Pro-/Haupt- und Forschungsseminar VSR (SS 2026)

Research Questions

• What is the history of graphical user interfaces, conversational interfaces (specifically chatbots), and how do these inform the interfaces of LLMs? Research and provide a brief overview of the development of user interfaces.
• Conduct a systematic analysis of the interfaces from Large Language Models. For this, start with collecting at least 10 different providers from LLMs. Interact with these LLMs and record your interactions. Then start labeling the interactions of both partners regarding all available interface elements. Both students should do this on their own without talking about the results. Research on useful qualitative methods for this, like ‘Grounded Theory’.
• Compare your labeling, discuss differences together with your advisor and create a list of UI elements. Describe how they look, where they are located, what their function is, and other relevant criteria.
• Create a prototype on the stereotypical LLM interface and present it.
• Compare the interfaces of LLMs with other chatbots. Where do you find differences? How can you explain them?

Literature

• Own research
• Khan, R., Das, A. (2018). Introduction to Chatbots. In: Build Better Chatbots. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-3111-1_1
• Mohit Jain, Pratyush Kumar, Ramachandra Kota, and Shwetak N. Patel. 2018. Evaluating and Informing the Design of Chatbots. In Proceedings of the 2018 Designing Interactive Systems Conference (DIS ’18). Association for Computing Machinery, New York, NY, USA, 895–906. https://doi.org/10.1145/3196709.3196735
• Adamopoulou, E., & Moussiades, L. (2020). An overview of chatbot technology. In Artificial Intelligence Applications and Innovations: 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5–7, 2020, Proceedings, Part II 16 (pp. 373-383). Springer International Publishing. https://doi.org/10.1007/978-3-030-49186-4_31
• Traubinger, V., Gaedke, M. (2024). Interaction Design Patterns of Web Chatbots. In: Stefanidis, K., Systä, K., Matera, M., Heil, S., Kondylakis, H., Quintarelli, E. (eds) Web Engineering. ICWE 2024. Lecture Notes in Computer Science, vol 14629. Springer, Cham. https://doi.org/10.1007/978-3-031-62362-2_22
• Tidwell, J., Brewer, C., Valencia, A.: Designing Interfaces: Patterns for Effective Interaction Design. O’Reilly, Beijing [China]; North Sebastopol, CA, third edition edn. (2020) https://katalog.bibliothek.tu-chemnitz.de/Record/0-1684818338?sid=60672775
• Savin-Baden, M., & Major, C. H. (2025). Qualitative research: The essential guide to theory and practice. Routledge. https://katalog.bibliothek.tu-chemnitz.de/Record/0-1939098580?sid=60673159
• Anker Helms Jørgensen and Brad A. Myers. 2008. User interface history. In CHI ’08 Extended Abstracts on Human Factors in Computing Systems (CHI EA ’08). Association for Computing Machinery, New York, NY, USA, 2415–2418. https://doi.org/10.1145/1358628.1358696
• Traubinger, V., Heil, S., Grigera, J., Garrido, A., Abhyankar, S., Gaedke, M. (2025). An Analysis of Federal and Municipal Chatbots in Germany. In: Følstad, A., et al. Chatbots and Human-Centered AI. CONVERSATIONS 2024. Lecture Notes in Computer Science, vol 15545. Springer, Cham. https://doi.org/10.1007/978-3-031-88045-2_13

Research Questions

• What are guardrails for AI agents?
• What are the limitations of agent guardrails? What can be enforced with guardrails and what cannot?
• How can guardrails be implemented in AI agents? Identify and compare different approaches and technologies for guardrails in AI agents. Present your findings.

Literature

• S. A. Akheel, “Guardrails for large language models: A review of techniques and challenges,” J Artif Intell Mach Learn & Data Sci, vol. 3, no. 1, pp. 2504–2512, 2025.
• Own research

Research Questions

• What are local AI agents? What are local agent platforms?
• How can local AI agents interact with local resources?
• Create a demo of a basic local AI agent for a research context!

Literature

• Own research

Research Questions

• Introduction: Scientific Knowledge Graphs (SKGs) contain information about publications, datasets, researchers, and institutions. To prevent the creation of isolated data silos, it is crucial that SKGs rely on standardized, well-founded ontologies rather than ad-hoc data models. This is where top-level ontologies like the Basic Formal Ontology (BFO) come into play. As a philosophically grounded and internationally standardized framework (ISO/IEC 21838-2), BFO provides a rigorous structural foundation for domain ontologies, facilitating data integration across diverse disciplines.
• What does “BFO compliance” entail, and what are the theoretical and practical implications of aligning a domain-specific ontology with a top-level ontology like BFO?
• What are the necessary methodological steps, constraints, and prerequisites to design and formalize an SKG ontology so that it can be officially declared a BFO-module?
• Which BFO superclasses are most suitable for representing the core elements of a Scientific Knowledge Graph? How can specific SKG concepts (like researchers, publications, or research data) be systematically mapped to these upper-level categories?

Literature

• ISO/IEC 21838-2:2021, Top-level Ontologies – Part 2: Basic Formal Ontology (BFO). International Organization for Standardization, 2021. Online. Available: https://www.iso.org/standard/74572.html
• https://pmc.ncbi.nlm.nih.gov/articles/PMC4108877/
• https://www.nature.com/articles/s41597-025-04580-1
• https://arxiv.org/abs/2509.01536
• Own research

Research Questions

• Introduction: In recent years, Large Language Models (LLMs) have significantly advanced the automation of Knowledge Graph Construction (KGC). The latest paradigm shift moves beyond static prompting towards “Agentic Frameworks” or “Agentic AI”. However, as a rapidly emerging field, the term “agentic” lacks a unified academic definition and is often used heterogeneously across research papers.
• How are “agentic frameworks” or “AI agents” defined across current research literature, and how can these varying definitions and architectural patterns be systematically grouped?
• Which specific agentic frameworks and tools are currently utilized in state-of-the-art papers for KGC?
• What are the key benefits, methodological limitations, and open challenges of applying these agentic approaches to KGC compared to traditional NLP or purely prompt-based LLM extraction pipelines?

Literature

• Own research

Research Questions

• What practical risks do current AI agents pose? In what form can these risks manifest?
• Where have AI Agents already caused issues? Which concrete incidents already happened, caused by or involving AI Agents?
• How can risks of AI Agents be identified in advance and managed accordingly?
• Present your findings!

Literature

• National Institute of Standards and Technology, “Artificial Intelligence Risk Management Framework: AI RMF 1.0,” U.S. Department of Commerce, NIST AI 100-1, Jan. 2023. doi: 10.6028/NIST.AI.100-1
• J. Kraprayoon, Z. Williams, and R. Fayyaz, “AI Agent Governance: A Field Guide.” May 2025. doi: 10.48550/arXiv.2505.21808
• S. Z. U. Rashid, I. Montasir, A. Haq, M. T. Ahmmed, and M. M. Alam, “Securing Agentic AI: Threats, Risks, and Mitigation,” in International Conference on Advancement In Cyber Security and Digital Forensics, Springer, 2025, pp. 683–698.
• Own research

Research Questions

• What is an ontology, and why do we need tools to build them?
• What web-based ontology editors exist today?
• How do these tools compare in features and usability?
• What challenges do non-technical users face with these tools?
• What features are still missing?

Literature

• Own research
• T. Tudorache et al., “WebProtégé: A Collaborative Ontology Editor and Knowledge Acquisition Tool for the Web,” Semantic Web J., vol. 4, no. 1, pp. 89–99, 2013, doi: https://doi.org/10.3233/SW-2012-0057
• A. Stellato et al., “VocBench 3: A Collaborative Semantic Web Editor for Ontologies, Thesauri and Lexicons,” Semantic Web J., vol. 11, no. 5, pp. 855–881, 2020, doi: https://doi.org/10.3233/SW-200370
• A. Hemid, W. Shabbir, A. Khiat, C. Lange, C. Quix, and S. Decker, “OntoEditor: Real-Time Collaboration via Distributed Version Control for Ontology Development,” in Proc. ESWC, LNCS, vol. 14664, Springer, 2024, doi: https://doi.org/10.1007/978-3-031-60626-7_18
• M. Hofer et al., “Construction of Knowledge Graphs: Current State and Challenges,” Information, vol. 15, no. 8, Art. no. 509, 2024, doi: https://doi.org/10.3390/info15080509

Research Questions

• What are the different stages in research? Present them and explain the general workflow.
• Where can AI tools be used in this process? Where can they help? What are inherent risks in using AI tools?
• Search at least 5 different AI tools for each stage and compare them according to their functionalities (Do you find reviews/testimonials/scientific as a basis? If not, create an evaluation scheme by first making notes on all features and then use coding techniques to create themes which can be compared). All AI tools should be free, accessible, and specifically for this research step (this does NOT include general LLMs like ChatGPT!).
• Present your evaluation results on a small web application (a gitlab repository will be provided).
• If you use some of the presented tools on your own, reflect on the usage and where you had to correct the output, and present these insights and reflections.

Literature

• Own research
• https://www.tu-chemnitz.de/ub/kurse-und-e-learning/elearning/studierende/mika/index.html.en
• https://libguides.umn.edu/StructureResearchPaper
• https://www.kth.se/en/larande/sprak/utbildning/sprak/eng/writing-guide/writing-conventions/the-imrad-format-for-research-and-lab-reports-1.1395046
• Pinzolits, R. (2023). AI in academia: An overview of selected tools and their areas of application. MAP Education and Humanities, 4, 37–50. https://doi.org/10.53880/2744-2373.2023.4.37
• Venkatesh, V. Adoption and use of AI tools: a research agenda grounded in UTAUT. Ann Oper Res 308, 641–652 (2022). https://doi.org/10.1007/s10479-020-03918-9
• Oyelude AA (2024), “Artificial intelligence (AI) tools for academic research”. Library Hi Tech News, Vol. 41 No. 8 pp. 18–20, doi: https://doi.org/10.1108/LHTN-08-2024-0131

Research Questions

• What do the concepts ‘Trust’, ‘Trustworthiness’, and ‘Explainable AI’ mean in relation to the User Experience of Chatbos? Provide definitions for these terms and explain them.
• Which research is available for these concepts in relation to Public Service? Which research is specifically available for governmental / public services chatbots? How does this research inform the design of AI chatbot interfaces?
• What are typical services which are offered by governmental / public services chatbots? Take literature and conduct a small survey of AI governmental chatbots to find examples. Describe use cases and create short dialogue snippets based on your findings.
• Create prototypes for interfaces which follow guidelines and best practices for trustworthy chatbots in a governmental / public services context. Present these prototypes and expain your design choices.

Literature

• Own research
• J. R. Schoenherr, R. Abbas, K. Michael, P. Rivas and T. D. Anderson, “Designing AI Using a Human-Centered Approach: Explainability and Accuracy Toward Trustworthiness,” in IEEE Transactions on Technology and Society, vol. 4, no. 1, pp. 9-23, March 2023, https://doi.org/10.1109/TTS.2023.3257627.
• Z. Atf and P. R. Lewis, “Is Trust Correlated With Explainability in AI? A Meta-Analysis,” in IEEE Transactions on Technology and Society, vol. 7, no. 1, pp. 70-77, March 2026, https://doi.org/10.1109/TTS.2025.3558448.
• Aoki, N. (2020). An experimental study of public trust in AI chatbots in the public sector. Government information quarterly, 37(4), 101490, https://doi.org/10.1016/j.giq.2020.101490.
• Senadheera, S., Yigitcanlar, T., Desouza, K. C., Mossberger, K., Corchado, J., Mehmood, R., … Cheong, P. H. (2025). Understanding Chatbot Adoption in Local Governments: A Review and Framework. Journal of Urban Technology, 32(3), 35–69. https://doi.org/10.1080/10630732.2023.2297665
• Traubinger, V., Heil, S., Grigera, J., Garrido, A., Abhyankar, S., Gaedke, M. (2025). An Analysis of Federal and Municipal Chatbots in Germany. In: Følstad, A., et al. Chatbots and Human-Centered AI. CONVERSATIONS 2024. Lecture Notes in Computer Science, vol 15545. Springer, Cham. https://doi.org/10.1007/978-3-031-88045-2_13
• Haugeland, I. K. F., Følstad, A., Taylor, C., & Bjørkli, C. A. (2022). Understanding the user experience of customer service chatbots: An experimental study of chatbot interaction design. International Journal of Human-Computer Studies, 161, 102788, https://doi.org/10.1016/j.ijhcs.2022.102788.