Master of Applied Artificial Intelligence

All learners will complete Contemporary Issues in Applied Artificial Intelligence (APAI603), with a focus on their chosen endorsement. 

Description
This course examines the most pressing technological, ethical, and policy challenges arising from recent breakthroughs in artificial intelligence (AI). Students will explore cutting-edge AI technologies including large language models (LLMs), autonomous agents, and prompt engineering techniques, gaining both theoretical understanding and hands-on experience in effectively working with these systems. The course then addresses critical questions of AI policy, law, and ethics, analysing regulatory frameworks, algorithmic bias, privacy concerns, liability issues, and challenges of AI governance. Through rigorous case studies of real-world AI deployments, students will critically examine transparency, accountability, fairness, and the evolving legal landscape surrounding AI systems. Students will also explore contemporary issues within their specific chosen endorsement, and report back on their findings.

Learning outcomes: 

1. Evaluate the technical capabilities and limitations of large language models (LLMs) - demonstrating understanding of their architectures, training methodologies, and practical applications across various domains.
2. Design and implement effective prompt engineering strategies - applying systematic techniques to optimize AI system performance for specific tasks and use cases.
3. Analyse the function and deployment of AI agents - assessing how autonomous systems interact with environments, make decisions, and the implications of increasing AI autonomy.
4. Critically examine AI policy and regulatory frameworks - comparing approaches across jurisdictions and evaluating their effectiveness in addressing emerging technological challenges.
5. Assess ethical dimensions of AI systems - identifying issues of algorithmic bias, fairness, transparency, and accountability in real-world deployments.
6. Apply legal reasoning to AI-related questions - including liability, intellectual property, privacy rights, and the adequacy of existing legal frameworks for governing AI technologies.
7. Synthesize technical and normative perspectives - to propose informed, practical recommendations for responsible AI development, deployment, and governance in professional and societal contexts.

You'll complete 30 points of business and technology courses, aligned with applied AI practice. You'll gain skills to help you use AI to drive innovation, efficiency, and strategic decision making.

Course 1: Business & AI (INFO601) - 15 points
Artificial Intelligence is transforming our world, reshaping business operations, public services, job markets and how we function as a society. This course presents AI solutions in the context of specific business objectives, models and industries. By analysing the latest innovative use cases, the course will provide insights into the opportunities, business and ethical impacts and implications of AI. As part of the course, students apply their learning to a New Zealand organisation to develop a business case and a roadmap for an AI based solution. Learning outcomes: 

1. Explain AI concepts and their business applications.
2. Identify potential opportunities for strategic advantage for specific AI techniques in a business context.
3. Design and recommend AI solutions for specific contexts
4. Evaluate the technical and business benefits, costs and risks of AI technologies in a specific business application.
5. Discuss the impact of AI applications at the individual, organisational and societal level.

Course 2: AI Solutions Design and Delivery (INFO602) - 15 points
This course prepares students to lead and manage AI-driven business initiatives from conception through launch. Students develop knowledge and skills in opportunity identification, technology evaluation, strategic decision-making, and systems governance specific to AI solutions. Emphasising both generative AI and traditional machine learning applications, the course addresses distinctive business challenges of AI solutions including data strategy, model governance, vendor relationships, responsible AI development, and stakeholder management. Through case studies and hands-on workshops, students build practical competencies for delivering successful AI solutions in business contexts. Learning outcomes: 

1. Apply modern systems analysis and design methods to identify and validate AI opportunities.
2. Apply decision frameworks to evaluate and select appropriate AI technologies for specific business problems.
3. Plan AI solution development strategies, including build-vs-buy decisions, data sourcing, platform and vendor selection, and prototyping approaches appropriate to business context.
4. Recommend comprehensive strategies for deploying, evolving, and governing AI solutions in business.
5. Assess approaches to AI-specific systems development challenges in business, such as data & model governance, ethical considerations, cultural responsiveness, risk management, and regulatory compliance.

You'll complete a 30-point Health Information Management course, aligned with applied AI practice. You'll gain skills to improve health outcomes, delivery and patient wellbeing.

Health Information Management (HLTH402) - 30 points
This course examines how Information Technology meets the information needs of health provider organisations, practitioners, and consumers and how IT can play a significant and positive role in the provision of healthcare services. Learning outcomes: 

1. Appraise how digital health and technology help address key health priorities and challenges. 
2. Examine how health information, digital health, and technology inform and shape decisions in healthcare.  
3. Analyse barriers and facilitators to digital health and technology application at macro, meso and micro levels.  
4. Scrutinise health information systems and design in healthcare settings. 
5. Apply ethical and evidenced-informed approaches to digital health and technologies in various settings. 
6. Create and present a digital health initiative to transform healthcare. 

You'll complete 30 points of law, policy and ethics courses, aligned with applied AI practice. You'll gain skills to guide responsible use cases. 

Course 1: Ethics of Artificial Intelligence (PHIL425) - 15 points
Artificial Intelligence (AI) is a new and rapidly developing field that affects social media, military actions, the way we are governed, our criminal justice and health systems, and many other areas that impact on our lives. In each of these areas, the use of AI can and will create situations that harm or benefit people and also non-human animals. Understanding the nature of these potential harms and benefits, their value and disvalue, and what can enhance, mitigate or remove them, can help to make the widespread adoption of AI technologies ethical and also more publicly acceptable. Learning outcomes: 

1. Understand key concepts in ethics and AI.
2. An ability to develop and evaluate ways in which AI innovations may respond to ethical challenges.
3. An ability to analyse ethical problems that may arise from the use of AI in ways that are useful for policy makers and businesses, including an understanding of the relevance of the Treaty of Waitangi to AI.
4. An understanding of ways in which ethical issues arising from the use of AI impact different communities.
5. An ability to express arguments in writing.
6. Critical thinking and analytical skills, including an ability to identify, explain, interpret and evaluate relevant arguments made by others, and the ability to use these skills to develop arguments about AI ethics.
7. Knowledge and understanding of issues in AI Ethics that are of specific relevance to Māori and Pasifika.

Course 2: Legal, Regulatory, and Policy Considerations Around AI Technologies (LLAW607) - 15 points
This course aims to equip students with the necessary skills to thrive in the digital age and understand the incredibly tough choices we—both as individuals and society as a whole—have to make with respect to the regulation, development, and societal adoption of Artificial Intelligence (AI) technologies to ensure they are beneficial for humanity and our environment. The course focuses on key policy concerns and regulatory considerations related to AI and technological innovation more generally. Reviewing the challenges associated with previous waves of technological innovations, we consider the properties of a regulatory and policy stance that is conducive to the safe adoption of new technologies. We then examine current policy and regulatory initiatives around responsible and trustworthy AI (e.g., OECD AI Principles, EU AI Act Proposal, AI standardization work). Based on regulatory theory—and AI regulation and governance in particular—we familiarize ourselves with the multitude of considerations necessary for effective regulatory intervention in general. Case studies spark thought-provoking, interactive reflection on the real-world problems stemming from AI. Complementing this theoretical background, the course also covers some practical issues, e.g., real-life examples of how AI principles are implemented within an organization from setting top strategic priorities down to collaborating with a variety of stakeholders and designing specific product features. Learning outcomes: 

1. Analyse how the technical attributes and capabilities of AI intersect with legal, regulatory, and policy issues.
2. Interpret, analyse, and critique the domestic and international AI legal, regulatory, and policy landscape and identify challenges related to AI development and adoption.
3. Explain legal, regulatory, and policy issues clearly for diverse audiences (including both technical experts and indigenous communities) and apply the principles of interdisciplinary and multi-stakeholder collaboration and problem solving.
4. Carry out independent research and demonstrate sound (academic) writing, public speaking, and communication skills, allowing for efficient presentation of research results both in written and oral form.
5. Demonstrate both independent and team work skills, facilitating employability with diverse stakeholders in academia, industry, the public sector, indigenous communities, and civil society.

You'll complete 30 points of education courses, aligned with applied AI practice. You'll gain skills to to enhance teaching, learning, and educational systems. 

You can either take our 30 point course, Digital and AI pedagogies for enhanced learning (EDME433) OR two 15-point courses, Foundations of AI Education (EADE441) and AI-Enabled Learning Design (EADE443).

Digital and AI pedagogies for enhanced learning (EDME433) - 30 points
Participants will gain a comprehensive overview of the field of technology-enhanced learning and develop an ability to select, evaluate and create digital and AI tools in a variety of digital education contexts. This course explores the evolution of digital education, and the impact AI technology, in particular, has on learning practice and theories. Drawing on theories of affordances, students learn about the opportunities and constraints of a wide variety of digital tools, and materials, and how they can be used in a pedagogically appropriate way to enhance learning. Focusing on digital pedagogies the course will enable participants to learn about how best they can use digital tools for teaching and learning in a particular context. Learning outcomes: 

1. Develop an understanding of how technology, including AI, has developed to the point it can be utilised in the classroom to support teaching and learning within Aotearoa New Zealand's bicultural and global contexts.
2. Demonstrate an advancing knowledge of theory and practice with the application of digital and AI technology in the classroom, drawing on appropriate pedagogical, cultural and social aspects of its use.
3. Critically assess research about the effectiveness of AI technology and its impact on learning, considering equity, cultural responsiveness, and Treaty-informed implications.
4. Appraise and critically evaluate suggested AI technology for teaching and learning, addressing ethical, cultural, and contextual factors that influence their effective integration in educational settings.

Foundations of AI Education (EADE441) - 15 points
Foundations of AI Education supports educators to critically re-examine teaching, learning, and assessment in response to the growing influence of artificial intelligence, with particular attention to generative AI. Rather than centring on tools alone, the course foregrounds foundational concepts, ethical considerations, and pedagogical design principles that enable educators to respond thoughtfully and responsibly to AI-enabled educational contexts. Drawing on contemporary research, theory, and practice, the course integrates three key dimensions: foundational AI literacy, the redesign of authentic assessment, and the development of AI-informed pedagogical approaches that support meaningful learning, learner agency, and educational integrity. Throughout the course, participants critically engage with the social, cultural, and ethical implications of AI, including issues of bias, data governance, academic integrity, and equity. By the end of the course, educators will be equipped to make informed, principled decisions about how (and whether) AI is integrated into educational practice, and to articulate coherent, values-informed approaches to teaching and assessment in an AI-rich world. Learning outcomes: 

1. Critically evaluate the educational, ethical, and social implications of artificial intelligence in education, including generative AI, with particular attention to equity, academic integrity, and cultural responsibility. 
2. Design and critically evaluate AI-supported assessment practices that enable learners to engage with generative AI in culturally inclusive, ethical, and equitable ways. 
3. Critically analyse, design, and enact AI pedagogy by evaluating contemporary theories and models and applying them to create AI-enhanced pedagogical approaches that support meaningful learning, and AI literacy, grounded in Te Tiriti o Waitangi and culturally sustaining practice. 

AI-Enabled Learning Design (EADE443) - 15 points
AI-Enabled Learning Design supports educators and learning designers to critically and creatively integrate generative AI into the design of digital and online learning experiences. The course focuses on how AI can be used to enhance learning design processes, support adaptive and learner-centred experiences, and streamline digital content development - while remaining attentive to ethical, cultural, and pedagogical considerations. Learning outcomes: 

1. Critically evaluate the pedagogical affordances and constraints of generative AI tools for learning design and digital content development. 
2. Design and apply AI-supported learning design principles to create learner-centred, adaptive, and immersive digital learning resources. 
3. Critically examine the ethical, cultural, and societal implications of integrating generative AI into digital learning solutions, including issues of responsibility, risk, and equity. 

All learners will complete an Applied Artificial Intelligence Project (APAI678), with a focus on their chosen endorsement. 

Description
In this course, learners will apply the knowledge they have gathered in their other core courses and their 30 points of Applied Artificial Intelligence Endorsement courses to research, design, execute, and evaluate a project applying Artificial Intelligence (AI) to a given topic within their endorsement area. For those learners who have taken the PGCert Applied AI Project Design course, they may choose to start with the topic area that they used for the Applied AI Project plan in that course or may choose an entirely new topic. Learners in this course will choose a topic within the field of their Applied AI endorsement, conduct a literature review to determine how AI has been applied within this specific topic, then design and execute a project utilising AI to resolve a problem within this topic, or to approve efficiency in this topic. Upon concluding the project, learners will then evaluate how well the AI was able to address the problem or improve efficiency for their chosen topic.

Learning outcomes: 

1. Demonstrate in-depth knowledge and understanding of AI technologies, in particular as related applying AI within the context of their chosen endorsement.
2. Apply AI to solve a problem or improve efficiency in a chosen endorsement utilising research, knowledge, and understanding, and taking into account existing solutions from around the world.
3. Demonstrate 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. Evaluate the outcomes of AI application in a given area, and reflect on how to further improve outcomes in future.
6. Communicate their solution effectively, in written and oral form, to academic experts within their chosen endorsement.