Why should I study AI and Machine Learning?
Artificial intelligence (AI) and machine learning (ML) are reshaping how we approach challenges in science, business, education, and daily life. From powering voice assistants and smart devices to streamlining logistics and medical diagnostics, AI is becoming embedded in modern society. Governments and industries around the world are investing heavily in these technologies, recognising their potential to drive innovation and productivity. The future job market reflects this momentum. Careers in AI and machine learning are growing rapidly, with employers seeking people who understand how to design, train, and evaluate intelligent systems. Studying AI and ML now puts you ahead of the curve.
Where does AI and Machine Learning lead?
This subject opens pathways to future study in areas such as Computer Science, Digital Technologies, Data Science, Engineering, and Mathematics. It builds practical skills for any career that values critical thinking, innovation, and digital fluency. Students may also pursue research-focused pathways through Honours, Masters, or PhD-level studies in AI or related disciplines. Whether you’re passionate about the environment, healthcare, education, gaming, or entrepreneurship, AI provides the tools to create smart, adaptable solutions.
Roles span industries such as education, environment, sport, defence, creative media, and finance, making AI one of the most versatile and in-demand skill sets for the modern workforce. You could help design intelligent tutoring systems, develop machine learning tools to support sustainable agriculture, or apply AI to accelerate complex research tasks in fields like mathematics, health, or climate science. Career opportunities include roles such as data analyst, AI researcher, software developer, or machine learning engineer, across both industry and research-based settings.
What work will I do in AI and Machine Learning?
Students will explore the applications of Artificial Intelligence (AI) and Machine Learning (ML) across three core domains: Computer Vision, Predictive Analytics, and Natural Language Processing (NLP).
In Semester 1 (A), students will build foundational programming skills, develop computational thinking, and engage with key mathematical concepts that underpin Computer Vision. These skills are then applied to gain an intuitive understanding of image classification and object detection models.
Semester 2 (B) shifts focus to Predictive Analytics, where students learn how predictions can be generated from input data, including language input represented mathematically.
Across each domain, students are introduced to the full ML pipeline, including the training and testing of models to classify images, detect objects within images and make data-driven predictions, including when given text input. They will consider the ethical implications of AI and work both individually and collaboratively to continually develop their digital technology and mathematical competencies while solving problems in real-world contexts.
What do other students think about AI and Machine Learning?
This course will be offered for the first time in 2026.
Where can I find more information about AI and Machine Learning?
For more detail about content and assessment, view the course information:
- Stage 1 (Year 11)