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2024/2025  KAN-CDIBV1004U  Artificial Intelligence in Business and Society

English Title
Artificial Intelligence in Business and Society

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 100
Study board
Master of Science (MSc) in Business Administration and Digital Business
Course coordinator
  • Daniel Hardt - Department of Management, Society and Communication (MSC)
Main academic disciplines
  • Information technology
  • Innovation
Teaching methods
  • Blended learning
Last updated on 23-01-2024

Relevant links

Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors:
  • Explain and analyze the key ideas and techniques underlying Artificial Intelligence technologies, including basic approaches to Machine Learning, basic concepts of linguistics and language processing, and basic concepts of visual processing.
  • Critically evaluate and compare the development and impact of Artificial Intelligence technologies in different business areas
  • Identify and analyze specific problems which can be addressed by new Artificial Intelligence techniques; perform technical assessment of proposed AI solutions
  • Critically assess the future potential of AI, in particular the potential for Artificial General Intelligence, assessing recent developments in the light of historical and current discussion.
Course prerequisites
interest and some experience with software and programming
Artificial Intelligence in Business and Society:
Exam ECTS 7,5
Examination form Oral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance, see also the rules about examination forms in the programme regulations.
Individual or group exam Oral group exam based on written group product
Number of people in the group 2-5
Size of written product Max. 15 pages
Assignment type Project
Release of assignment Subject chosen by students themselves, see guidelines if any
Written product to be submitted on specified date and time.
20 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale 7-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content, structure and pedagogical approach

This course examines new Artificial Intelligence technologies that are rapidly transforming the digital marketplace. A few short years ago Artificial Intelligence technology was primarily relegated to the realm of fantasy and science fiction – now it is driving new businesses and technologies in a wide range of areas, such as machine translation, sentiment analysis, voice-based assistants, and facial recognition. We will explore the key ideas underlying this revolution in AI technology, looking at the historical roots of AI, and the cognitive revolution in the key fields of language processing and visual processing. 

Students will get hands-on experience with newly developed tools for working with a variety of AI technologies. We will see how these technologies are central to the strategies of IT Giants like Google, Amazon, and Facebook  – and we will look at speculation about how these developments may well accelerate in the near future. A key point is that AI technologies are becoming widely available, with public descriptions and open-source implementations. This means that they are no longer the province of large powerful companies, and we will see how AI technologies are playing an increasingly important role now for many small companies. Students will have an opportunity with hands-on exercises to learn how to access and deploy many of the leading technologies.  

This will include machine learning platforms, chatbot systems and large language models, and

 other current AI platforms. They will also explore fundamental issues about the

ultimate potential of AI and related ethical and societal questions.

Description of the teaching methods
The class is a mixture of recorded lectures, other online activities, face to face lecture/discussion sessions, and practical exercises in a hands-on session, where students get experience in the development, deployment and assessment of computational AI tools. This will include chatbot systems and large language models, and other recent AI platforms and tools.
Feedback during the teaching period
Students submit result of hands-on exercises each week, and they receive detailed written feedback on their submissions before the following session.

The instructor also has weekly office hours where the students can get feedback of various forms, including followup on their weekly activity sessions, clarification and discussion of weekly readings and lectures, and discussion of plans for course project.

Students periodically are presented with online quizzes and other activities where they receive automatic feedback on their responses.

Students receive written and/or oral feedback on their plans for a course project.
Student workload
Lectures 24 hours
Workshops 24 hours
Class Preparation 98 hours
Exam and Preparation for Exam 60 hours
Expected literature


The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas before they buy any material.


Turing, A. (1950). Computing machinery and intelligence. Mind59(236), 433.


Ferrucci, D., et al. (2010). Building Watson: An overview of the DeepQA project. AI magazine31(3), 59-79.

Breck, E., & Cardie, C. (2017). Opinion mining and sentiment analysis. In The Oxford Handbook of Computational Linguistics 2nd edition.


Deep Learning for AI. Bengio et al. Communications of the ACM, 2021.




Last updated on 23-01-2024