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Government AI Programme

A scoped training format for public-sector teams that need safe, reviewable AI workflows.

Beginner to team-readyScoped cohortPublic-sector teams
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Overview

What this programme is

The Government AI Programme adapts the Academy learning spine for teams that need cautious, reviewable use of AI in public-service contexts.

The emphasis is on safe use, controlled examples, clear review checkpoints, and human judgement. Exercises use sample, public, sanitized, or approved data only.

Who it is for

Built for the right context

  • Teams exploring practical AI literacy for public-service work.
  • Officers who need safer drafting, summarising, research, and briefing habits.
  • Groups that need a training brief scoped before delivery.

Objectives

What learners should leave with

  • Map appropriate and inappropriate AI use cases.
  • Draft reviewable prompts and outputs for routine work.
  • Build a simple workflow map with human checkpoints.
  • Review public-facing or internal sample outputs before use.

Module outline

Seven modules, summary only

M-01

AI Basics

Understand what AI can and cannot do before using it for real work.

  • Common AI uses
  • Human judgement
  • Safe first tasks
Safe-use checklist
M-02

Prompting

Give clearer instructions and review the result instead of accepting the first answer.

  • Prompt structure
  • Context and constraints
  • Review loops
Reusable prompt template
M-03

Workflows

Break a repeated task into steps where AI can help without taking over.

  • Task mapping
  • Drafting support
  • Human checkpoints
Workflow map
M-04

Research

Use AI to explore and compare information while checking sources and missing context.

  • Question framing
  • Source comparison
  • Briefing notes
One-page comparison brief
M-05

Build Practice

Turn the learning into a visible output, prototype, worksheet, tracker, or build-lab path.

  • Output selection
  • Build planning
  • First useful draft
Practical build artifact
M-06

Review And Safety

Check AI output for mistakes, privacy risk, weak claims, tone issues, and missing review.

  • Accuracy checks
  • Privacy awareness
  • Share-readiness review
Review checklist
M-07

Final Project

Package one useful result and explain what was made, how it was checked, and what comes next.

  • Project story
  • Evidence of work
  • Next improvement
Final project story

Delivery

How it runs

  • Delivered by request after scope, audience, and data boundaries are agreed.
  • Exercises avoid private or sensitive data unless formally approved by the organisation.
  • Outputs are treated as training artifacts, not production systems.

Next action

Review the path, then take the next step.

Public pages stay at overview level. Academy learning surfaces hold the actual lessons, labs, and resources.

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