Introducing AIR: Artificial Intelligence and Robotics at ARCSA
For almost a decade, the Adelaide Robotics and Computer Science Academy has grown steadily, deliberately, and thoughtfully.
We began with foundational robotics, introduced Python programming, expanded into drones and computer vision, and gradually moved closer to how robotics and artificial intelligence are taught at the university level.
In 2026, ARCSA enters a new phase.
We are proud to introduce Artificial Intelligence and Robotics (AIR), our most advanced program to date, designed to prepare high school students for future studies and careers in robotics, artificial intelligence, and related fields.
A University-Level Program for High School Students
AIR is a university-level program taught to high school students.
It does not attempt to replace tertiary education. Instead, it prepares students for it by introducing them, carefully and progressively, to the same tools, concepts, and ways of thinking they will later encounter at university.
To our knowledge, no other academy in Adelaide offers a structured program of this depth. Likewise, teaching ROS 2 (Robot Operating System 2) to high school students as part of a sustained curriculum is unprecedented internationally. ARCSA previously took a similar step in 2021, when we began teaching computer vision to high school students years before it became common in school-level programs.
AIR continues this philosophy: depth first, trends second.
Two Pillars: Robotics and Artificial Intelligence
The AIR program is built around two complementary pillars that alternate across terms: Robotics and Artificial Intelligence.
This structure allows students to move repeatedly between theory and application, abstraction and embodiment, building understanding with each cycle.
The Robotics Pillar: Advanced Robotics with ROS 2
At the heart of the robotics pillar is ROS 2, the industry-standard framework used worldwide in robotics research and development.
Students study advanced robotics using professional tools and workflows, including:
- Linux for robotics
- Python and C++ for robotics
- ROS 2 fundamentals and architecture
- Robot navigation, localisation, and mapping
- Behaviour Trees and decision-making
- Robot perception, control, and simulation
Robotics learning is supported by ARCSA’s BotBox Robotics Lab, a professional robotics environment designed to mirror how robotics is taught and practised at the university level.
The robots used in the lab are AI-enabled and equipped with modern sensors, including LIDAR, cameras, and gripper mechanisms. The lab also includes a robotic arm and conveyor system, allowing students to explore autonomy, perception, and manipulation in realistic scenarios.
Students do not assemble robots. They program, analyse, and control them, focusing on systems thinking, reliability, and behaviour in real-world conditions.
Our Partner: The Construct Robotics Institute
The robotics pillar of AIR is supported by our partnership with The Construct Robotics Institute, an internationally recognised robotics education organisation based in Barcelona.
The Construct specialises in teaching ROS and modern robotics through structured online platforms used by universities, researchers, and professionals around the world.
As part of AIR, students may earn official certificates for selected ROS 2 modules. These certificates are awarded upon successful completion of assessments and reflect genuine engagement with professional-level robotics content. Certificates are not automatic and are not the primary goal of the program, but they can complement a student’s academic portfolio.
The Artificial Intelligence Pillar: Learning AI by Building It
The second pillar of AIR focuses on artificial intelligence as a discipline, not as a collection of tools.
Students explore how intelligent systems are designed, trained, evaluated, and improved, developing both technical understanding and intellectual maturity.
Building a Go-Playing AI
One of the major AI projects in AIR involves building an artificial intelligence system that learns to play the game of Go.
Through this project, students study:
- Machine learning fundamentals
- Tree search and decision-making
- Neural networks and deep learning
- Reinforcement learning and self-play
Go is used because it played a central role in one of the most significant breakthroughs in modern AI and provides a rich, structured environment for understanding learning and strategy.
The goal is not to build a perfect player, but to understand how intelligence can emerge from data, learning, and experience.
Building a Nano-LLM
Students are also introduced to modern language models by building a small language model from first principles.
Topics include:
- Natural Language Processing fundamentals
- Bag of Words and Word2Vec
- Recurrent Neural Networks and LSTMs
- Attention mechanisms
- Transformers and modern architectures
- Training, fine-tuning, and evaluation
- Reinforcement Learning with Human Feedback (RLHF)
Rather than treating large language models as black boxes, students learn how they are structured and why they behave the way they do.
Hardware and Software: Two Complementary Curricula
To support AIR, ARCSA has formalised two complementary curricula:
- The Hardware Curriculum, focused on autonomous robots, sensors, drones, smart systems, and applied robotics
- The Software Curriculum, focused on machine learning, computer vision, deep learning, reinforcement learning, and language models
These curricula allow students to see how intelligence exists both as abstract models and as embodied systems acting in the real world.
A Program That Evolves Over Time
AIR is designed as a long-term, evolving program.
Robotics and artificial intelligence alternate across terms, allowing students to revisit each domain with stronger foundations and greater confidence. As technology evolves, new hardware platforms and AI projects may be introduced, while core principles remain constant.
Who AIR Is For
AIR is intended for motivated students who enjoy challenge, curiosity, and deep understanding.
Students may enrol in AIR by:
- Completing Robotics with Blocks or Robotics with Python at ARCSA, or
- Passing an entry assessment designed to demonstrate a solid understanding of Python
This ensures that all students are prepared for the depth and pace of the program.
