Human-Computer Interaction for AI

Course Objective

At the end of this course, students will be able to:

Knowledge and understanding:

• Describe the fundamental principles of user experience design.
• Describe fundamental input and output techniques for human-computer interaction.

Applying knowledge and understanding:

• Gather requirements for designing interactive systems.
• Design a human-agent interaction to answer a scientifically grounded research question.
• Apply low-fidelity and high-fidelity prototyping methods in HCI.

Making judgements:

• Conduct user studies to empirically evaluate the UX and usability of their interaction design.

Communication:

• Communicate design solutions and evaluations on an academic level.

Lifelong learning skills:

• Work in teams to achieve a common goal.

Course Content

In many application domains, AI systems must collect input from humans or collaborate with them to enhance their performance and decision-making processes. This course will introduce students in the AI program to the existing theories and methods for the design and evaluation of interactions between humans and [intelligent, socially interactive, or immersive] computing systems. The aim is to give students an understanding of the concepts of usability, user experience (UX), and user-centered design and to equip them with empirical research skills for prototyping and iterative testing of such interactive computing systems.

The course is divided into two parts, each achieving different learning goals;

In the theoretical part of the course (lectures), students will learn about the core topics in HCI, such as the lifecycle of interaction design, human factors, interaction elements, modeling interaction, and common research methods in HCI studies.

In the practical part of the course, students will acquire hands-on research experience by collaborating in groups to design, conduct, and report on an HCI experiment with human participants.

Additional Information Teaching Methods

This course is taught on campus. The lectures will not be recorded.

Attendance in the practical sessions of the course is mandatory.

Students may use AI tools, including large language models, when preparing assignment reports ONLY for the purpose of refining and improving the quality of their writing. Any content generated with the assistance of such tools must be carefully reviewed, critically evaluated, and appropriately revised before inclusion in the report. The submission of AI-generated content without sufficient evaluation or modification will be regarded as academic fraud.

Method of Assessment

The final course grade is composed of two assessment components:

• Written Exam (Individual - 50% of the final grade)
• Research project report (Group work - 50% of the final grade)

The final grade will be calculated

Final Course Grade = 0.5 x Individual Exam Grade + 0.5 x Group Project Report Grade

To pass the course, students must pass both the individual exam and the research project report (for each component, a grade >=5.5 is required).

Please note:

• The group must submit one report, and all group members will receive the same grade.
• Resit opportunities will be provided for both the individual exam and the group project report.

Literature

MacKenzie, I. Scott. Human-Computer Interaction : An Empirical Research Perspective, Elsevier Science & Technology, 2013. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/vunl/detail.action?docID=1110719.

(Students have free access to the online version of the book through their VUnetID)

Additional Information Target Audience

Bachelor Artificial Intelligence

Explanation Canvas

All details of the course syllabus will be published on the Canvas page.

Recommended background knowledge

Knowledge of programming, statistics, qualitative and quantitative research methods