AIST3510 Human-computer Interaction


Course code AIST3510
Course title Human-computer Interaction
Course description This course provides an introduction to the fast evolving field of human computer interaction (HCI). HCI is a multidisciplinary subject concerning the design, implemen-tation and evaluation of interactive computing systems for human use, and the study of major phenomena surrounding them. We will provide a broad overview of the field, including the theory and principles underlying good designs, with an emphasis on the interface design process, development and evaluation. We will also sample some state-of-the-art technologies in HCI, such as speech recognition, haptics, virtual reality, software agents and computer supported cooperative work.
Unit(s) 3
Course level Undergraduate
Semester 1 or 2
Exclusion SEEM3510
Grading basis Graded
Grade Descriptors A/A-:  EXCELLENT – exceptionally good performance and far exceeding expectation in all or most of the course learning outcomes; demonstration of superior understanding of the subject matter, the ability to analyze problems and apply extensive knowledge, and skillful use of concepts and materials to derive proper solutions.
B+/B/B-:  GOOD – good performance in all course learning outcomes and exceeding expectation in some of them; demonstration of good understanding of the subject matter and the ability to use proper concepts and materials to solve most of the problems encountered.
C+/C/C-: FAIR – adequate performance and meeting expectation in all course learning outcomes; demonstration of adequate understanding of the subject matter and the ability to solve simple problems.
D+/D: MARGINAL – performance barely meets the expectation in the essential course learning outcomes; demonstration of partial understanding of the subject matter and the ability to solve simple problems.
F: FAILURE – performance does not meet the expectation in the essential course learning outcomes; demonstration of serious deficiencies and the need to retake the course.
Learning outcomes 1. Acquiring the mathematical and engineering foundations that underlie human-computer interaction;
2. Appreciation of the use of other fields of knowledge in the interdisciplinary field of HCI, with the target to achieve a high degree of system usability;
3. Understanding of the integration of component technologies into end-to-end systems that support user-centered HCI;
4. Ability to design and critique user interfaces, as well as conduct empirical evaluation of their interim and overall performances;
5. Awareness of the state-of-the-art technologies that support HCI in real applications and usage contexts.
(for reference only)
Essay test or exam: 50%
Homework or assignment: 50%
Recommended Reading List 1. Designing the User Interface: Strategies for Effective Human-Computer Interaction (6th Edition / May 3, 2016 / ISBN-13: 978-0134380384) Ben Shneiderman, Catherine Plaisant, Maxine Cohen, Steven Jacobs, Niklas Elmqvist, Nicholas Diakopoulos Pearson Education
2. Designing with the Mind in Mind: Simple Guide to Understanding User Interface Design Guidelines (2nd Edition / Feb 24,2014 / ISBN-13: 978-0124079144) Jeff Johnson Morgan Kaufmann


AISTN programme learning outcomes Course mapping
Upon completion of their studies, students will be able to:  
1. identify, formulate and solve AI-related engineering problems (K/S);  
2. design a system, component, or process to meet desired needs within realistic constraints, such as economic, environmental, social, political, ethical, health and safety, manufacturability and sustainability (K/S/V);
3. understand the impact of AI solutions in a global and societal context, especially the importance of health, safety and environmental considerations to both workers and the general public (K/V);  
4. communicate and work effectively in multi-disciplinary teams (S/V);
5. apply knowledge of mathematics, science, and engineering appropriate to the AI degree discipline (K/S); 
6. design and conduct experiments, as well as to analyze and interpret massive data (K/S);  Y
7. use the techniques, skills, and modern computing tools necessary for engineering practice  appropriate to the AI and computing discipline (K/S);
8. understand professional and ethical responsibility (K/V); and
9. recognize the need for and the importance of life-long learning (V).
Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes