Applied AI Degree

Note: This is a draft document.

Degree Vision

State of AI

AI programs are becoming more abstracted, simpler to use and so also becoming ubiquitous.

AI expert spectrum:

1. Using existing AI tools (e.g. Using chatGPT to provide information, DALL-E to generate images, Github Copilot to write code).
2. Designing focused AI tools by combining existing tools (e.g. designing a tool that accesses chatGPT and DALL-E to produce professional documents).
3. Designing AI (e.g. designing and training a language model, creating a custom predictive model for data)

At the moment,

• Most high school students can use existing tools.
• There are degrees focused on designing AI, but they are technical and only have little focus on human aspects.

Degree Vision

There is industry demand for the middle option:

• This person would be a problem solver, who can combine existing AI tools to provide highly sophisticated focused tools for a domain.
• They will be a people person, who works with others, communicates effectively and creates tools for a specific need.
• This person will need to make sure tools are usable and ethical, and have understanding of policies for the responsible use of AI.
• This person will also need to have an understanding of the social and psychological impacts of AI and be able to evaluate its effect.

Note that even though this person will not be building the AI models, they will require an understanding of how they work, to use them effectively and fix problems when they occur.

Transdisciplinary Structure,

Do we speak to additional schools?

Critical AI

Critical AI refers to an approach within the field of artificial intelligence (AI) that critically examines the societal impacts, ethical implications, power dynamics, and biases inherent in AI systems and their deployment.

Possible Course Learning Outcomes

Technical Proficiency in AI

• Fundamental Understanding: Demonstrate a solid understanding of core AI concepts, including machine learning, deep learning, natural language processing, and computer vision.
• Mathematical and Statistical Skills: Apply mathematical and statistical methods essential for developing and analyzing AI algorithms.
• Programming Skills: Develop proficiency in programming languages and tools commonly used in AI, such as Python, R.
• Practical Application: Apply AI techniques to real-world problems through projects, lab work, and internships.

Ethical and Philosophical Understanding

• Ethical Awareness: Identify and analyse ethical issues related to AI, including privacy, bias, fairness, and the societal impact of AI technologies.
• Moral Reasoning: Develop the ability to make informed and ethically sound decisions in the development and deployment of AI systems.

Social and Cultural Awareness

• Impact on Society: Understand the societal implications of AI, including its effects on labour markets, social structures, and cultural practices.
• Cultural Sensitivity: Appreciate the diverse cultural contexts in which AI technologies are deployed and the varying impacts they may have.

Policy and Governance

• Regulatory Knowledge: Understand the legal and regulatory frameworks governing AI, including data protection laws, AI governance, and international policies.
• Policy Development: Develop skills to contribute to the formulation of policies and guidelines for the responsible use of AI.

Psychological and Cognitive Understanding

• Human-Computer Interaction: Understand principles of human cognition and behaviour to design user-friendly AI systems.
• Psychological Impacts: Analyse the psychological effects of AI on humans, including issues of trust, dependency, and interaction.

Historical and Contextual Knowledge

• Historical Perspective: Appreciate the historical development of AI and its technological precursors, understanding the evolutionary nature of AI technologies.

Communication and Collaboration

• Effective Communication: Communicate complex AI concepts effectively to diverse audiences, including technical and non-technical stakeholders.
• Interdisciplinary Collaboration: Work collaboratively in interdisciplinary teams, integrating perspectives from computer science, humanities, and social sciences.

Lifelong Learning and Adaptability

• Continuous Learning: Demonstrate the ability to engage in lifelong learning to keep pace with rapid advancements in AI and related fields.
• Adaptability: Adapt to new challenges and opportunities in the evolving landscape of AI and technology.

Degree Structure

Topics Covered in the Degree

Enabling	Knowing	Awareness	Sharing
Programming	Data Science	Ethics and Morality	Technical Writing
Mathmatics	Machine Learning	AI Policy and Regulation	Project Management
	Artificial Intelligence	Impact on Society and Culture	Communication Skills
		Human-AI Interaction	History of AI

Majors would be designed to focus on a specific disciplines (e.g. AI in education, business, law, government).

Things to consider

• A capstone project will be needed.
• Will we want particular companies to be involved?
• Which accrediting body will we be using?