Learning pathways

From AI interest to practical capability.

UpwizeAI pathways are being shaped as modular routes. Each level builds capability through context, applied practice and evidence, so learners can see why they are learning and where it could take them.

Find your best starting point Explore the map

The pathway map

Choose a route by capability, not course title alone.

The map below keeps the current five-level structure, but clarifies what each route is for and the kinds of roles it may support.

Level 1

Foundations

For learners who need confidence, language and judgement before deeper application.

Level 1A · Four weeks live online

AI Foundations and Prompt Engineering

Learn how AI works, how to ask better questions, and how to use tools with clearer judgement.

Understand AI, ML, LLMs and generative AI in plain language.
Build repeatable prompting habits for research, writing, analysis and communication.
Know how to check outputs, spot weak answers and decide when human review is needed.

Outcome: a practical prompt/playbook set and a clearer next-step recommendation.

Level 1B · Five weeks live online

Applied Generative AI Fundamentals

Use generative AI across text, documents, data and workflow tasks while building a first practical project.

Apply AI to a real task rather than isolated tool demos.
Compare tools and techniques across content, data, workflow and decision support.
Turn a simple use case into a documented mini-project.

Outcome: a first AI-assisted workflow and evidence you can reuse in a portfolio or team conversation.

Level 2

Applied

For learners ready to build AI-supported products, services or internal workflows.

Founding cohort · 8 weeks live online

Generative AI Developer Cohort

Build, evaluate, optimise and deploy a production-minded GenAI application through eight guided build cycles.

Create grounded RAG systems and bounded agent workflows.
Measure quality, latency, memory and cost before choosing how to optimise.
Deploy an observable capstone with safety, security and human review in view.

Outcome: a deployed capstone, evaluation suite and technical decision record you can defend.

Generative AI for Digital Transformation

Translate AI capability into business workflows, experiments, operating models and measurable value.

Identify where AI could improve a workflow, decision or customer experience.
Frame use cases with value, risk, adoption and operating model in view.
Design experiments that produce learning, not just impressive demos.

Outcome: a use-case canvas, pilot brief and measurable adoption hypothesis.

Level 3

Research and Innovation

For learners who want to test, evaluate and improve AI systems with evidence.

Applied AI Research and Evals

Learn evaluation design, benchmarking and system analysis so decisions are based on observed behaviour.

Evaluation foundations: define what good looks like, create task sets, scoring rubrics, edge cases and failure-mode checks.
Benchmarking: compare prompts, models, retrieval choices or workflow versions under consistent conditions.
Evidence-led iteration: use findings to improve prompts, guardrails, retrieval and workflow design, then retest.

Outcome: an evaluation plan, benchmark notes and a findings report that supports product, governance or research decisions.

The Research Lab Sprint

Collaborate around focused research problems, experiments and applied innovation challenges.

Frame a research question clearly enough to test within a short sprint.
Collect evidence through experiments, comparisons, observation and structured critique.
Turn uncertain findings into a concise recommendation for what to explore or build next.

Outcome: a sprint brief, experiment log and practical research summary.

Level 4

Leadership

For people shaping AI adoption, product direction, governance or organisational change.

AI Product Strategy and Leadership

Frame AI opportunities, define responsible experiments, guide teams and connect adoption to value.

Identify high-potential AI opportunities using customer, workflow and business context.
Define experiments with value, risk, feasibility and adoption signals.
Translate technical possibility into a roadmap or decision brief stakeholders can act on.

Outcome: an AI opportunity map, pilot hypothesis and value/risk narrative.

AI Governance and Policy Leadership

Shape responsible adoption through risk thinking, policy design, evaluation and operating controls.

Understand core AI risk patterns across privacy, accuracy, safety, bias, misuse and accountability.
Connect governance controls to real workflows rather than generic policy language.
Define review, escalation and evidence practices for responsible adoption.

Outcome: a practical governance checklist and responsible-use operating model for one use case.

Level 5

Educator and Ecosystem

For people preparing to teach, mentor or scale AI literacy inside communities and organisations.

AI Educator and Ecosystem Leadership

Design applied learning experiences, facilitate AI confidence and support capability building at scale.

Design learning outcomes, activities and assessment tasks that prove applied capability.
Facilitate AI learning with confidence, safety and human judgement in the room.
Create support loops through workshops, mentors, communities or internal champions.

Outcome: a workshop/session plan, learner activity set and feedback model.

Not sure?

Let the pathway start with your context.

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