| Course code | AIST3510 |
| Course title | Human-computer Interaction 人機互動 |
| 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. 人機互動設計的基礎，包括人類處理信息的模型及其理論、智能介面的設計方法、步驟及評估之方法。人機互動的要素：佈局、顯示、規約、對話、程序及誤差的處理。應用於人機互動的新科技：語音識別、觸感合成、虛擬真實、軟件代理、群體軟件等。 |
| 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. |
| Assessment (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); | Y |
| 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); |
Y |
| 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). |
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| Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes | |