AI & Programming Foundations
AI Literacy and Responsible Practice
F03 · Foundation
Entry requirements
No programming prerequisite
What 24 hours means
- Lessons
- 18 h
- Capstone
- 4 h
- Assessment
- 1 h
- Remediation
- 1 h
Syllabus
Four modules, twelve lessons. Every lesson states what it covers and the evidence you produce for it.
Module 1
Understanding AI
AI system types · Data and model lifecycle · Prompts and task boundaries
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F03.01 · 90 min
AI system types
Compare predictive, generative and robotic systems using everyday examples.
Evidence: Six examples categorised with realistic capabilities and limitations.
F03.02 · 90 min
Data and model lifecycle
Trace collection, preparation, training, deployment and monitoring.
Evidence: A lifecycle map identifying a responsible owner at each stage.
F03.03 · 90 min
Prompts and task boundaries
Module 2
Using AI well
Checking generated claims · Bias and representativeness · Privacy and confidentiality
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F03.04 · 90 min
Checking generated claims
Verify a generated answer against a supplied source pack.
Module 3
Risk and oversight
Copyright and provenance awareness · Human oversight · AI security awareness
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F03.07 · 90 min
Copyright and provenance awareness
Record source, licence status and permitted use for supplied assets.
Module 4
Adoption planning
Evaluating usefulness · Communicating limitations · Adoption roadmap
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F03.10 · 90 min
Evaluating usefulness
Define quality, time and cost measures for a proposed AI pilot.
Evidence:
Capstone
Prepare an AI adoption proposal for a college department with a workflow map, risk register, evaluation plan and human review procedure.
Lab environment
Browser exercises
Lab infrastructure is not yet provisioned, so this describes the environment the course is designed for rather than one you can launch today.