LEARNING OUTCOMES
After completing the course, students will be able to …
- Recall and distinguish AI-based methods to synthesise and analyse data in the context of empirical research.
- Execute a subset of these methods independently and adjust them to specific applications.
- Contrast the strengths and weaknesses of AI-based methods and identify appropriate use-cases.
- Debate the ethical implication of leveraging AI in the context of empirical research.
Credits: 2
Schedule: 21.10.2024 - 29.11.2024
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Christian Guckelsberger
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
This course represents one of the possible specialisations following the basic research methods teaching in CS-E5010 Research Methods: Foundations D. The goal is to equip students with the knowledge, skills and critical thinking to use AI (e.g. foundation models, reinforcement learning) for data synthesis (e.g. interview data, behaviour data) and analysis (e.g. automated coding). Use cases comprise, amongst others, the automation of empirical research (e.g. automated playtesting), preparation of research studies (e.g. survey development), or estimation of user characteristics and behaviours in online settings (e.g. adaptive user interfaces, procedural content generation).
The needs and interests of students from different majors and programmes (e.g. Information Networks, Software Engineering, Game Design and Development) will be met based on a diverse set of research examples and custom-tailored learning and exercise materials.
Alternatives to this specialisation course:
- Research Methods: Case studies & Design Science (CS-E5011).
One specialisation cannot necessarily substitute another as degree requirement, and students are consequently encouraged to check their desired course choice against the requirements of their respective major. The same holds if students wish to take multiple specialisation courses, and wonder whether the additional credits can be counted toward their study progress.
Assessment Methods and Criteria
valid for whole curriculum period:
Lab performance, classroom activity, and assignments.
Workload
valid for whole curriculum period:
Lectures, labs, weekly learning tasks, assignment.
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language: English
Teaching Period: 2024-2025 Autumn II
2025-2026 Autumn IIRegistration:
Max. 40 students will be accepted to the course. Priority is given to 1. Master's students for whom this course is mandatory; 2. Other Master's and Doctoral students 3. Exchange students.