MVZn5082 Data-Driven Research and AI Tools for International Relations and European Studies

Faculty of Social Studies
Spring 2025
Extent and Intensity
2/0. 4 credit(s). Type of Completion: z (credit).
In-person direct teaching
Teacher(s)
Ing. Mgr. Adriana Ilavská, Ph.D. (lecturer)
Guaranteed by
prof. PhDr. Zdeněk Kříž, Ph.D.
Department of International Relations and European Studies – Faculty of Social Studies
Contact Person: Olga Cídlová, DiS.
Supplier department: Department of International Relations and European Studies – Faculty of Social Studies
Timetable
Tue 10:00–11:40 P22
Prerequisites
The course is conducted in English (with all readings and discussions in this language), so students should feel confident reading, discussing, and writing in the language to participate fully in class activities and assignments.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 30 student(s).
Current registration and enrolment status: enrolled: 25/30, only registered: 0/30, only registered with preference (fields directly associated with the programme): 0/30
fields of study / plans the course is directly associated with
Course objectives
The primary goal of this course is to familiarize students with data sources and AI tools that can be ethically utilized to enhance not only their academic research but also a wide range of professional tasks. While the course emphasizes improving research and analytical capabilities in International Relations and European Studies, it also highlights the versatility of these tools in non-academic settings. The course emphasizes practical applications, ethical considerations, and the potential of AI tools to support critical tasks, such as data analysis, literature reviews, and effective problem-solving in professional settings. This course provides hands-on experience with data and AI tools, equipping students with practical skills to apply data-driven research methods in both academic and professional settings.
Learning outcomes
Upon successful completion of this course, students will be able to:
- Identify and effectively use diverse qualitative and quantitative data sources and databases relevant to International Relations and European Studies.
- Leverage AI tools to support key research activities, including literature reviews, data analysis, methodological integration, and the presentation of results.
- Integrate data and methods while ensuring ethical considerations guide their research practices and AI usage.
Syllabus
  • The course is structured to guide students through all stages of the research process, integrating diverse data types and AI tools.
  • The semester begins with an exploration of literature sources and the use of AI tools for identifying research questions and conducting literature reviews. Students will learn to critically evaluate studies, integrate relevant data, and develop research questions.
  • Next, the focus shifts to identifying, integrating, and analyzing qualitative and quantitative data sources, with an emphasis on utilizing AI tools for analysis while maintaining ethical practices.
  • In the later stages, students will explore how AI can assist in presenting research findings effectively to academic and professional audiences.
  • The course concludes with a discussion on the broader implications of AI and data integration for research and practice in International Relations and European Studies.
  • The teacher has the right to adjust the course schedule during the semester.
Teaching methods
The teaching will take the form of interactive lectures that provide a concise introduction to key concepts, followed by practical demonstrations, guided exercises, and discussions. Guided exercises will enable students to apply these concepts immediately, solving small tasks individually or in groups, while discussions will encourage critical thinking about challenges, ethical considerations, and broader implications of AI in research.
Assessment methods
Assessment for this course will consist of three main components.
1. Practical Assignments (50%) - three tasks distributed throughout the semester, designed to apply the tools and techniques introduced. Points will be awarded for each assignment, and students will have the opportunity to resubmit the one with the lowest score as part of their final project.
2. Final Project (40%) - requires integrating course concepts, culminating in a written report
3. Active Participation in Classes (10%) - points will be awarded for engagement in discussions, group activities, and in-class exercises
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
  • Enrolment Statistics (recent)
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