AI Hypergeneralist
Combine disciplines. Move fluidly across technology, business, creativity, and research.
For ambitious students learning to connect ideas across disciplines and use AI to research, build, analyse, and communicate faster.
- Age range
- 14–22
- Duration
- 32 weeks
- Format
- Hybrid
- Level
- Intermediate
- Cohort
- 8–12 students
- Outcome
- Multimodal portfolio project
Cross-disciplinary fluency for ambitious learners.
For ambitious students learning to connect ideas across disciplines and use AI to research, build, analyse, and communicate faster.
- What this program is
Cross-disciplinary fluency for ambitious learners.
- Who it is for
Students with wide interests who want range, adaptability, and creative intelligence.
- What students build
Portfolio-worthy multimodal AI project
- What students learn
Medium-length cycles with growing autonomy. Students lead their own build sprints, defend their decisions in studio critique, and ship a portfolio-worthy multimodal artefact.
- How students are taught
Small cohorts of 8–12 students, mentor ratio 1 mentor : 6 students. Edison runs on case-based learning, rotating studio roles, prototype-first modules, and public capstone defence.
- Final outcome
Multimodal portfolio project
Every Edison program runs on the same five-mode cycle — Inquiry → Explore → Build → Critique → Exhibit — with depth and autonomy calibrated to the level. AI Hypergeneralist is calibrated for the intermediate stage.
Students are mentored in small cohorts, defend their work in studio critique, and exit each module with a portfolio-ready artefact they can demonstrate, explain, and improve.
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
Curiosity Core
- 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, module by module.
Each module builds on the last. Students complete short cycles of inquiry, build, and critique, ending with a final showcase artefact.
- M01
Module 01
Workflow Design
Compose multi-step AI workflows around a real problem.
- M02
Module 02
Multimodal Build
Combine text, image, audio, and data in a single artefact.
- M03
Module 03
Prototype Sprints
Two-week build cycles with feedback and iteration.
- M04
Module 04
Studio Critique
Run and receive rigorous peer reviews; defend choices.
- M05
Module 05
Portfolio Defence
Present, defend, and document the project for portfolio.
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
Reasoning & Research
- ChatGPT
- Claude
- Perplexity
- NotebookLM
Category
Design & Content
- Figma
- Canva
- Midjourney
- Runway
Category
Automation
- Make
- Zapier
- Airtable
- Notion
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 (8–12 students, mentor ratio 1 mentor : 6 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.