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Durham University

Faculty Handbook 2022-2023

Module Description

Please ensure you check the module availability box for each module outline, as not all modules will run each academic year.

Department: Computer Science

COMP3647: Human-AI Interaction Design

Type Open Level 3 Credits 10 Availability Available in 2022/23 Module Cap None. Location Durham

Prerequisites

  • COMP2261 Artificial Intelligence

Corequisites

  • None

Excluded Combination of Modules

  • None

Aims

  • To discuss how the design of Human-AI interactions may affect user experience.
  • To introduce methods and tools for designing interactive AI systems.
  • To develop ethical and societal principles in the design of interactive AI systems.

Content

  • 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.)

Learning Outcomes

Subject-specific Knowledge:
  • An understanding of impacts of interactive AI system design on user experience.
  • An understanding of concepts and principles of Human-AI interaction design.
Subject-specific Skills:
  • An ability to apply concepts and principles of Human-AI interaction design.
  • An ability to conduct experiments for assessing interactive AI systems.
Key Skills:
  • An ability to propose interactive AI solutions to real-world problems.
  • Awareness of ethical and societal considerations in building interactive AI systems.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

  • Lectures enable students to learn new materials relevant to Human-AI interaction design and evaluation, as well as their applications in the real-world.
  • Formative and summative assessments assess students' knowledge and skills of using Human-AI interaction principles, methods and tools in individual projects.

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
lectures 20 2 per week 1 hour 20
preparation and reading 80
total 100

Summative Assessment

Component: Coursework Component Weighting: 100%
Element Length / duration Element Weighting Resit Opportunity
Summative assignment 100% No

Formative Assessment:

Example formative exercises are given during the course.


Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University



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