Edison AI Academy · Foundations
AI Hypergeneralist
AI is not replacing ambitious students. It is replacing students who were never taught to think with AI.
A broad, rigorous foundation in AI fluency for students who want to become capable across tools, ideas, and real-world use cases.
- Age range
- 13–18
- Duration
- 38 weeks · 4 terms
- Format
- Hybrid
- Level
- Foundation
- Cohort
- 12–16 students
- Outcome
- AI fluency portfolio
What is the AI Hypergeneralist program?
The AI Hypergeneralist is Edison AI Academy's flagship year-long program for students aged 13–18 — 38 weeks across four terms, roughly 120 hours of work. Students progress through the Edison Method at rising depth: understanding how AI works, prompting with precision, writing Python that calls AI APIs, building retrieval systems and simple agents, and defending a capstone at a formal exhibition.
It produces six major projects and a professional portfolio. No prior coding is needed — bootcamp graduates and complete beginners both start here. It is the foundation year for every advanced Edison pathway.
Learn to think, work, and create with AI across disciplines.
A broad, rigorous foundation in AI fluency for students who want to become capable across tools, ideas, and real-world use cases.
The AI Hypergeneralist is Edison's flagship year — a full-year education for students who intend to be fluent with AI, not merely familiar with it.
Four terms move through the Edison Method at rising depth: Think, Build, Create, Communicate. A student begins by explaining how a language model works and ends by defending a capstone in front of an audience. In between: Python that talks to AI APIs, a working retrieval system, a first reasoning agent, six major projects.
This is not a coding bootcamp. It is a finishing school for a generation that will never know a workplace without AI — and it produces a portfolio that stands on its own.
- What this program is
Learn to think, work, and create with AI across disciplines.
- Who it is for
Students who want strong AI literacy across multiple domains.
- What students build
AI fluency and a defendable portfolio piece
- What students learn
Students move beyond passive AI use and develop real AI-native capability, using AI to research, reason, create, analyse, build, and communicate with greater clarity.
- How students are taught
Small cohorts of 12–16 students, mentor ratio 1 mentor : 7 students. Edison runs on case-based learning, rotating studio roles, prototype-first modules, and year 1 exhibition.
- Final outcome
AI fluency portfolio
The Edison Learning Engine
How a program assembles into capability.
Edison programs are not a sequence of lessons. They are a learning engine, six parts that fit together to produce thinkers who can build with AI, defend their work, and lead.
Assembly
- 01
Curiosity Core
Students begin with questions, not templates. They learn to investigate problems before reaching for tools, the genuine intellectual engine of the program.
- 02
AI Fluency Layer
Students learn how to use AI systems as thinking partners, research assistants, and creative collaborators, across prompts, models, tools, and workflows.
- 03
Builder Studio
Students move from ideas into prototypes, turning abstract concepts into tangible AI-powered outputs through structured, hands-on experimentation.
- 04
Systems Thinking Ring
Students learn to see the whole system: users, constraints, incentives, workflows, ethics, and impact, the connective tissue that turns parts into outcomes.
- 05
Communication Lens
Students learn to present their thinking clearly, explain how their solution works, defend their decisions, and refine in response to critique.
- 06
Final Prototype Engine
Every module ends with a working prototype that students can demonstrate, explain, and improve, the assembled output of the entire learning engine.
The learning pathway, term by term.
Thirty-eight weeks across four terms — roughly 120 hours of structured work. Each term is a full pass through the Edison Method, cut deeper each time.
- T01
Weeks 1–10
Term 1 — Think With AI
How language models work, prompting as reasoning design, and AI-assisted research that separates generated claims from verified fact. The term closes with an evidence-based ethics position paper.
- LLM literacy
- Advanced prompting
- AI research
- AI ethics
Artefact AI research report and ethics paper
- T02
Weeks 11–20
Term 2 — Build With AI
Python from the first line — logic, functions, data — through to a first AI API call and a working AI workflow tool. The term where no-code stops and real code begins.
- Python
- AI APIs
- Data handling
- Error handling
Artefact A coded AI application
- T03
Weeks 21–30
Term 3 — Create With AI
Design thinking, evaluation rubrics, retrieval-augmented generation, and a first ReAct agent — built into the most ambitious project of the year and tested with real users.
- RAG
- AI agents
- Evaluation
- Design thinking
Artefact A user-tested AI product
- T04
Weeks 31–38
Term 4 — Communicate + Exhibit
The capstone: scoped, built across structured sprints, polished, documented, and defended in a five-minute presentation at the Year 1 Exhibition before parents, schools, and mentors.
- Capstone delivery
- Technical communication
- Defending work
Artefact Year 1 capstone and exhibition
Four terms. Six builds. One portfolio that stands alone.
Each term is a deeper pass through the same method. The work a student defends in week 38 would be unrecognisable to them in week 1.
- 01Arrive
A capable AI user who has never written code or built a system.
- 02Build
A student writing Python against AI APIs, building retrieval systems and a first reasoning agent across six real projects.
- 03Exhibit
A confident builder defending a year-long capstone before parents, schools, and mentors.
Students learn with the tools modern builders actually use.
The AI Hypergeneralist toolkit is curated to match the depth and ambition of this program. Students learn to choose, combine, and switch between tools, not memorise a single platform.
Category
AI Assistants & Research
- ChatGPT
- Claude
- Perplexity
- NotebookLM
Category
Productivity & Workflow
- Notion
- Google Workspace
- Microsoft Copilot
Category
Creative Output
- Canva
- Figma
- Runway
Tools evolve. Edison teaches the durable thinking, choosing the right tool, combining tools well, and switching when a better tool emerges.
Edison AI Academy
Founding faculty
Students learn from instructors who combine AI fluency, curriculum design, strategy, and practical implementation experience. Edison AI Academy is built around the belief that young people do not just need to learn tools, they need to learn how to think, build, and lead with them.
Cohorts are deliberately small (12–16 students, mentor ratio 1 mentor : 7 students), so every student is known, stretched, and held to a high standard.
We teach young people how to think, build, and lead with AI, not just how to use it.
Edison AI Academy is a selective program. Fees reflect the small cohort size, mentor ratio, and the depth of the work students complete.
Flexible payment plans may be available for accepted students. Bursaries and scholarships are reviewed individually as part of the admissions conversation.
We frame this as an investment in your child's future readiness, not a transactional fee for content delivery.
AI Hypergeneralist — answers we get asked most.
Not sure if AI Hypergeneralist is the right fit?
Speak with Edison AI Academy about your child's goals, current skill level, and best-fit pathway. We will recommend the right program and the next available cohort.
Three short steps. We'll match you to the right Edison pathway.
Our admissions team reviews every application personally and replies within one business day. The form takes about two minutes.
Your AI Hypergeneralist preference travels with you to the application form — we'll know exactly what you're interested in.