AI Foundations Learner Workbook

Use this resource with the related module activity and keep the result for your capstone preparation.

This workbook keeps your decisions, practice, evidence, revisions, and capstone preparation in one place. Do not enter real personal, confidential, employer, client, financial, health, legal, education, authentication, or security-sensitive information. Use supplied, public, fictional, or safely prepared synthetic material.

How to use it

1. Complete the ungraded diagnostic.
2. Keep one short record for each module checkpoint.
3. Link or attach the reusable resource used for that checkpoint.
4. Record feedback and one revision where needed.
5. Carry the final records into the capstone submission.

Course progress

Diagnostic reflection

• Concepts already familiar:
• Concepts I should study carefully:
• One misconception I changed after feedback:

The diagnostic does not block entry or rank you.

Module 1 record: classify the system

• System or scenario:
• Observable inputs, mechanism, output, and objective:
• Classification and evidence:
• What remains unknown:
• Resource: ai-system-classification-checklist.md

Module 2 record: explain generated output

• What the prompt supplied:
• What the model generated:
• Added search, tools, memory, or actions:
• Supported and unsupported claims:
• Verification next step:
• Resource: generated-output-explanation-card.md

Module 3 record: choose the method

• Task pattern:
• Evidence and acceptable error:
• Input classification:
• Chosen method and rejected alternatives:
• Human authority and stop condition:
• Resource: task-suitability-checklist.md

Module 4 record: specify the work

• Outcome and audience:
• Context and authorised sources:
• Constraints, boundaries, and missing-information rule:
• Output format and review criteria:
• Workflow stages and approval:
• Defect found and targeted revision:
• Resource: task-specification-template.md

Module 5 record: verify the output

• Material claim or requirement:
• Evidence or reproducible method:
• Result: Supported / Contradicted / Partly supported / Unresolved
• Change made:
• Decision: Accept / Revise / Verify / Reject
• Remaining limitation:
• Resource: output-review-rubric-and-evidence-log.md

Module 6 record: control or stop

• Affected people and possible harm:
• Minimized and permitted data:
• Untrusted-content, tool, and action risks:
• Fairness and accessibility checks:
• Rights or specialist questions:
• Named owners, escalation, and stop condition:
• Resource: responsible-use-decision-worksheet.md

Capstone preparation

• Chosen pathway:
• Task and intended user:
• Safe source material:
• Autonomy level:
• Human decision:
• Most important verification:
• Most important safeguard:
• Planned failure tests:

Use the Module 7 capstone brief, pathway guide, submission template, rubric, worked example, and reflection prompts.

Reusable revision log

Evidence and source log

Final course reflection

1. Which decision method changed how you use AI most?
2. Which error or failed test taught you the most?
3. What information will you no longer enter without checking permission?
4. Which claims will always need independent verification?
5. What human decision must remain outside your workflow?
6. What is your clearest stop condition?
7. What would trigger a future review of your workflow?

This workbook records learning evidence. It is not a certificate, identity verification, professional approval, or legal-compliance record.