BAN443 Transforming Business with AI: The Power of Large Language Models

• Topics

  This course aims to provide a comprehensive overview of how recent developments within AI and large language models (LLMs) will transform the business landscape, research, and the labor market. The course will explore the capabilities and limitations of LLMs, ethical considerations, data analysis techniques, and the integration of AI technology into various workflows. The course is designed to equip students with a critical understanding of how LLMs transform business and research, a critical understanding of their costs and benefits, as well as the practical skills to employ LLMs for productive tasks, including big data analysis.

• Learning outcome

  Knowledge

  Upon completion of the course, the student can:

  • Explain the foundational concepts of AI and LLMs, including an overview of the current landscape of models, covering both text models like ChatGPT and others.
  • Identify applications and implications of AI in business and research.
  • Recognize the ethical considerations, privacy concerns, and risks associated with AI technology.

  Skills

  Upon completion of the course, the student can:

  • Design and execute effective prompts for interacting with LLMs.
  • Automate the use of large language models through APIs for productive purposes.
  • Analyze large text datasets and derive insightful conclusions, leveraging modern techniques to uncover themes and trends.
  • Evaluate the quality of an AI model for a given purpose.

  General Competence

  Upon completion of the course, the student can:

  • Assess the impact of AI on the labor market and organizational productivity, including a nuanced understanding of AI's strengths and weaknesses.
  • Develop and present comprehensive research projects that demonstrate practical applications of AI for big data analysis. 
  • Engage in informed discussions about the future directions of AI technology, its societal implications, potential risks, and other ethical considerations.

• Teaching

  Lectures, online surveys and quizzes, mandatory assignments, group presentations, guest lectures with business applications. 

• Recommended prerequisites

  Some knowledge of Python is an advantage but not necessary. The course is structured to accommodate students with varying levels of programming experience, offering introductory content to ensure all participants can effectively engage with the course material.

• Credit reduction due to overlap

  There is no direct overlap with other courses. Students who take the course might also be interested in taking BAN432 Applied Textual Data Analysis For Business And Finance. 

• Compulsory Activity

  1. Three group presentations in front of the class.

  All group members need to be active and present during both presentations. Group size is restricted to 3-4 students, but students can ask for an exemption if they want to work in smaller/larger groups. Students also need to hand in slides for each presentation. The slides need to be comprehensive. 

  2. Participation in four mandatory online surveys. 

  The online surveys will be administered during class. The surveys will, among other things, include multiple choice questions of concepts covered in class. The surveys will give a pass/fail grade. It is necessary to pass at least 3 out of the 4 online surveys to get a course approval. 

  The course approval is only valid for one semester.

• Assessment

  The assessment is based on the final paper, and will be written in the same groups as with the presentations.

  The paper is due 4 weeks after the final presentation.

  The paper must be written in English.

• Grading Scale

  A-F.

• Computer tools

  Python and ChatGPT API.

• Literature

  Selected articles in Leganto