AI Literacy Foundations

What you will be able to do

• Explain training and inference in plain language.
• Separate demonstrated capability from marketing claims.
• Evaluate AI outputs using evidence, uncertainty, and fit-for-purpose tests.

Prerequisites: None.

Course modules

1. 1. What AI is

   Outcome: Distinguish AI, machine learning, and ordinary software.

   Practice: Classify five everyday systems and explain which definition of AI you used.

   Competencies: AI concepts and boundaries

   what is ai →machine learning basics →

2. 2. How systems learn

   Outcome: Describe the role of examples, objectives, and generalization.

   Practice: Diagnose why a model can perform well in testing but fail for a new population.

   Competencies: Data, training, and generalization

   how ai learns →machine learning basics →ai training →

3. 3. Models and inference

   Outcome: Explain what happens when a trained model produces an output.

   Practice: Trace one user request from input through model output and human review.

   Competencies: Models, inference, and limitations

   ai models →ai inference →neural networks →

4. 4. Evidence and uncertainty

   Outcome: Check claims, benchmarks, and confident-sounding outputs.

   Practice: Create a claim ledger for one AI product announcement.

   Competencies: Evaluation and benchmark literacy · Uncertainty and confidence calibration · Source and claim verification

   ai benchmarks →ai hallucinations →ai myths →