Teaching

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

Designed as a postgraduate-level module, it focuses on the critical and responsible design and evaluation of AI technologies, with a particular emphasis on human-AI interactions. It aims to provide MSc HCI students with a cross-disciplinary foundation and the advanced skills to effectively utilise and critically evaluate the impact of Human-AI Interaction (HAII) concepts and technologies within diverse ecosystems.

Key Topics
  • Human-centred approach to human-AI interaction design
  • Cognitive and behavioural models
  • Designing explainable and transparent AI
  • Ethical, societal, and legal implications in human-AI interaction
  • Interaction design for AI-powered systems
  • Multimodal interaction with AI
  • AI in collaborative and assistive contexts
  • Emerging trends in human-AI interaction
Expected Learning Cutcomes
  • To have a good understanding of the theoretical foundations of human-AI interaction, including human-centred design, cognitive models, and user behaviour in AI contexts.
  • To be able to comprehend AI ethics and responsibilities, including ethical, societal and legal considerations in human-AI systems, such as fairness, transparency, bias mitigation, and user privacy.
  • To be able to identify challenges in designing AI systems for trust, explainability, and usability across different domains and user groups.
  • To gain knowledge of current trends in human-AI interaction, including multimodal interfaces, adaptive systems, and collaborative AI.
  • To be able to apply human-centred design methodologies to create intuitive, inclusive, and user-friendly AI systems.
  • To be able to use evaluation frameworks and usability testing to assess AI systems for interaction quality, trust, and ethical, societal, and legal implications.
  • To be able to perform effective user research to gather insights on user needs, behaviours, and expectations when interacting with AI technologies.
  • To be able to address specific human-AI interaction challenges, such as explainability, trust-building, and reducing cognitive load.

Link to NU module catelogue.

Past Modules

COMP2261: Artificial Intelligence - 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.

COMP3647: 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.