ICDL AI Essentials study plan
ICDL AI Essentials · Give each week a clear focus.
8 weeks · About 6 hours per week
What is AI?
Define AI, machine learning, deep learning, and algorithm in your own words, and state one difference between each pair
Classify 12 everyday products as rule-based software, narrow AI, or not AI, and justify each label in one sentence
Distinguish narrow AI from general AI and give two reasons why current consumer tools are narrow
Identify the three components of an AI system (data, model, output) in four real product descriptions
Your checkpoint
A one-page glossary of 15 core terms, each with a definition under 25 words and one real-world example, checked against course material with zero factual errors
What is AI?
Compare supervised, unsupervised, and reinforcement learning, listing the type of data and feedback each needs
Trace how training data becomes a prediction by describing the steps of training, testing, and deployment for one example
Explain overfitting and data quality problems in plain language, with one example of each
Evaluate three claims about what an AI system can do and mark each as supported, overstated, or false
Your checkpoint
A comparison table covering three learning types with two use cases each, plus a 5-question self-quiz you write and score at least 4/5 on
AI ethics, safety and risks
Identify at least six sources of bias in AI, from data collection through deployment
Analyze one documented case of AI harm by naming the affected group, the mechanism, and the missed safeguard
Distinguish fairness, accountability, and transparency, and describe one practical test for each
Argue both for and against a stated AI use case in 150 words each, using ethical reasoning rather than opinion
Your checkpoint
A 400-word bias case analysis that names the data-stage, model-stage, and deployment-stage risks and proposes one mitigation for each
AI ethics, safety and risks
Describe privacy and security risks specific to AI, including data collection, inference, and model misuse
Outline what informed consent and purpose limitation mean for a system that trains on user data
Match five regulatory or governance principles to concrete organizational controls
Rank ten AI risks by likelihood and impact and assign a realistic mitigation to each
Your checkpoint
A risk register of 10 AI risks with likelihood, impact, mitigation, and responsible role, plus a score of at least 80% on a 20-question mixed quiz covering domains 1 and 4
Milestones
Week 2: Explain the difference between AI, machine learning, and deep learning to a non-expert in under 3 minutes, then answer three follow-up questions without notes
Week 4: Score at least 80% on a closed-book 20-question quiz covering domains 1 and 4, and log every missed item with a corrected explanation
Week 6: Present three everyday AI systems to a non-expert, correctly naming the data used, the task performed, and one limitation for each, assessed against a 5-point checklist
Week 8: Score at least 85% on a timed 40-question mock exam spanning all four domains, with every error reworked in writing