Master of Artificial Intelligence (Online) curriculum

To make data meaningful and informative, it must be transformed from raw and often messy formats into structured, reliable, and analysable forms.

This unit introduces the end-to-end process of data wrangling, which includes data discovery, cleaning, transformation, integration, and validation to prepare data for analysis and modelling. Students will also explore the fundamentals of machine learning, including how prepared data is used to build and evaluate simple predictive models. By combining data preparation with introductory modelling, students will learn to generate insights that support data-driven decision-making in real-world contexts across business and community sectors.

The aim of this unit is to equip students with the practical and conceptual skills to prepare, analyse, and model data for meaningful insights.

As artificial intelligence and emerging technologies transform global industries and societies, professionals must demonstrate not only technical mastery but also ethical judgment, cultural awareness, and leadership integrity. This unit prepares postgraduate students to lead responsibly in the governance and implementation of advanced digital innovations.

Students critically evaluate ethical theories, governance frameworks, and professional standards relevant to AI, data analytics, and automation. They explore how values, power, and culture shape technological design, policy, and impact, drawing on diverse perspectives including Aboriginal and Torres Strait Islander knowledges to promote inclusion and social responsibility.

Through research-informed inquiry, collaborative dialogue, and applied policy analysis, students engage with real-world case studies and professional dilemmas to build advanced capability in ethical leadership and decision-making. Learning activities encourage reflective practice, strategic thinking, and intercultural understanding across local and global contexts.

The aim of this unit is to develop ethical leaders and reflective practitioners who can integrate ethics, empathy, and professional responsibility into technology governance and innovation. Graduates will be equipped to influence policy, guide responsible design, and foster trust and fairness in digital transformation.

To effectively work with datasets, data scientists need be able to apply established techniques of statistical analysis to the information they work with. Statistical modelling is the application of statistical analysis techniques to datasets. It is a mathematical representation of observed data, allowing relationships between data to be identified, predictions about future sets of data made, and visualization of data to aid understanding. Statistical modelling techniques fall into two groups; supervised learning includes regression and classification models; unsupervised learning includes clustering algorithms and association rules. By exploring case studies and industry-relevant examples, students will have the opportunity of gaining an in-depth understanding of the range and application of both supervised and unsupervised statistical data modelling techniques.

The aim of this unit is to facilitate the development of skills required to analyse datasets.

Artificial Intelligence systems increasingly support high-stakes decisions, yet failures often stem not from model accuracy but from weaknesses in system architecture, data pipelines, security, and lifecycle governance. AI systems inherit traditional software vulnerabilities—such as insecure data flows, dependency risks, adversarial threats, and inadequate validation—and these risks are amplified when AI is deployed without strong Software Development Lifecycle (SDLC) practices. Effective AI development therefore requires early risk identification, secure-by-design engineering, robust data quality controls, and continuous monitoring to prevent societal, cultural, and organisational harm.
This unit positions AI as a socio-technical system that must be engineered and governed holistically across its lifecycle. Students examine how AI models and workflows interact with organisational processes and user needs, and how responsible system design depends on anticipating risks at the earliest stages. The unit highlights key international standards underpinning trustworthy AI, including ISO/IEC 42001 AIMS, ISO/IEC 5259 Data Quality for AI, and global incident-reporting frameworks. Students also explore inclusive and culturally aware co-design approaches, including principles informed by Australian Aboriginal and Torres Strait Islander perspectives, to support cultural safety and relational accountability. Through hands-on work with MLOps, secure cloud deployment, and automated testing, students learn to operationalise AI systems that are secure, transparent, reliable, and aligned with responsible innovation principles. The aim of this unit is to develop students’ capability to design, implement, and manage intelligent systems that are scalable, secure, transparent, and responsible, grounded in recognised AI lifecycle and governance standards.

Artificial Intelligence (AI) now influences decisions that shape people’s opportunities, organisational strategy, and societal outcomes. While AI enables powerful advances in prediction, optimisation, and innovation, it also introduces complex ethical, cultural, and governance challenges. Hidden biases, opaque decision pathways, privacy risks, and unequal access to AI technologies can undermine fairness, accountability, wellbeing, and public trust. Addressing these issues requires responsible, transparent, and culturally aware approaches to the design, development, and deployment of AI systems.
This unit responds to the need for professionals who can design, govern, and evaluate AI responsibly. Taking a human-centred and interdisciplinary perspective, it draws on insights from computer science, psychology, ethics, social science, and design thinking to ensure that AI serves human values, dignity, and the common good. Ethics and human-centred thinking are positioned as core foundations for identifying societal risks, which in turn guide the development of fairness, accountability, and wellbeing-related measures. Students develop a fit-for-purpose design mindset grounded in recognised industry standards, ethical frameworks, and governance principles that promote trust, fairness, accountability, and cultural safety.
The unit examines how communication and information flows shape the design, fit-for-purpose use, and governance of AI technologies, enabling students to understand how effective AI solutions are developed and applied in organisational contexts. Students also build a strong understanding of the AI lifecycle including design, development, testing, deployment, and post-deployment monitoring with particular emphasis on the testing stage, where societal, cultural, and ethical risks must be identified and mitigated before deployment. Through real-world case studies across domains such as healthcare, education, and public safety, students explore how ethical, culturally aware, and human-centred principles can be embedded throughout the AI development process.

The aim of this unit is to develop graduates who can design, assess, and advocate for AI systems that are trustworthy, fair, accountable, and aligned with ACU’s mission of ethical innovation for the common good.

AI is fundamentally transforming how organizations design, analyze, and implement decisions in complex, data-driven environments.
This unit provides an advanced exploration of AI-driven decision intelligence, integrating machine learning, optimizations, probabilistic reasoning, and systems thinking to improve decision quality under uncertainty. Students examine how AI models interact with human judgment, organizational processes, and real-world constraints, and how decision pipelines are designed, validated, and continuously monitored.
A major emphasis of the unit is the practical application of Responsible AI (RAI). Students develop capability in error and bias analysis, interpretability and explanation methods, counterfactual reasoning, data quality assessment, and robust testing. These skills enable students to identify model limitations, evaluate behavioral edge cases, and ensure algorithmic decisions remain transparent, contestable, and fair. Structured human evaluation is highlighted as an essential governance mechanism, demonstrating how human oversight complements automation to maintain safety, compliance, and ethical alignment.
The unit advances ACU’s mission of ethical innovation and supports the UN Sustainable Development Goals (SDG 9 and SDG 11). The aim of this unit is to enable students, through modelling, simulation, critical analysis, and applied experimentation, to design AI-enabled decision systems that balance technical innovation with risk mitigation and social responsibility.

The ability to anticipate outcomes and make data-driven decisions is fundamental to success across industries. Predictive modelling and analytics leverage historical and current data through statistical, machine learning, and deep learning techniques to estimate future behaviour, identify patterns, and inform strategic decisions.

In this unit, students will explore predictive approaches across diverse data types and apply appropriate techniques to solve real-world problems. The unit emphasises reproducible modelling practices, feature engineering, model evaluation, and the communication of analytical results to stakeholders. Students will develop both technical proficiency and critical insight into the opportunities, limitations, and ethical considerations of predictive analytics in practice.

The aim of this unit is to equip students with the knowledge and practical skills to design, implement, and communicate predictive models that support informed decision-making and innovation across a range of domains.

Artificial intelligence is driving transformative advances across domains such as automation, natural language processing, computer vision, and healthcare analytics. Deep learning underpins these innovations by enabling data-driven decision-making and intelligent system design.
This unit explores advanced deep learning architectures, including convolutional neural networks (CNNs), sequence-based models such as recurrent neural networks (RNNs) and transformers, graph neural networks, and generative models. Students will apply these methods to real-world datasets, optimise model performance, interpret results, and evaluate trade-offs involving overfitting, fairness, and computational efficiency. The unit emphasises responsible and reproducible practices in model development, testing, and deployment through applied tasks and case studies. By integrating theoretical understanding with hands-on implementation, students will develop the capability to design, train, and evaluate deep learning solutions that are robust. The unit supports ACU’s mission of ethical innovation and aligns with the UN Sustainable Development Goals, particularly SDG 9 (Industry, Innovation and Infrastructure) and SDG 12 (Responsible Consumption and Production).
The aim of this unit is to develop students’ ability to apply deep learning techniques responsibly to solve complex real-world problems.

Generative Artificial Intelligence (GenAI) is transforming modern computing by enabling machines to synthesise, reason, and act across multiple modalities.
This unit provides an advanced exploration of the models, algorithms, and architectures that underpin state-of-the-art generative and agentic systems. Understanding the characteristics of foundation models—pre-trained large language models that form the backbone of generative AI is essential for identifying their capabilities, limitations, biases, and risks. Students will critically examine how these characteristics influence performance, reliability, and ethical deployment in real-world contexts. The unit covers text, image, audio, and video generation; instruction-tuning; in-context learning; tool use and reasoning with large language models; and the integration of generative components into autonomous and agentic systems. Students will gain applied expertise in the architectures and training dynamics of foundation models, including transformers, diffusion models, variational autoencoders, and reinforcement-learning-based frameworks. Through practical implementation and critical reflection, students will develop the capacity to design, evaluate, and apply generative AI systems that are innovative, transparent, and socially responsible. This unit supports ACU’s mission of ethical innovation and aligns with the UN Sustainable Development Goals (SDG 9 – Industry, Innovation and Infrastructure; SDG 16 – Peace, Justice and Strong Institutions).
The aim of this unit is to equip students with advanced theoretical understanding and practical skills to build and deploy generative and agentic AI systems that are responsible, ethical, and trustworthy.