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Artificial Intelligence · beginner

AI Foundations & Responsible AI

Understand AI systems, data, evaluation, bias, privacy, and accountable human review. Build a practical responsible-use playbook.

With Admin

beginner9 lessons · 3 sections5–7 hours of reading and practice, plus 2–3 hours for the project; estimates exclude video.

THE TAKEAWAYS

What you’ll learn

  • Distinguish AI, machine learning, deep learning, and generative applications using examples.
  • Explain why a model's fluent output is not proof of truth or universal competence.
  • Trace text, image, and audio processing and locate an extraction error before summarization.
  • Write prompts with a task, evidence, constraints, and a checkable output.
  • Separate source-supported claims from overgeneralization and unsupported conclusions.
  • Map a personal workflow with inputs, review points, and a final human decision.
  • Minimize sensitive data and check permissions before sharing or reusing material.
  • Analyze performance gaps and define meaningful human oversight without inventing causes.
  • Create and test a responsible-use playbook with ownership and correction routes.

Is this for you?

AI users, educators, managers, and aspiring builders who want to evaluate systems and establish sensible review practices.

Before you start

  • Complete AI for Everyone or demonstrate equivalent prompting and verification habits.
  • Comfort with percentages and a calculator; no programming required.
  • Bring a fictional classroom or workplace workflow to analyze.

PUT IT TO WORK

What you’ll build

Responsible-use playbook. Client scenario: a small academy team wants to use AI for announcements and learner feedback. Deliverable: two-page playbook plus three scenario decisions.

Trace how information moves through an AI system, distinguish rules from learned behavior, and question claims about quality. Work through extraction errors, small-sample evidence, performance gaps, information sharing, and meaningful review. Your final playbook defines permitted tasks, restricted inputs, named owners, escalation, and correction steps. This is practical risk literacy, not a promise of legal compliance or universal safety.

Course curriculum

SECTION 1Systems, data, and learned behavior3 lessons
  1. AI, ML, deep learning, and generative AIReading
  2. What models can and cannot doReading
  3. Multimodal AI: text, images, audio, and videoReading
SECTION 2Evidence, limitations, and responsible decisions3 lessons
  1. Prompt design that produces useful workReading
  2. Verification and source checkingReading
  3. Build your personal AI workflowReading
SECTION 3Human oversight and a responsible-use playbook3 lessons
  1. Privacy, copyright, and sensitive dataReading
  2. Bias, fairness, and human oversightReading
  3. Capstone: an AI use policy for your workReading