Science focus: concepts are explained with scientific and research-oriented examples. Questions are optional and never block the next unit.
TOPIC 1

Generic AI Applications

AI is applied in education, healthcare, agriculture, governance, business analytics, finance, robotics, media and entertainment.

Science example
Scientific learners should compare where prediction, language, vision or automation is used in each domain.
Quick revision: Generic AI Applications is a key concept to be able to define, explain and apply in a simple scientific context.
TOPIC 2

AI in Climate Modelling

AI can learn patterns in climate and weather datasets, support parameterisation, downscaling, forecasting components and rapid analysis of large simulations. It complements, rather than replaces, physical climate science.

Science example
Analyse historical temperature, rainfall and atmospheric variables to identify patterns.
Quick revision: AI in Climate Modelling is a key concept to be able to define, explain and apply in a simple scientific context.
TOPIC 3

Healthcare Diagnostics

AI can assist analysis of medical images, signals and clinical data. Outputs require clinical validation and qualified human oversight.

Science example
Image models may flag suspicious regions for clinician review.
Quick revision: Healthcare Diagnostics is a key concept to be able to define, explain and apply in a simple scientific context.
TOPIC 4

Bioinformatics

AI and ML can help analyse biological sequences, molecular data, gene-expression data and other high-dimensional biological datasets.

Science example
Classify biological samples from measured molecular features.
Quick revision: Bioinformatics is a key concept to be able to define, explain and apply in a simple scientific context.
TOPIC 5

Environmental Monitoring

AI can combine satellite imagery, camera data and sensor measurements to detect environmental changes or anomalies.

Science example
Detect land-cover change or unusual air-quality readings.
Quick revision: Environmental Monitoring is a key concept to be able to define, explain and apply in a simple scientific context.
TOPIC 6

Scientific Data Analysis

AI can support classification, regression, clustering, anomaly detection and pattern discovery in experimental or observational datasets.

Science example
Cluster samples, predict measurements or detect unusual sensor readings.
Quick revision: Scientific Data Analysis is a key concept to be able to define, explain and apply in a simple scientific context.
TOPIC 7

Research Automation

AI tools can assist literature discovery, summarisation, coding, data preparation, documentation and workflow automation. Researchers remain responsible for verification, reproducibility and citations.

Science example
Use an AI assistant to draft analysis code, then test and validate every step.
Quick revision: Research Automation is a key concept to be able to define, explain and apply in a simple scientific context.
TOPIC 8

Hands-on AI Tools

The syllabus names ChatGPT, Gemini, Copilot, Claude, Canva AI, Runway ML, Google Teachable Machine, DALL·E, Adobe Firefly, Pictory and Google AI Studio. Students should learn appropriate use, prompting, verification and limitations.

Science example
Use Teachable Machine for a no-code classification demo; use language tools to explain results while checking scientific accuracy.
Quick revision: Hands-on AI Tools is a key concept to be able to define, explain and apply in a simple scientific context.

End of Unit 3

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