AI Foundations & Intelligent Systems

Establish the core concepts needed to reason about AI and intelligent systems.

• AI, ML and Deep Learning — Distinguish the major terms.
• Intelligent Systems — Understand intelligent behaviour in systems.
• Core AI Components — Recognise recurring AI system components.

Learn to identify and frame problems where AI may create useful value.

• Problem Identification — Turn broad needs into bounded problems.
• AI Suitability — Assess whether AI is appropriate.
• Human-in-the-Loop — Position human judgement appropriately.

Understand major approaches by which systems learn or reason.

• Supervised & Unsupervised Learning — Compare foundational learning approaches.
• Reinforcement Learning — Understand learning through feedback and reward.
• Generative & Rule-Based Approaches — Contrast generative and deterministic approaches.

Connect foundational concepts to real-world AI applications.

• Language Intelligence — Survey language-focused AI.
• Vision & Perception — Survey visual intelligence.
• Prediction & Decision Support — Connect AI to decisions and forecasting.

Understand the lifecycle from problem definition through evaluation and iteration.

• Problem Definition — Define the intended outcome.
• Data-to-Model Workflow — Understand the path from data to model.
• Iteration & Learning — Use evidence to refine the approach.

Understand data quality, preparation and evidence requirements.

• Data Sources & Provenance — Understand where evidence comes from.
• Data Quality & Preparation — Prepare reliable data.
• Evidence Requirements — Identify evidence needed to support claims.

Develop familiarity with models, AI tools and structured experimentation.

• Model Families — Recognise common model families.
• AI Tools — Use tools appropriately in the workflow.
• Experiment Design — Structure meaningful experiments.

Interpret AI outputs and evaluate system performance.

• Evaluation Metrics — Select appropriate evaluation measures.
• Validation & Error Analysis — Interpret model behaviour.
• Communicating Results — Present evidence clearly.

Identify and prioritise an AI use case.

• Stakeholder Need — Translate stakeholder need into a use case.
• Use-Case Prioritisation — Compare candidate AI opportunities.
• Success Criteria — Define measurable success.

Prepare evidence and build an initial solution or analytical prototype.

• Data Preparation — Prepare the working evidence.
• Prototype Workflow — Build the first practical workflow.
• Initial Demonstration — Produce an initial demonstrable result.

Use evidence to improve an applied AI solution.

• Test Design — Design meaningful tests.
• Result Analysis — Interpret test results.
• Iteration Decisions — Use evidence to decide what changes.

Explain an AI solution to technical and non-technical stakeholders.

• Technical Narrative — Explain the technical approach.
• Stakeholder Communication — Communicate implications to stakeholders.
• Documentation — Create a reproducible project record.

Understand the components of an integrated AI-enabled system.

• System Components — Identify core components.
• Architecture Patterns — Recognise common AI system patterns.
• System Boundaries — Define system boundaries.

Explore how AI capabilities connect to applications and workflows.

• APIs & Services — Understand service-based integration.
• Application Integration — Connect AI capabilities to applications.
• Integration Constraints — Recognise operational constraints.

Design for appropriate interaction between people and AI systems.

• Decision Support — Position AI as decision support.
• Human Oversight — Design meaningful oversight.
• User Interaction — Design understandable AI interactions.

Understand operational feedback and system improvement.

• Monitoring — Observe system behaviour.
• Feedback Loops — Use operational feedback.
• Lifecycle Maintenance — Maintain systems over time.

Recognise sources and consequences of bias in AI systems.

• Sources of Bias — Recognise how bias enters systems.
• Fairness Considerations — Consider fairness in context.
• Mitigation Thinking — Consider mitigation approaches.

Understand privacy and security considerations in AI systems.

• Privacy Principles — Recognise privacy obligations.
• Security Risks — Recognise security risks in AI systems.
• Secure AI Practice — Apply basic secure design thinking.

Communicate how AI systems work and where their limits lie.

• Interpretability — Understand interpretability concepts.
• Documentation & Provenance — Document how evidence was produced.
• Communicating Uncertainty — Communicate limits and uncertainty.

Connect responsible AI principles to operational decisions.

• Accountability — Clarify responsibility.
• Governance Controls — Understand governance mechanisms.
• Responsible Deployment — Connect principles to deployment decisions.

Frame the capstone problem and design the proposed AI approach.

• Problem Statement — Produce a precise capstone problem statement.
• Solution Design — Design the proposed AI approach.
• Evaluation Plan — Define how success will be demonstrated.

Develop or demonstrate the proposed solution.

• Implementation Artefact — Produce the core project artefact.
• Workflow Demonstration — Demonstrate the solution in context.
• Technical Record — Record implementation decisions.

Evaluate the solution and assemble defensible evidence.

• Results — Present observed results.
• Limitations & Risks — State limitations honestly.
• Evidence Review — Assemble the evidence record.

Communicate the demonstrated capability and reflect on next steps.

• Project Presentation — Present the completed project.
• Defence & Reflection — Defend decisions and reflect on learning.
• Next Capability Step — Identify the next capability progression.