Digital Transformation of Economy and Society in the Age of Generative AI 1600-SZD-ID-TCGS
The course focuses on a comprehensive overview of the digital transformation of the economy and society, with particular emphasis on the processes of datafication, algorithmization, and platformization. The mechanisms enabling companies and institutions to generate economic value from data—now a key resource in contemporary economies—will be discussed.Datafication is the process of converting various aspects of economic, social, and individual life into data, which is then analyzed and used for decision-making and the development of business strategies. This process encompasses data extraction, structuring, integration, and analysis, with its outcomes applied in management practice and organizational process automation.Algorithmization is a mechanism that enables the processing of data into valuable information, crucial for decision-making and process optimization. Topics include machine learning (ML), deep learning (DL), natural language processing (NLP), and the latest advances in Generative AI (GenAI), such as GPT language models. Participants will explore new opportunities arising from GenAI, including process automation, content generation, and support for business innovation.Platformization refers to the development and expansion of digital platforms, which have become fundamental infrastructure within the digital economy. The course will analyze the impact of platforms on market relationships, business competitiveness, and new business models built upon data and algorithms.The classes will also cover recent trends stemming from GenAI development, including potential scenarios for transforming economic sectors and society through this technology. Participants will be introduced to practical examples of GenAI applications, such as automating creative processes, intelligent recommendation systems, dynamic customer relationship management, and value creation through new digital services.By combining the theoretical content presented in the textbook with the analysis of current technological trends, the course will provide participants with comprehensive knowledge essential for effective operation and strategic decision-making in the digital economy.The course will have a clear structure. Students will first independently study successive chapters from the textbook "The Economics of Digital Transformation" by Katarzyna Śledziewska and Renata Włoch. Subsequently, the content will be discussed, analyzed, and critically evaluated during class meetings.An additional component of the course will be a practical task for students: preparing presentations illustrating how datafication, algorithmization, or platformization influence areas related to their academic disciplines or professional interests. Students will present their prepared analyses to the group, facilitating perspective exchange and deeper understanding of the practical consequences of digital transformation across various fields.
Course coordinators
Learning outcomes
Knowledge | The graduate knows and understands:
WG_02 - the main development trends in the disciplines of the social sciences in which the education is provided
Skills | The graduate is able to:
UW_01 – make use of knowledge from various fields of science, in particular the social sciences in order to creatively identify, formulate and innovatively
solve complex problems or perform tasks of a research nature, and in particular to: define the purpose and object of scientific research in the field of the social sciences, formulate a research hypothesis; develop research methods, techniques and tools and apply them creatively; make inferences based on scientific findings
UK_04 - participating in scientific discourse in the field of the social sciences
Social competences | The graduate is ready to
KK_01 - critically evaluating achievements within a given scientific discipline in the field of the social sciences
Assessment criteria
Description of requirements related to participation in classes, including the permitted number of explained absences: Student requirements include regular class attendance and systematic preparation through independent reading of successive textbook chapters. Students are required to actively participate in discussions during class meetings and to prepare and deliver a presentation on the impact of datafication, algorithmization, or platformization processes within a field of their choice. Attendance and active engagement in class activities are mandatory for passing the course. In case of failing to obtain a pass grade on the first attempt, students are required to prepare and deliver an additional presentation and take an oral exam covering topics discussed during classes and included in the textbook. Permitted number of absences: 1.
Principles for passing the classes and the subject (including resit session): Course completion is based on two key components: active class
participation, including regular preparation for discussions and active engagement in the analysis of discussed topics, and the preparation and delivery of a student presentation exploring the impact of datafication, algorithmization, or platformization on a chosen field.
Methods for the verification of learning outcomes: Learning outcomes will be verified through the assessment of the quality and depth of students'
participation in class discussions and the evaluation of their presentations. Presentation evaluation criteria include substantive correctness, ability to
critically analyze discussed issues, clarity and logical structure of the presentation, as well as the student's ability to link the presented content with topics covered during classes and in the textbook. Additional evaluation criteria include students' ability to effectively articulate arguments and appropriately select examples and literature references. In case of retake requirements, learning outcomes will also be verified through an oral examination.
Evaluation criteria: Learning criteria primarily include understanding key concepts related to digital transformation, particularly the processes of datafication, algorithmization, and platformization, as well as the ability to critically analyze these processes. Students should demonstrate the capacity to apply theoretical knowledge to practical examples and identify the consequences of these processes within selected social and economic domains. An important criterion is the ability to independently formulate and present insights and arguments based on the relevant literature and discussions conducted during classes. Additionally, student activity and engagement in class, preparation for discussions, teamwork skills, and the effective communication of their own conclusions will be evaluated.
Bibliography
Śledziewska, K., Włoch, R. (2021). The Economics of Digital Transformation. The Disruption of Markets, Production, Consumption, and Work. Routledge.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: