This course will examine critical issues, current debates and key concepts related to digital transformation of society, with a focus the development, deployment and regulation of disruptive technologies in the public sector. The course will be divided into project teams working on different topics around a real-world case study. Each team will meet regularly with the supervisor to design and execute the project, to discuss progress, results and final output.

(i) What Makes Health-AI Fail? Diagnosing Failures Across the Lifecycle (contact: Ali Ozkes): Despite growing investments and rising expectations, many AI initiatives in healthcare fail across different stages of their lifecycle, from planning to actual use or scale-up. Some technologies are never adopted, while others are discontinued due to a mix of technical, regulatory, and organizational issues. Building on scholarly literature about AI in health and digital transformation, this project investigates why such failures occur and how they differ depending on the implementation stage. The goal is to map key causes, ranging from data limitations and lack of stakeholder trust to ethical concerns and governance gaps, through literature review, analysis of publicly available data, and expert interviews. The project seeks to provide a multi-perspective, evidence-based assessment of why healthcare AI often falls short of its promise and what lessons can be learned.

(ii) AI Literacy and Candidate Perspectives in Algorithmic Hiring (contact: Ali Ozkes): AI tools are now standard in recruitment, from automated résumé screening to video-based interviews. While these technologies promise efficiency and fairness, jobseekers, especially first-time entrants to the labor market, often encounter them with a mix of curiosity and concern. This project explores how candidates’ AI literacy shapes their perceptions of fairness and transparency, their preparation tactics, and their willingness to apply for jobs. Drawing on relevant literature, surveys, and labor market data, the aim is to assess how knowledge about AI impacts behavior and decision-making in modern recruitment processes.

(iii) AI, Language, and Bureaucracy (contact: Shefali Virkar): International students face several unique challenges when opting to pursue higher education in a foreign country. Chief among these are the linguistic barriers that they come up against when interacting with government systems in the host nation. To navigate through  linguistically complex bureaucratic environments, international students have increasingly come to rely on Artificial Intelligence technologies. This research project proposes to investigate the concerns international students have when navigating a foreign language environment, and the AI-based language tools they use as they interact with government systems in Austria.

(iv) Generative Artificial Intelligence and Education (contact: Shefali Virkar): Generative AI has brought academia to an inflection point, forcing educational institutions and policymakers to rethink traditional approaches to learning, teaching, and assessment. Most research on Generative AI does not take into account either how university students actually use Generative AI in different contexts, or the extent to which the crossover or transfer of use from non-academic to academic contexts. The aim of this project is to critically examine how students engage with Generative AI tools in both academic and non-academic settings, and to explore how this knowledge can be harnessed in the classroom of the future to support traditional teaching methods and foster critical thinking.

Upon completion of the course, students are able to

- Critically evaluate a research question in the broad topic of Digital Economy from the view of (micro)economics and information systems
- Plan a research project to answer such a research question
- Perform a structured literature search on a given topic
- Design an experiment or empirical study for a specific research question
- Identify appropriate analysis methods
- Conduct appropriate statistical analyses for said data
- Interpret the results of said analyses and evaluate them critically
- Write a research paper according to current academic standards from the relevant disciplines describing the research project and its outcomes

Attendance is mandatory.

Pursuant to the general guidelines issued by the Vice-Rector for Academic Programs and Student Affairs, the attendance requirement is met if a student is present at least 80% of the time.

Students conduct an interdisciplinary research project spanning all stages: from defining a research question, doing a literature research, to stating hypotheses, implementing an experimental or empirical study, analysing the experimental or empirical data and interpreting and critically reflecting the findings. Besides acquiring methodological knowledge, students gain practical experience in planning and carrying out a research project and also take the perspective of a project manager. Students work in groups and are coached regularly by the two lecturers.

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