OAIN 2110 - Big Data and Artificial Intelligence for Intelligence

***UPDATED for 2025/26***

This course examines the transformation of intelligence policies, practices, and epistemologies driven by Big Data and Artificial Intelligence (AI). It explores how digital infrastructures, Big Data and AI analytics, and algorithmic reasoning reshape the ways intelligence is produced, validated, and implemented in contemporary security environments and geopolitical contexts. Combining theoretical analysis with empirical case studies, it investigates the technological features, strategic, ethical and (geo)political implications, and oversight of AI-driven intelligence across different contexts. Through a multi-disciplinary approach combining Security Studies, Intelligence Studies, Science and Technology Studies (STS), and Data Studies, the course provides students with a critical understanding of the ways data, analytics, and automation are redefining expertise, secrecy, and knowledge in Intelligence Communities.

LEARNING OUTCOMES

1. Technology knowledge acquisition: Equipping students with foundational knowledge of Big Data analytics and AI technologies relevant to intelligence, including core concepts, methods, applications, and associated challenges.
2. Intelligence Domain Understanding: Developing students' understanding of why intelligence communities adopt Big Data and AI, examining needs, gaps, resources, and ecosystem dynamics across organizational and geopolitical contexts.
3. Critical Evaluation: Strengthening students' ability to critically assess the risks and societal, ethical, and geopolitical implications of datafication and the automation of intelligence processes.
4. Impact Assessment: Enabling students to evaluate the operational, institutional, ethical, and geopolitical impacts of Big Data and AI on intelligence practice.
5. Strategic and Policy Insight: Preparing students to formulate informed strategic and policy recommendations for intelligence communities and political decision-makers.

PROFESSIONAL SKILLS

• Research & Innovation
• Technology assessment
• Risk assessment and decision-support
• Strategic thinking
• Policy analysis and impact assessment
• Strategic and political consulting

Ayse Alyzée CEYHAN

Séminaire

English

- In Class Presence: 2 hours a week /24 hours a semester
- Reading and Preparation for Class: 4 hours a week/48 hours a semester
- Research and Preparation for Group Work: 2,5 hours a week /30 hours a semester
- Research and Writing for Individual Assessments: 4 hours a week/48 hours a semester

A background in Intelligence and Security Studies is highly recommended and a strong interest in emerging technologies is essential. This course will require students' deep engagement to explore a complex topic through an interdisciplinary approach with weekly reading assignments, exercises, in-class tests, a group work, and a final paper. No math or computer science background is required.

Spring 2025-2026

1. Reading memos, class participation, and in-class/online exercises :30%
Readings play a pivotal role for this course. Students will submit 1,5-2-page reading memos for the first six/seven sessions, followed by selected submissions in the second quarter of the semester.
2. In-class knowledge test: 10%
Session 8
3. Group work:30%
Groups of approximately five students will produce an 8–10-page analytical paper on an assigned topic, which will be presented in class. Group work integrates conceptual analysis with empirical cases.
4. Final individual paper: 30%
A 6–8-page individual paper will conclude the semester. The final paper requires engagement with course readings, lectures, and distributed materials. Topics will be distributed by the session 5 or 6.

• The course combines professor's lectures, readings, case studies and class discussion to develop theoretical, technical, and practical understanding.
• Readings and memos: Feedback will be provided after each memo submission.
• Test: Feedback will be provided the week after the test.
• Group work: Feedback will follow the in-class presentation and written submission.
• Final paper: Feedback will be provided in the final grade sheet.