AI Foundations and Applications (NCO105)

This course introduces students to essential concepts and practical skills needed to work confidently with artificial intelligence (AI) in academic, professional, and everyday contexts. It begins with foundational knowledge of AI, covering its core concepts, historical development, and applications across different industries. Building on this foundation, students explore data literacy for AI, learning how data shapes AI systems and how issues such as data quality, bias, and representation influence AI outcomes. Students will also gain a conceptual understanding of key concepts in machine learning and modern AI technologies, and learn how generative models produce text, images, and other forms of outputs. With this conceptual grounding, the course shifts to practical skill development. Students learn effective strategies for interacting with AI systems, including writing iterative prompts, evaluating AI outputs, and integrating AI tools into productivity workflows. Finally, students will examine ethical issues surrounding the use of AI, including bias, transparency, accountability, privacy and responsible use in relation to academic integrity and professional practice. By the end of this course, learners will be equipped with both conceptual understanding and practical skills to use AI tools responsibly and effectively. Emphasising human judgment as central to ethical AI use, the course prepares students to apply AI in problem-solving and decision-making, strengthening their readiness for the modern workplace.

• What is Artificial Intelligence? Core Definitions and Distinctions
• Critical engagement with AI: Keeping Humans in the Loop
• Data, Datasets, and Their Representation in AI
• Data Quality, Bias, and Reliability of AI Outputs
• Fundamentals of Machine Learning
• AI Models and the Learning Process
• What Makes AI “Generative”?
• Why Generative AI Could be Useful, Persuasive, and Wrong
• Effective Interaction with AI Systems
• Applying AI for Productivity
• Foundations and Pillars of AI Ethics
• Academic Integrity in the Age of AI: Strategies for Responsible AI Use

• Explain fundamental AI concepts and terminology
• List applications of different AI approaches and their limitations
• Classify different types of data for AI training
• Use prompts iteratively and effectively to improve AI outputs
• Interpret AI outputs critically
• Discuss Ethical implications of AI use