Postgraduate Programme and Module Handbook 2025-2026
Module COMP53615: Human-AI Interaction Frameworks and Practices
Department: Computer Science
COMP53615: Human-AI Interaction Frameworks and Practices
Type | Tied | Level | 5 | Credits | 15 | Availability | Available in 2025/2026 | Module Cap |
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Tied to | G5T609 |
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Tied to | G5T709 |
Prerequisites
- None
Corequisites
- None
Excluded Combination of Modules
- None
Aims
- To introduce theories, methods and tools for designing and evaluating interactive AI systems from the human-centred design perspective.
- To explore the relationships among Human-AI interaction design, user experience and user trust.
- To develop ethical and societal principles in the design of interactive AI systems.
Content
- AI and User Experience (UX)
- Human-Centred AI Design Frameworks
- Evaluation: Analytical and Empirical Methods
- Explainable AI and Trustworthy Autonomous Systems
- Affective Computing: Theories and Practices
- Generative AI: Applications and Impacts
- Ethics and Responsible AI
- Emerging Trends in Human-AI Interaction
Learning Outcomes
Subject-specific Knowledge:
- By the end of this module, students should be able to demonstrate:
- an understanding of impacts of interactive AI system design on user experience and user trust.
- an understanding of concepts and principles of Human-AI interaction design.
- an understanding of potential benefits and risks associated with the use of AI systems at the societal level.
Subject-specific Skills:
- By the end of this module, students should be able to demonstrate:
- an ability to apply concepts and principles of Human-AI interaction design.
- an ability to conduct experiments for assessing interactive AI systems.
- an ability to analyse potential positive and negative societal impacts of AI systems.
Key Skills:
- By the end of this module, students should be able to demonstrate:
- 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.
- Computer classes provide students with hands-on experience in developing large language model applications (LLMAs) and using prototyping techniques, equipping them with the skills needed to complete their coursework.
- Summative assessments assess students' knowledge and skills of using Human-AI interaction principles, methods and tools in a bench test and group projects.
- The assignment element of the coursework component consists of a coding exercise with accompanying report, done as groupwork. Groups of 2 to 4 students will collaboratively develop a prototype of a large language model (LLM) application for a designated area. Within each group, some members will focus on back-end development as AI developers, while others take on front-end responsibilities as UX designers. Each prototype will undergo an evaluation process, and final outputs will include both a functional LLM prototype and a technical report.
Teaching Methods and Learning Hours
Activity | Number | Frequency | Duration | Total/Hours | |
---|---|---|---|---|---|
Lectures | 9 | 1 per week | 2 hours | 18 | |
Computer Classes | 8 | 1 per week | 2 hours | 16 | |
Preparation and Reading | 116 | ||||
Total | 150 |
Summative Assessment
Component: Coursework | Component Weighting: 100% | ||
---|---|---|---|
Element | Length / duration | Element Weighting | Resit Opportunity |
General Test | 60 minutes | 20% | |
Assignment | 80% |
Formative Assessment:
Via computer classes.
■ 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