Course Code: AIB315
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Synopsis

This course examines how humans and artificial intelligence (AI) interact and collaborate in organisational and business contexts. It introduces foundational concepts of Human–AI collaboration and explores how AI supports, augments, and influences human decision-making and work practices. Students will learn principles of effective Human–AI interaction, including trust, transparency, usability, and performance evaluation, as well as methods for designing and assessing Human–AI systems. The course covers individual, collective, and hybrid Human–AI interaction models, with applications across business domains such as recommendation systems. It also addresses key challenges associated with Human–AI collaboration, including bias, fairness, risk, alignment, and ethical responsibility. At the end of this course, students will be equipped to analyse, evaluate, and design Human–AI systems that are effective, responsible, and aligned with organisational goals.
Level: 3
Presentation Pattern: EVERY REGULAR SEMESTER

Topics

  • Foundations of Human–AI Collaboration
  • AI Capabilities and Limitations for Human–AI Collaboration
  • Business Applications of Human–AI Collaboration
  • Human–AI Interaction Principles, Trust, and Research Methods
  • Collective and Hybrid Human–AI Interaction
  • User Experience Design for Human–AI Interaction
  • Usability Testing and Evaluation
  • Organisational and Human Challenges of Human–AI Interaction
  • Metrics for Evaluating Human-AI Performance
  • Recommendation Systems for Human-AI Collaboration
  • Bias, Fairness, and Risk in Human–AI Collaboration
  • Alignment, Ethics, and Responsibility in Human–AI Collaboration

Learning Outcome

  • Explain key concepts and frameworks underlying Human–AI collaboration.
  • Discuss how AI supports and influences human decision-making.
  • Appraise applications of human-AI collaboration in different business contexts.
  • Apply appropriate Human–AI interaction principles for various business and organisational scenarios.
  • Analyse Human–AI performance using appropriate evaluation metrics.
  • Propose Human–AI interaction and collaboration design patterns to improve organisational outcomes.