Applied AI Literacy for Work & Career
AI literacy has moved from nice-to-have to a baseline employability skill: employers increasingly expect entry-level hires to use generative-AI tools quickly, safely, and critically. This module trains exactly that. Learners practise selecting the right AI tool for a task, engineering and iteratively refining prompts, detecting hallucinations and bias before they cause harm, applying data-privacy (RA 10173) and disclosure rules, and integrating AI into a personal career workflow — job search, learning, and professional communication. Every chapter ends in a doable, gradeable task, and the module closes with a portfolio capstone: a real AI-assisted deliverable with its documented, verified, honestly-disclosed process. The standard throughout: AI-assisted, human-approved.
Study This Module
Read through the full module content — five chapters of detailed material with examples, key takeaways, and practice points. Built for self-paced learning and review.
Open Study Material →Option 2Take the Test
40 multiple-choice questions across the five units of competency (8 per unit). Per-unit breakdown at the end, retake as many times as you want, scores saved to your dashboard.
Start Test →Option 3Performance Tasks
8 graded hands-on tasks — real artifacts scored against rubrics by a mentor. The knowledge test proves you know it; these prove you can do it.
Open Tasks →Learning outcomes
- Select and apply appropriate generative-AI tools to complete authentic workplace tasks.
- Engineer prompts with context, role, task, and format — and iteratively refine them to improve output.
- Critically evaluate AI output for accuracy, bias, and hallucination, verifying claims against reliable sources.
- Apply responsible AI practices: data privacy (RA 10173), appropriate disclosure, and workplace integrity.
- Recognize and mitigate over-reliance risks while keeping personal skills sharp.
- Integrate AI tools into a documented personal career and productivity workflow, assembled as a portfolio artifact.
Module content
Understanding AI & Choosing the Right Tool
What generative AI is (prediction, not truth — plausible is not the same as true), what it is good and bad at, tool families, and the three-question habit for matching tool to task: output type, data sensitivity, cost of error. The skilled-user mindset: AI-assisted, human-approved.
Prompting & Iteration
The C-R-T-F prompt anatomy (Context, Role, Task, Format), why prompt quality decides output quality, and the professional loop: prompt → critique → revise → compare. Building a prompt-and-refine log as evidence of skill.
Verifying AI — Accuracy, Bias & Hallucination
Hallucinations as confident invention, bias as the tilt you don't see, and the verification routine: sort claims from wording, check reliable sources, apply the two-source rule for high stakes, confirm citations exist, and calibrate scrutiny to consequences.
Responsible & Safe AI
Data privacy under the Data Privacy Act (RA 10173) — anonymise by default, never feed personal or company-confidential data into external tools. Honest disclosure of AI assistance, the line between legitimate help and fraud, and guarding against over-reliance.
AI in Your Career Workflow — Capstone
Chaining the skills into designed routines: the job-search engine, the learning accelerator, and the communication desk. Building a personal prompt library, then producing the capstone: a real AI-assisted deliverable with its documented workflow, iterations, verification record, and disclosure.