Master of Applied Artificial Intelligence*

Description
The course will begin with a thorough examination of Artificial Intelligence (AI) evolution—from the Dartmouth Conference through the expert systems era, the subsequent "AI winters," and the recent deep learning renaissance—providing students with essential context for understanding the field's current trajectory. It will then systematically cover machine learning principles and neural network architectures, ensuring students grasp the mathematical and computational foundations underlying modern AI systems. The course will include coverage of large language models (LLMs), examining their transformer architectures, capabilities, limitations, and broader societal implications. Finally, we will explore AI agents and autonomous systems, preparing students to critically evaluate emerging technologies.

Learning Outcomes

1. Trace the historical development of artificial intelligence - identifying key milestones, paradigm shifts, and the socio-technical factors that have shaped the field from its inception through the present day.
2. Understand the fundamental principles of machine learning - including the distinctions between supervised, unsupervised, and reinforcement learning paradigms, and articulate how algorithms learn from data.
3. Describe the architecture and function of neural networks - demonstrating understanding of how layered computational structures process information and enable pattern recognition across diverse applications.
4. Analyze the capabilities and limitations of large language models (LLMs) - evaluating their underlying mechanisms, practical applications, and ethical considerations in deployment.
5. Distinguish between different types of AI agents - explaining how autonomous systems perceive environments, make decisions, and execute actions to achieve specified objectives.

Description
This course is designed to equip you with foundational knowledge and skills needed to navigate the complex ethical considerations that arise in the rapidly evolving field of data science. You'll learn to identify, evaluate, and mitigate ethical data issues, exploring concepts such as autonomy, wellbeing, justice, confidentiality, and informed consent. Using a case-based approach, you'll become confident using data ethics principles at every stage of data analysis to guide your practice, from planning, processing, and sharing analyses. This course has a particular focus on data sovereignty, exploring how data ethics and Te Tiriti o Waitangi connect, looking at Māori sovereignty, partnership and justice. You'll walk away with a framework to guide your work with data, helping ensure that your processes are appropriate, ethical and impactful.

Note: this course is typically taken at the same time as Law for AI Professionals (LLAW606) to equal typical part-time study hours. 

Description
This course introduces the foundations of law and legal institutions in New Zealand, providing Artificial Intelligence (AI) professionals with essential legal literacy. Students will learn how law operates as a system of social ordering, how it shapes technological innovation, and how to identify and manage legal risk in AI development and deployment.

Learning Outcomes

1. Explain the fundamental purposes, sources, and principles of law within the New Zealand legal system.
2. Analyse the institutional structures and regulatory mechanisms that influence the development and deployment of AI technologies in New Zealand and globally.
3. Apply basic concepts of contract, tort, and intellectual property law to identify potential legal risks in AI projects and innovation settings.
4. Evaluate alternative approaches to dispute resolution and legal risk management relevant to technology and AI contexts.

Note: this course is typically taken at the same time as Data Ethics (PHIL426) to equal typical part-time study hours. 

Description
This course examines the practical deployment of applied artificial intelligence (AI) across diverse professional domains, providing students with concrete understanding of how AI technologies are transforming industries and practice. Through in-depth case studies, students will explore AI applications in various domains including healthcare (diagnostic imaging, personalized medicine, drug discovery), legal practice (contract analysis, legal research, predictive case outcomes), engineering (predictive maintenance, design optimisation, quality control), architecture (generative design, building performance modelling, construction planning) etc. The curriculum analyses both successful implementations and notable failures, enabling students to critically assess the opportunities, limitations, and contextual factors that determine AI project outcomes. Based on knowledge gained earlier in the field, students will evaluate domain-specific challenges including data requirements, regulatory compliance, professional ethics, and integration with existing workflows. By examining real-world applications across sectors, this course equips graduates to identify viable AI opportunities, anticipate implementation challenges, and lead AI adoption initiatives within their chosen professional fields.

Learning Outcomes

1. Analyse real-world AI implementations across multiple sectors - evaluating the technical approaches, business models, and outcomes of AI deployments in healthcare, law, engineering, architecture, finance, education, and other professional domains.
2. Assess the feasibility and appropriateness of AI solutions for specific domain challenges, considering factors such as data availability, regulatory constraints, stakeholder needs, and organisational readiness.
3. Identify domain-specific barriers and enablers to successful AI adoption, including technical limitations, ethical considerations, professional standards, workflow integration requirements, and change management challenges.
4. Evaluate the impact of AI technologies on professional practice, analysing how AI systems augment or transform existing workflows, decision-making processes, and the roles of practitioners across different fields.
5. Critically examine failed or problematic AI implementations, extracting lessons about common pitfalls, unintended consequences, and the gap between technological promise and practical reality in diverse contexts.
6. Develop strategic recommendations for AI adoption within specific domains, synthesising insights from case studies to propose actionable implementation plans that address technical, organizational, ethical, and regulatory considerations.

Description
In this course, learners will apply the knowledge they have gathered in their other core courses to work through the process of designing a plan for a project to effectively utilise, and evaluate the efficacy of, current generation Artificial Intelligence (AI) within a given context area of interest to them. Within this course, learners will conduct a broad survey of topics within a field of interest related to them, for example within the disciplines of Business, Law, Health or Education. Learners will conduct a literature review to determine how AI has been applied within this specific topic, with the view of critically understanding and examining the approaches and effectiveness of previous applications of AI. Learners will then design a plan for how current generation AI tools could be used to either solve a problem, or improve efficiency, within that given topic, including how they could evaluate the accuracy and effectiveness of the outputs. Learners will not be required to execute their plan within this course; however the plan developed in this course may be used as a basis for the applied project in the PGDipAAI/MAAI.

Learning Outcomes

1. Demonstrate knowledge and understanding of AI technologies and considerations from foundation courses.
2. Plan and design how to apply AI to a given topic based on research, knowledge, and understanding, and taking into account existing solutions from around the world.
3. Demonstrate personal attributes such as problem solving ability, communication ability, and professional responsibility, including awareness and understanding of biculturalism in Aotearoa New Zealand and how to appropriate apply AI respectfully in given cultural contexts.
4. Demonstrate understanding of the ethical, legal, and societal implications, opportunities, and constraints of applied AI in a specific domain, including awareness of global trends and practices and how these are relevant in Aotearoa New Zealand.
5. Communicate the proposal effectively, in written and oral form, to the academic community.