

    Where AI actually helps in Science — and where it fails

    ~50 min
    Session plan

    Open with a deliberately scary live demo: ask a general AI for 'an impressive, reactive chemistry demonstration for a Year 9 class' and read the output to the room — then pull out the safety gaps together (PPE? ventilation? concentrations? compatibility?). That single demo earns the whole course's safety discipline. Then build the seven-row use-case grid on the board from the staff's own examples, and have each teacher stress-test one Lessio output for the four failure modes.

    Discussion prompts
    • Where in our Science teaching would a confident factual error do the most damage before we'd notice?
    • Which practicals do we run where an AI-drafted method would be genuinely tempting — and genuinely risky?
    • How does Working Scientifically keep us honest about what AI can and can't do for student investigation?
    Watch for

    Two camps: the over-trusting ('the method looked fine to me') and the dismissive ('so it's useless for Science'). The truth is in between — superb for drafting data, explanations and scaffolds; never trusted for facts, numbers or safety. Don't let the safety demo turn into 'AI is dangerous, ban it'; the message is 'AI drafts, a qualified human verifies'.

    Standards2.1 Content and teaching strategies of the teaching area2.6 Information and Communication Technology (ICT)4.4 Maintain student safety
  2. 2

    Subject-specific prompt craft for Science

    ~50 min
    Session plan

    The hands-on hour. Live-build one Science prompt with the room: take a vague 'make a prac on X', add the real outcome code (have the NESA Science 7–10 syllabus open to grab one), add the WS skill, add the units/data/safety guard-rails, and watch the output improve on screen. Then everyone rebuilds a prompt from their own week and pairs up to critique. Keep the NESA outcomes list and your school's safety process both visible.

    Discussion prompts
    • Which Science guard-rail (units, data, safety) does our faculty most often forget to ask for?
    • What's a Science task you'd never thought to give AI that a well-built prompt makes possible?
    • Let's write one faculty prompt — fully grounded, with the safety hand-back — that we can all reuse.
    Watch for

    Staff will write good content prompts but drop the self-check and the safety line — exactly the bits that matter most in Science. Insist the self-check and (where relevant) the risk-assessment hand-back are in every prompt before it's 'done'. Confidence varies; pair a strong prompter with a hesitant one.

    Standards2.1 Content and teaching strategies of the teaching area2.5 Literacy and numeracy strategies2.6 Information and Communication Technology (ICT)
  3. 3

    Planning & resources for Science with Lessio

    ~50 min
    Session plan

    A faculty-team build. Each teacher (or pair) generates a real program for next term in Lessio — Stage 4, Stage 5 or senior — and runs the Science review-before-use checklist on it live. Teach code-verification by having everyone confirm two of their own outcome codes against the NESA Science 7–10 site, and teach content-verification by having each person find and fix one science inaccuracy. End by collecting the practicals named across all the units into one list to be risk-assessed.

    Discussion prompts
    • Where would a hallucinated outcome code or a wrong science explanation do the most damage in our programs?
    • Which mandatory Science requirements (Working Scientifically coverage, practical work, depth studies in senior) must our reviews always check?
    • Could a colleague defend this program in a registration audit — and is every prac in it safe to run?
    Watch for

    The risk is treating a polished Lessio draft as finished — emphasise that the verifying and editing ARE the professional work, and in Science that includes checking the *science*, not just the layout. Watch for programs that list Working Scientifically outcomes but don't actually build the skills.

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

    Assessment, feedback & integrity in Science

    ~50 min
    Session plan

    Two halves. First, an assessment make-and-verify: each teacher generates a Science assessment with a marking guide in Lessio, then swaps with a colleague to re-work every answer and check units and marks — surfacing how often a confident guide is wrong. Second, an integrity redesign: map current senior depth studies and 7–10 investigations against 'could a student outsource this to AI?' and redesign one to make the process visible (checkpoints, in-class data, a viva).

    Discussion prompts
    • Which of our Science assessment tasks are most exposed to AI misuse — and how would we redesign them around first-hand data?
    • Where could AI legitimately scaffold a depth study without ever doing the student's inquiry?
    • How will we teach students to use AI ethically in their investigations — as a tutor they cite, not a ghost-writer?
    Watch for

    Integrity anxiety runs high in senior Science. Steer firmly from 'detection' (unreliable) to task design and first-hand data. Watch for staff trusting an AI marking guide without re-working the answers — make the swap-and-verify step mandatory; it's the moment the 'verify the numbers' message becomes real.

    Standards2.3 Curriculum, assessment and reporting5.1 Assess student learning5.2 Provide feedback to students on their learning
  5. 5

    Capstone — build a real Science resource and critique it

    ~50 min
    Session plan

    Run as a longer workshop or directed time. Each teacher builds their connected Science set in Lessio and self-assesses against the Science Ethical-Use Checklist — with a partner sign-off specifically on the safety items (no practical passes without a completed risk assessment). Collect the reflections: they're your evidence of a Standards-relevant PD day and the teachers' eTAMS record. Finish by agreeing the faculty's 'how we use AI in Science' rule, with the safety line written in bold.

    Discussion prompts
    • What's our shared standard for 'ready to use' AI-drafted Science material — and where does safety sit in it?
    • What one faculty rule for responsible AI use in Science should we adopt from today?
    • How will we log this as PD and keep building our Science prompt library from here?
    Watch for

    Some will want to skip the reflection — but it's what makes this real PD and the eTAMS evidence, so protect the time. The capstone's hard edge is the safety sign-off: don't let any practical-containing artefact be called 'done' without a completed risk assessment. Capture the faculty rule — that's what leadership takes forward.

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

After the day

Collect each teacher's capstone artefact and reflection — that's your evidence of a Standards-relevant PD day, and theirs to log in eTAMS. Part of the 'Subject AI Playbooks' line, included in the whole-school Lessio programme; also available standalone per teacher or per faculty. Because NESA removed the Accredited/Elective PD categories in 2024, this playbook counts as Standards-relevant PD without an endorsement gate — a Science faculty can run it as a twilight or staff development day, with Lessio doing the NSW-grounded Science generation underneath.

  • Every fact, data set and equation verified against a reliable source — never assumed from the AI.
  • No AI-generated practical, demonstration or risk assessment used without a qualified safety/risk check against the school's chemical and safety guidance.
  • No student personal data entered into general AI tools; cohorts described, never children.
  • Every outcome code and Working Scientifically skill checked against the official NSW Science syllabus.
  • Every AI output reviewed and owned by the teacher; AI use disclosed where policy requires, and students taught to use AI with integrity in their investigations.

Standards-relevant professional learning, mapped to the APST · verified against national and NSW frameworks, June 2026.