Admission requirements
Only students of the Advanced MSc International Relations and Diplomacy can take this course.
Description
This course explores the implications and applications of AI in global affairs, providing students with an understanding of its potential and limitations.
We begin by examining how AI models function, what they can achieve, and where their current limitations lie. Next, we explore real-world applications of AI in international relations and diplomacy, analyzing case studies and emerging trends.
Throughout the course, students will develop their own proposals to address global questions and challenges using AI-driven solutions. Finally, we will engage in discussions on the ethics of AI and the regulatory frameworks shaping its use in international contexts.
Course objectives
By the end of this course, learners will:
Gain familiarity with widely used AI models, including machine learning models and large language models.
Understand the implications, opportunities, and limitations of AI in global affairs.
Develop the ability to propose AI-driven solutions to contemporary challenges in international relations and diplomacy.
Critically evaluate the ethical considerations and regulatory landscapes surrounding AI in international contexts.
Timetable
On the right-hand side of the programme front page of the studyguide you will find a link to the online timetables.
Mode of instruction
Seminar, class discussion, and guest lectures.
Study load: 140 hours
Assessment method
Attendance and participation (pass/fail)
In-class presentation (30%)
Mid-term assignment (30%)
Final project (40%)
Failed partial grades or components should be compensated by passed partial grades or components. The calculated grade must be at least 5.50 to pass the course. It is not possible to re-sit a partial grade or component once you have passed the course.
Passed partial grades obtained in the academic year 2025-2026 remain valid during the academic year 2026-2027.
Should a student fail the overall course, the student can complete the course in the next academic year. In cases of exceptional circumstances, a student may apply to the board of examiners for a resit to complete the course in the same academic year.
Reading list
The reading list will be available on Brightspace one week before the starting date.
Registration
TBA
Contact
Dr. Arash Pourebrahimi a.pourebrahimi.andouhjerdi@fgga.leidenuniv.nl
Remarks
This course is an elective designed for MIRD students who have taken Quantitative Research Methods. (First year students qualify only if they can exhibit a solid understanding of fundamental statistical methods, including hypothesis testing and multiple regression analysis.)
This elective is conditional on at least 5 students registering for this course.
Second year students have priority for the registration to this course.