AI-01 · Foundation · ENGLISH · PLANNED
Begin with a baseline, trace a model, measure errors and improve it.
Study time is an initial activity estimate, excluding extra reading, revision and extended laboratory or project work. Videos and question bodies have not been produced by adding this outline.
Complete these courses or demonstrate equivalent knowledge. These are recommended prerequisites, not enforced enrolment locks.
YouTube playlist: ARK Artificial Intelligence | AI Literacy: From Curiosity to Understanding
Playlist title reserved in the plan; no YouTube playlist or upload has been created yet.
Sign in to save this learning pathPractical outcome: Sort fictional product features into rules predictions and generated outputs.
Explain what artificial intelligence means using a visual and one worked real-world example. Context: Sort fictional product features into rules predictions and generated outputs. End with a prediction or check-for-understanding.
Explain rules versus learned patterns using a visual and one worked real-world example. Context: Sort fictional product features into rules predictions and generated outputs. End with a prediction or check-for-understanding.
Explain prediction using a visual and one worked real-world example. Context: Sort fictional product features into rules predictions and generated outputs. End with a prediction or check-for-understanding.
Explain classification using a visual and one worked real-world example. Context: Sort fictional product features into rules predictions and generated outputs. End with a prediction or check-for-understanding.
Use the first concepts from this section. Sort fictional product features into rules predictions and generated outputs. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain generation using a visual and one worked real-world example. Context: Sort fictional product features into rules predictions and generated outputs. End with a prediction or check-for-understanding.
Explain training versus using a model using a visual and one worked real-world example. Context: Sort fictional product features into rules predictions and generated outputs. End with a prediction or check-for-understanding.
Explain models versus databases using a visual and one worked real-world example. Context: Sort fictional product features into rules predictions and generated outputs. End with a prediction or check-for-understanding.
Explain human decisions using a visual and one worked real-world example. Context: Sort fictional product features into rules predictions and generated outputs. End with a prediction or check-for-understanding.
Sort fictional product features into rules predictions and generated outputs. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering What artificial intelligence means, Rules versus learned patterns, Prediction and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Examine a toy fruit classifier with deliberately missing examples.
Explain examples and labels using a visual and one worked real-world example. Context: Examine a toy fruit classifier with deliberately missing examples. End with a prediction or check-for-understanding.
Explain features using a visual and one worked real-world example. Context: Examine a toy fruit classifier with deliberately missing examples. End with a prediction or check-for-understanding.
Explain training data using a visual and one worked real-world example. Context: Examine a toy fruit classifier with deliberately missing examples. End with a prediction or check-for-understanding.
Explain test data using a visual and one worked real-world example. Context: Examine a toy fruit classifier with deliberately missing examples. End with a prediction or check-for-understanding.
Use the first concepts from this section. Examine a toy fruit classifier with deliberately missing examples. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain pattern learning using a visual and one worked real-world example. Context: Examine a toy fruit classifier with deliberately missing examples. End with a prediction or check-for-understanding.
Explain overfitting intuitively using a visual and one worked real-world example. Context: Examine a toy fruit classifier with deliberately missing examples. End with a prediction or check-for-understanding.
Explain biased samples using a visual and one worked real-world example. Context: Examine a toy fruit classifier with deliberately missing examples. End with a prediction or check-for-understanding.
Explain uncertain answers using a visual and one worked real-world example. Context: Examine a toy fruit classifier with deliberately missing examples. End with a prediction or check-for-understanding.
Examine a toy fruit classifier with deliberately missing examples. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Examples and labels, Features, Training data and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Verify an AI-written fictional study summary against supplied source facts.
Explain writing a clear task using a visual and one worked real-world example. Context: Verify an AI-written fictional study summary against supplied source facts. End with a prediction or check-for-understanding.
Explain providing context using a visual and one worked real-world example. Context: Verify an AI-written fictional study summary against supplied source facts. End with a prediction or check-for-understanding.
Explain asking for evidence using a visual and one worked real-world example. Context: Verify an AI-written fictional study summary against supplied source facts. End with a prediction or check-for-understanding.
Explain checking factual claims using a visual and one worked real-world example. Context: Verify an AI-written fictional study summary against supplied source facts. End with a prediction or check-for-understanding.
Use the first concepts from this section. Verify an AI-written fictional study summary against supplied source facts. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain hallucinations using a visual and one worked real-world example. Context: Verify an AI-written fictional study summary against supplied source facts. End with a prediction or check-for-understanding.
Explain personal-data boundaries using a visual and one worked real-world example. Context: Verify an AI-written fictional study summary against supplied source facts. End with a prediction or check-for-understanding.
Explain human review using a visual and one worked real-world example. Context: Verify an AI-written fictional study summary against supplied source facts. End with a prediction or check-for-understanding.
Explain choosing when not to use ai using a visual and one worked real-world example. Context: Verify an AI-written fictional study summary against supplied source facts. End with a prediction or check-for-understanding.
Verify an AI-written fictional study summary against supplied source facts. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Writing a clear task, Providing context, Asking for evidence and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Verify an AI-written fictional study summary against supplied source facts. Submit an original worked artifact, evidence or tests, assumptions and a reflection. Rubric: correctness, method, practical usefulness, reproducibility and communication. Self-reviewed, not certified.
6–12 balanced questions sampling every section of AI Literacy: From Curiosity to Understanding, including interpretation, calculation or reasoning, a misconception and a new application. Passing is 70%; review errors before retrying.
Data experiments, evaluations, model tracing and reproducible projects. Guided work leads to an independent attempt, with a diagram, explanation, trace, calculation or tested artifact. Question bodies are generated when opened in your private learning path. Programming submissions are stored as text; the platform does not execute submitted code.
Use original or appropriately licensed datasets. Practical science activities use models or supplied data unless a qualified supervisor has reviewed a physical experiment.
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