One school. One qualification. Seven years of real work.
The Catalyst AI Diploma is the only programme we run. Every student is on it from Year 7 to Year 13. It is project-based from the first day to the last. Deep subject teaching across the curriculum, an explicit AI-literacy spine, and a STEM specialism in the senior phase — assessed continuously through portfolio and viva, not through written exams.
How the programme is structured.
Foundation
Broad subject teaching across the curriculum. Short, scaffolded project cycles each term. AI literacy taught explicitly from Year 9 — prompting, evaluation, bias, provenance. Daily AI tutoring in core subjects.
Diploma Core
Five extended projects across two years. Portfolio assessment with external moderation. Each project takes a full term, supervised by a subject specialist, and ends in a public defence.
Senior Phase
STEM specialism through extended research projects with university and industry mentors. Outputs are university-grade — published papers, working systems, patentable designs. Direct progression to universities, apprenticeships and industry.
Broad subject teaching with project cycles built in.
In Years 7–9, students study the full curriculum. There are no exams, but there is rigour — daily reading, mathematics every day, hands-on science, languages, history and geography, art, music and physical education. AI tutors run alongside teachers to deliver instant feedback, retrieval practice and one-to-one support.
Each term ends with a short project cycle: a piece of work, a public presentation, and a defence. The skill we are building is the habit of finishing things, defending them, and learning from the questions.
The Catalyst AI Diploma — Phase 2
From age 14, the work gets serious. Students complete five extended projects over two years. Each project takes a full term, is supervised by a subject specialist, ends in a viva, and lives in a portfolio that follows the student to whatever comes next.
Why this exists
Working life is project-shaped, not exam-shaped. People are paid to scope problems, gather what they need, ship things that work, and explain every choice in them. The Diploma teaches that skill directly. By 16, our students have shipped five substantial pieces of work each — and can talk about every line, every decision, every dead end.
The five units
Build & Ship
Design, build and release a working AI-powered tool that solves a real problem for a real user. Assessed on code quality, user research and ethical review.
Investigate & Argue
A long-form research project on a contemporary issue. AI assistance is permitted and logged; the viva tests independent understanding.
Model & Simulate
Build a quantitative model of a system — economic, environmental, biological — and defend its assumptions, limits and failure modes.
Create & Critique
Produce a substantial creative work (writing, film, music, design) that uses AI tools deliberately, with a written critical commentary.
Capstone
A self-directed project of the student's design, supervised by a faculty mentor and an external partner. Public showcase at year-end.
How it's assessed
| Component | Weight | How it works |
|---|---|---|
| Portfolio of work | 50% | The five project artefacts, with provenance logs and reflective commentary. |
| Viva voce | 30% | One-to-one defence with internal and external assessors. Tests independent understanding. |
| Process journal | 10% | Weekly entries showing iteration, dead ends and what was learned. |
| Public showcase | 10% | Capstone presentation to faculty, families and an invited external panel. |
What students leave with
A Catalyst AI Diploma is reported with an overall outcome (Distinction*, Distinction, Merit, Pass), the five unit outcomes, and the full portfolio of work — code, hardware, documents, films, models — that the student can show to a university, an apprenticeship employer, or anyone else who wants to know what they can actually do. External moderation is provided by a panel of former chief examiners, university admissions tutors and industry practitioners.
STEM specialism, university-grade output.
From 16, our students go deep. Each Senior Phase student takes a primary specialism, a secondary specialism, and a working language. They spend two years on extended research projects with university and industry mentors. The output is not a set of exam grades — it's a body of work credible enough to take a graduate seat at a research group, an apprenticeship offer at a tech firm, or a place at a university that admits on portfolio and interview.
Mathematics
Pure, applied and computational mathematics. Symbolic AI as a "second pair of eyes" on proofs; original problem-solving as the assessed output.
Physics & Engineering
From electromagnetism to robotics. Workshop access, in-house fabrication, partnerships with university engineering departments.
Chemistry
Molecular visualisation, reaction prediction and synthesis planning alongside traditional practical work.
Biology & Bioinformatics
Working with real genomic and ecological datasets using AI-assisted analysis. Lab work in partnership with university bio departments.
Computer Science & AI
Students build and deploy real production systems — LLM-based applications, ML pipelines, embedded systems. Outputs are working software, not exam answers.
Research methods
A shared spine for every senior student: literature review, experimental design, statistics, technical writing, peer review.
What the timetable looks like.
Subject teaching
Timetabled lessons across the curriculum, with AI-supported feedback loops. Written work returned within 24 hours.
Project workshop
Protected afternoon for project work. Faculty mentors available; AI tools used openly and logged.
Subject teaching
Lessons designed around the previous day's AI-generated retrieval practice and intervention groupings.
Build day & weekly defence
Full day of project work, ending with a stand-up where each student presents progress to peers and faculty.