Teaching

CSC8611: Human-Artificial Intelligence (AI) Interaction & Futures

Artificial intelligence (AI) is revolutionising our world by enabling the development of intelligent systems that mimic human cognition and behaviour. AI facilitates the integration of information, enabling autonomous responses and supporting informed human decision-making. This course explores the critical and responsible design, development, and evaluation of AI technologies, emphasising human-AI interaction. It aims to equip MSc students in the School of Computing at Newcastle University with the knowledge and skills to effectively utilise and evaluate the impact of AI within their ecosystems.

Key Topics
  • Introduction to advanced automation< - personalisation, adaptive systems, prediction/forecasting, cognitive services, qualitative analysis (visual and natural language processing), hybrid intelligence systems, black boxing
  • Intelligence, problem-solving, & decision-making in humans and machines
  • Designing interactions with applied artificial intelligence, machine learning (ML) & recommender systems
  • AI interaction and experience design + development
  • Human-AI benefits, victims, & disasters
  • Understandable / relatable AI
  • Ethical & responsible AI
  • Human-AI ecosystems & markets (case studies e.g. in autonomous agriculture, manufacturing, transportation, finance, healthcare, security, social media, gaming ...etc)
Expected Learning Cutcomes
  • To have a broad foundational understanding of types and techniques in AI/ML
  • To be able to demonstrate good understanding of the potential use cases and benefits of artificial intelligence (AI) technologies
  • To have a critical understanding of the ethical, social and legal implications of AI applications on human life and work
  • To be able to understand appropriate design, development and research methods for human-AI interaction
  • To be able to design and develop applied artificial intelligence / machine learning applications for given requirements
  • To be able to critically assess potential benefits and possible negative effects of AI systems in situated use

Link to NU module catelogue.

Past Modules

Machine Learning

Designed as a submodule of COMP2261: Artificial Intelligence, this course was specifically tailored for second-year Computer Science undergraduate students at Durham University.

Key Topics
  • Philosophy behind machine learning
  • Fundamental concepts in machine learning
  • Machine learning workflow
  • Defining machine learning tasks
  • Data preparation
  • Model selection and evaluation
  • Implement machine learning algorithms using Python and scikit-learn
  • Interpreting results
Expected Learning Cutcomes
  • Understand key principles of ML for use in managing dataset and building models
  • Understand differences between supervised learning and unsupervised learning
  • Understand the math behind ML models and algorithms
  • Be able to select and implement appropriate learning algorithms for real-life problems
  • Be able to train, optimise, evaluate, and compare ML models
  • Be able to scientifically report the result of machine learning projects

Link to DU module catelogue.

Human-AI Interaction Design

Designed as an optional module for third-year Computer Science undergraduate students at Durham University.

Key Topics
  • AI and User Experience
  • Human-Centred AI Design
  • Human-AI Communication Channels
  • Inclusive Design and Digital Accessibility
  • Explainable AI and Building Trust
  • Privacy and Security Considerations
  • Affective Design for Interactive AI
  • Psychophysical Methods
  • Ambient Intelligence
  • Applications (e.g., gaming, healthcare, education, finance, automotive vehicles, etc.)
Expected Learning Cutcomes
  • Understand impacts of interactive AI system design on user experience
  • Understand concepts and principles of Human-AI interaction design
  • Be able to apply concepts and principles of Human-AI interaction design
  • Be able to conduct experiments for assessing interactive AI systems
  • Be able to propose interactive AI solutions to real-world problems
  • Be aware of ethical and societal considerations in building interactive AI systems

Link to DU module catelogue.