Module COMP53615: Human-AI Interaction Frameworks and Practices

Department: Computer Science

COMP53615: Human-AI Interaction Frameworks and Practices

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

• 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.

• 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.

• 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

Summative Assessment

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