Undergraduate Course: Economics of Artificial Intelligence (ECNM10126)
Course Outline
| School | School of Economics |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course introduces some key concepts and major issues in the economics of AI for students with a knowledge of economic and econometric analysis at the undergraduate level. It emphasizes the potential impact of AI on key economic outcomes such as aggregate output, inequality and unemployment. |
| Course description |
This course will aim to chart our current understanding of the evolution of AI and its likely economic impacts. The course will cover (1) AI engineering, current capabilities and trends (2) AI Automation and (3) The Macroeconomic implications of AI. While the focus will be somewhat skewed towards theory, empirical evidence will be discussed where available. The course is taught through a programme of lectures and tutorials. An independent project will be important ingredients of the course.
|
Information for Visiting Students
| Pre-requisites | None |
Course Delivery Information
|
| Academic year 2026/27, Available to all students (SV1)
|
Quota: 0 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Seminar/Tutorial Hours 9,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
165 )
|
| Assessment (Further Info) |
Written Exam
80 %,
Coursework
20 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
20% AI-assisted assignment
80% exam
|
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a knowledge and understanding of key concepts, issues and models in the economics of AI, along with empirical evidence (where available) on and policy implications of those models and a deeper understanding of recent research activity in some more specialised areas.
- Research and investigative skills such as problem framing and solving and the ability to assemble and evaluate complex evidence and arguments.
- Develop communication skills in order to critique, create and communicate understanding.
- Demonstrate personal effectiveness through task-management, time-management, dealing with uncertainty and adapting to new situations, personal and intellectual autonomy through independent learning.
- Apply practical/technical skills such as, modelling skills (abstraction, logic, succinctness), qualitative and quantitative analysis and general IT literacy.
|
Reading List
| The course readings will be based on recent academic papers. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Andrei Potlogea
Tel: (0131 6)51 3759
Email: Andrei.Potlogea@ed.ac.uk |
Course secretary | |
|
|
University Homepage