COMP53615: Human-AI Interaction Frameworks and Practices

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• 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.

• 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

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.

• 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.

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