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AI Literacy · AI-204

Professional AI Literacy

Build a complete working understanding of artificial intelligence, machine learning, generative systems, benefits, risks, and responsible adoption.

LEVELBeginner to intermediate
LESSON TIMEAbout 12 hours
FORMAT16 self-paced lessons
PATHWAYCDAP required

Practice and the final project time is additional to the lesson estimate.

Check if it fits youSee the full syllabus

IS THIS COURSE FOR YOU?

What to expect from Professional AI Literacy

Professionals seeking a comprehensive foundation for responsible AI adoption.

What you will learn

  • Explain core AI and machine-learning concepts
  • Evaluate benefits, limitations, and risks
  • Develop a human-supervised adoption plan

LESSON OUTLINE SAMPLE

What artificial intelligence is

What artificial intelligence is is part of Foundations and history of AI. Define AI, distinguish it from human intelligence, and explain the milestones that shaped modern systems.

This sample shows the lesson goals and selected takeaways. Enroll to access the complete lesson notes, activities, and assignments.

What this lesson covers

  • Explain What artificial intelligence is accurately in your own words.
  • Apply the lesson to a realistic personal, education, or workplace situation.
  • Use evidence, safety checks, and human judgment to evaluate the result.

Selected takeaways

  • Define AI, distinguish it from human intelligence, and explain the milestones that shaped modern systems.
  • Good digital work combines practical skill with evidence and responsible judgment.
  • Document what you checked, changed, and still need to learn.

COMPLETE SYLLABUS

5 modules · 16 lessons

16 follow-up assignments · 5 assessments
01
Foundations and history of AIDefine AI, distinguish it from human intelligence, and explain the milestones that shaped modern systems.
4 lessons
  1. 1.1What artificial intelligence is
  2. 1.2Human and machine intelligence
  3. 1.3AI milestones and expert systems
  4. 1.4AI in everyday life
02
How machines learnExplain how data, algorithms, models, training, evaluation, and prediction work together.
3 lessons
  1. 2.1Training data algorithms and models
  2. 2.2The machine learning lifecycle
  3. 2.3Supervised unsupervised and reinforcement learning
03
Generative AI systemsUnderstand foundation models and how systems generate text, images, audio, and other media.
3 lessons
  1. 3.1How generative AI creates content
  2. 3.2Large language and foundation models
  3. 3.3Image generation and diffusion models
04
Benefits risks and evaluationJudge productivity, accessibility, bias, hallucination, privacy, and deepfake risks using evidence.
3 lessons
  1. 4.1AI productivity accessibility and personalization
  2. 4.2Bias hallucinations and false confidence
  3. 4.3Deepfakes privacy and security
05
Responsible adoptionApply fairness, transparency, inclusion, reliability, privacy, and accountability to an adoption plan.
3 lessons
  1. 5.1Responsible AI principles
  2. 5.2Human oversight and accountability
  3. 5.3AI adoption capstone plan

FINAL COURSE PROJECT

Professional AI Adoption Brief

Submit an adoption brief covering the use case, system boundaries, data, people, controls, measures of success and implementation roadmap, supporting evidence of your process, and a concise reflection. Do not include passwords, private records or confidential information.

Approximately 4 hours · Instructor reviewed

How success is evaluated

  • The project responds directly to the stated audience, purpose and scenario.
  • The submitted artifact demonstrates skills from multiple modules rather than one isolated lesson.
  • The learner provides evidence of testing, verification and revision.
  • The work addresses business value, feasibility, risk governance, workforce impact and measurable outcomes.
  • The explanation is original, clear, well organized and suitable for instructor review.

GRADING AND CERTIFICATE POLICY

Progress by demonstrating mastery

This course is completed through verified evidence of learning—not by averaging every activity into a traditional grade. You can use feedback, revise your work, and resubmit when an instructor requests changes.

  1. Complete the lessonsFinish the required lessons and applied practice in the published course release.
  2. Pass knowledge checksMeet each assessment’s published passing score. Attempt limits and retry guidance are shown before starting.
  3. Demonstrate practical skillsSubmit required assignments using safe, non-confidential evidence and receive instructor approval.
  4. Pass the final projectComplete the course-specific project and satisfy its published marking rubric. Revise and resubmit when requested.
  5. Unlock the certificateThe certificate becomes eligible only after every required lesson, assessment, assignment approval, and final-project requirement is verified.
How grading decisions are made
  • Courses use mastery-based completion rather than an averaged GPA or assignment-category percentage.
  • Practice activities help learners improve and do not reduce a course grade.
  • The rubric displayed with the active assignment in My Learning is authoritative.
  • Accuracy, safety, privacy, accessibility, or ethical failures may require revision even when other work is strong.
  • Instructor feedback and earlier submissions remain visible when work is revised and resubmitted.

Published assessment requirements: Foundations and history of AI knowledge check: 70% passing score, up to 3 attempts · How machines learn knowledge check: 70% passing score, up to 3 attempts · Generative AI systems knowledge check: 70% passing score, up to 3 attempts · Benefits risks and evaluation knowledge check: 70% passing score, up to 3 attempts · Responsible adoption knowledge check: 70% passing score, up to 3 attempts

Ready to begin?

Study at your pace and keep access to your learning record and feedback.