Abschlussarbeiten
Bachelor- und Masterstudierende der Wirtschaftsinformatik an der WiSo-Fakultät der Universität Potsdam können auf Anfrage hin ihre Abschlussarbeit unter Betreuung des Lehrstuhls schreiben. Der Ablauf hierfür sieht folgendermaßen aus:
- Überlegen Sie sich ein Thema, das zum Forschungsbereich des Lehrstuhls passt, oder wählen Sie eines der ausgeschriebenen Themen.
- Greifen Sie auf den Moodlekurs zu, der weitere Informationen über den Ablauf bezüglich Abschlussarbeiten enthält: Moodle-Kurs
- Füllen Sie das folgende Formular zur Anfrage einer Abschluss aus: Anfrageformular
- Nach der Bearbeitung Ihrer Anfrage melden wir uns bei Ihnen zurück, um ein Erstgespräch mit Ihnen zu vereinbaren. In diesem Gespräch wird die Abschlussarbeit besprochen und Sie werden gebeten, ein 2-3 seitiges Exposé zu verfassen. Dieses sollte folgende Teile enthalten: Relevanz der Frage, Stand der Literatur & Theoriefundierung, Forschungsfragen, Methoden und erwartete Ergebnisse.
Offene Themen für Abschlussarbeiten
Enhancing learning with AI (Bachelor)
Successful learning has been described as occurring through interaction between a student and more knowledgeable others, typically teachers. However, the emergence of large language models, such as ChatGPT, challenges the anthropocentric conception of what a teacher can be. Consequently, learning can no longer be understood solely as emerging within a sociocultural space by human-to-human interaction, rather than being a sociomaterial enactment from both humans and technology. Accordingly, learning is shaped not only by social relations but also by the material characteristics of the technology involved, that unlike social relations can be intentionally designed.
This bachelor’s thesis applies a Design Science Research approach to explore the design principles of AI systems that promote successful learning. Drawing on educational theory, the study designs and evaluates an AI system aiming to enhancing students’ learning through interaction with AI.
Requirements
The following skills are required in order to succeed:
- Programming experience both front- and backend development
- Interest in learning more about modern AI-system-designs like RAG
- Interrest in the research domain.
Additionally you need to be enrolled in a Bachelor programm at the University of Potsdam.
Potential supervisor:
Till Schirrmeister
The Emotional Divide: Affective Polarization Across different Social Media Platforms (Master)
Description
Affective polarization is defined as a growing emotional division between groups and has emerged as a defining challenge of the digital age. Social media platforms, such as TikTok, Reddit, YouTube, X or Bluesky , facilitate rapid information diffusion and enables users to form and reinforce emotion-driven communities. Due to the platform infrastructures and narratives and connected emotions propagate differently across platform ecosystems, creating reinforcement of affective issues within a digital social sphere.
This thesis explores a cross channel analysis to understand how affective polarization manifests, evolves, and diffuses across distinct social media environments. Drawing on existing theories of networked communication and platform-mediated discourse, it examines how content characteristics (AI generated and human generated), audiences or algorithmic factors shape the emotional gap between opposing groups. By integrating data from multiple platforms, the qualitative or quantitave analysis seeks to uncover how cross-channel dynamics intensify affective divides and discovers potential pathways for mitigation of negative effects.
Requirements
You should be interested in cognitive emotion science and how support social cohesion to connect the phenomenon of affective polarization with the information systems research.
Potential supervisor:
Vivian Mantz
References
Bakker, B. N., & Lelkes, Y. (2024). Putting the affect into affective polarisation. Cognition and Emotion, 38(4), 418–436.
Boxell, L., Gentzkow, M., & Shapiro, J. M. (2024). Cross-country trends in affective polarization. The Review of Economics and Statistics, 106(2), 557–565. doi.org/10.1162/rest_a_01160
Iyengar, S., Sood, G., & Lelkes, Y. (2012). Affect, not ideology: A social identity perspective on polarization. Public Opinion Quarterly, 76(3), 405–431. doi.org/10.1093/poq/nfs038
Iyengar, S., Lelkes, Y., Levendusky, M., Malhotra, N., & Westwood, S. J. (2019). The origins and consequences of affective polarization in the United States. Annual Review of Political Science, 22(1), 129-146. https://doi.org/10.1146/annurev-polisci-051117-073034
Piccardi T., Saveski M., Jia C., Hancock J., Tsai J., & Bernstein M. (2024). Social media algorithms can shape affective polarization via exposure to antidemocratic attitudes and partisan animosity. Computers and Society.
Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and information diffusion in social media—Sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29(4), 217–248. doi.org/10.2753/MIS0742-1222290408
Torcal, M., & Harteveld, E. (Eds.). (2023). Handbook of affective polarization. Edward Elgar Open Access (CC-BY-NC-ND license).
The Hidden Cost of AI: The Impact of Non-Causal Relationships (Master)
Description:
In recent years, the widespread adoption of machine learning (ML) and artificial intelligence (AI) technologies has revolutionized various industries, from business to healthcare. However, a critical limitation inherent in many AI models is their reliance on associative relationships rather than causal ones (Pearl 2018). This raises concerns regarding the potential for these models to make misjudgments and yield unintended consequences, particularly in scenarios where causal understanding is important.
This thesis seeks to explore the hidden costs of AI by investigating the implications of relying on associative relationships in AI models. Some even propose, that AI is not able to learn anything at all (Bishop 2021). The central hypothesis is that the failure to uncover causal relationships may lead to inefficient decisions and negative outcomes, posing risks for businesses or social applications such as digital health.
The study will evaluate these hidden costs by replicating previous machine learning applications and reevaluating them using causal models, investigating an economic or societal impact of using AI.
By highlighting the importance of causal inference in AI models, this thesis aims to motivate the development of more “causable” (Chou et al. 2022) AI systems, thereby ensuring their effective deployment.
Requirements:
For this thesis you should be interested in (critical) perspectives on artificial intelligence and have previous experiences in data science projects.
Potential supervisor:
Kai Schewina
References
Bishop, J. M. (2021). Artificial intelligence is stupid and causal reasoning will not fix it. Frontiers in Psychology, 11, 2603.
Chou, Y. L., Moreira, C., Bruza, P., Ouyang, C., & Jorge, J. (2022). Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications. Information Fusion, 81, 59-83.
Pearl, J. (2018). Theoretical impediments to machine learning with seven sparks from the causal revolution. arXiv preprint arXiv:1801.04016.
Delegation to Generative AI in Public Administration: Impacts on Trust, Digital Sovereignty, and Bias
Description
The rapid spread of Generative Artificial Intelligence (GenAI) technologies, such as large language models and conversational agents, transforms how public administrations work and how citizens interact with the state and its authorities (Yun et al., 2024). New technologies such as GenAI do not only promise efficiency gains and cost reductions, they also change these interactions (Lindgren et al., 2019). Tools like GenAI-based chatbots or decision-support systems are increasingly used to answer citizen inquiries, process support cases, and to perform predictive tasks such as in policing. At the same time, delegating tasks to GenAI systems raises concerns about transparency, accountability, digital sovereignty, and algorithmic bias. In this context, public administrations become part of an AI delegation ecosystem in which authorities, citizens, and companies delegate tasks to agentic IS artifacts while also shaping these technologies through data provision, procurement, and design choices (Baird & Maruping 2021). One possible angle for this thesis could be to investigate which factors influence the adoption and enactment of GenAI in public organizations, and how this affects citizen trust, perceived legitimacy, and digital sovereignty, drawing on the IS delegation framework for agentic IS artifacts (Baird & Maruping, 2021).
The thesis may take one of the following perspectives (or even take another perspective of your choice, interest and access):
Interaction perspective: How does delegation to GenAI influence how public organizations interact with citizens, and how does this reshape these interactions?
Outcome perspective: How does the delegation of communication or decision-support tasks to GenAI systems in public administration influence digital sovereignty, trust, and algorithmic bias?
Depending on your interests, the thesis can focus on a specific domain (health, tax, …) or a specific level of government (e.g., municipal, state, or federal agencies). Methodologically, the thesis can apply a structured literature review (Bachelor theses only), surveys of citizens interacting with GenAI-based public service tools, interviews with public administration employees, or design-science approaches prototyping and evaluating a GenAI-based assistant.
Requirements & Contact
Interest and previous experiences with the research topic as well as familiarity with any appropriate methods and context are beneficial but not mandatory.
Potential supervisor:
Kai Schewina
References
Baird, A., & Maruping, L. M. (2021). The next generation of research on IS use: A theoretical framework of delegation to and from agentic IS artifacts. MIS Quarterly, 45(1), 315–341.
Kuss, P., & Meske, C. (2025). From entity to relation? Agency in the era of artificial intelligence. Communications of the Association for Information Systems, forthcoming.
Lindgren, I., Madsen, C. Ø., Hofmann, S., & Melin, U. (2019). Close encounters of the digital kind: A research agenda for the digitalization of public services. Government Information Quarterly, 36(3), 427–436.
Yun, L., Yun, S., & Xue, H. (2024). Improving citizen-government interactions with generative artificial intelligence: Novel human–computer interaction strategies for policy understanding through large language models. PLOS One, 19(12), e0311410.