ARTIFICIAL INTELLIGENCE
The AI Board Award
in Applied AI
Build a broad foundation in computational thinking, programming, data, AI models, responsible practice and project planning—then learn how to evaluate AI system outputs in real-world contexts.
RQF Level 3
Qualification level
120 Credits
8 Units
8 Months
Proposed COSMO duration
COURSE INTRODUCTION
Build AI capability from the foundations up.
This is a substantial Level 3 diploma rather than a short introductory workshop. It develops understanding across computational thinking, data processes, programming, responsible practice, project planning, model use, data preparation and AI-output evaluation.
Think Computationally
Break complex problems into structured steps.
Work with Data
Understand how data supports AI systems.
Use AI Responsibly
Apply safe and responsible AI practice.
Evaluate Outputs
Interpret AI results critically.
PROGRAMME CURRICULUM
Eight units that build progressively.
Understanding Computational Thinking
Build the problem-solving mindset that underpins programming, data work and AI systems.
Understanding AI and Data Processes
Understand how AI systems depend on data and structured processes.
Responsible and Safe AI Practices
Explore responsible, ethical and safe approaches to artificial intelligence.
Understanding Programming and Its Use
Build foundational understanding of programming and its role in AI.
AI Project Planning
Learn how to structure and plan an AI-related project.
Using AI Models
Explore how AI models are used within applied AI solutions.
Data Handling and Preparation for AI
Understand data quality, handling and preparation in AI workflows.
Evaluating and Interpreting AI System Outputs
Develop the ability to review and interpret outputs produced by AI systems.
APPLIED AI JOURNEY
Think. Build. Prepare. Evaluate. Apply.
01
Think
Use computational thinking to structure complex problems.
02
Build
03
Prepare
Handle and prepare data for use within AI systems.
04
Evaluate
04
Apply
WHAT YOU'LL DEVELOP
A broader AI skill set than prompt writing alone.
Computational Thinking
Approach problems systematically.
Programming Awareness
Understand programming in applied AI environments.
Data Preparation
Work with concepts behind AI-ready data.
AI Model Use
Understand how models fit into AI solutions.
Output Evaluation
Interpret AI results critically.
Responsible AI
Apply safe and responsible practices.
Project Planning
Structure an AI project around clear objectives.
Applied Problem Solving
Connect technical concepts to practical AI use cases.