Universität Wien

300407 UE AI and Interdisciplinary Research (2026S)

Basics, Potential and Limitations

3.00 ECTS (2.00 SWS), SPL 30 - Biologie
Continuous assessment of course work
Moodle
Fr 20.03. 16:45-19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 20 participants
Language: German, English

Lecturers

Classes (iCal) - next class is marked with N

The mandatory preliminary meeting will take place during the first session!

  • Friday 06.03. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
  • Friday 13.03. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
  • Friday 27.03. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
  • Friday 17.04. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
  • Friday 24.04. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
  • Friday 08.05. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
  • Friday 15.05. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
  • Friday 22.05. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
  • Friday 29.05. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
  • Friday 05.06. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
  • Friday 12.06. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
  • Friday 19.06. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
  • Friday 26.06. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1

Information

Aims, contents and method of the course

The rapid development of Artificial Intelligence (AI) has, over the past years, fundamentally transformed virtually all fields of research while simultaneously opening new interdisciplinary perspectives. This interdisciplinary course therefore focuses on the application of AI in cross-disciplinary research contexts. Its central objective is to foster a robust Digital AI Literacy, understood as the reflective understanding, critical evaluation, and competent application of AI-based methods in interdisciplinary scientific practice.

The course is designed primarily – though not exclusively – for students in archaeology, evolutionary anthropology, botany, ecology, evolutionary genomics and systems biology, conservation and biodiversity management, zoology, as well as cognitive, behavioral, and neurobiology.

In addition to student-led contributions, this thematic focus is further developed through joint discussions, hands-on exploration of selected AI tools, and insights into the interdisciplinary AI project “LEGION – Machine LEarninG-enabled Identification of Archaeological Objects in the Middle Danube River Basin” (Heritage_2024-12_LEGION; funded under the Heritage Science Austria 2.0 program of the Austrian Academy of Sciences – ÖAW), which has been running since 1 February 2026.

Within this project, machine learning (ML) approaches are combined with eXplainable AI (XAI) and Human-in-the-Loop (HITL) methodologies in a collaborative framework between the Austrian Archaeological Institute (ÖAI) of the ÖAW and the Computer Vision Lab (CVL) at TU Wien. The project aims at the methodologically controlled analysis of tens of thousands of everyday ceramic finds from the ancient metropolis of Carnuntum (Lower Austria), part of the UNESCO World Heritage site “Danube Limes.”

Aims and Content

  • In-depth knowledge of AI-related concepts, methods, approaches, and applications such as Generative AI (GenAI), XAI, HITL, ML, Artificial Neural Networks (ANN), Deep Learning (DL), Neural Radiance Fields (NeRFs), Maximum Entropy (MaxEnt) Modeling, and Robotics. Students are also encouraged to contribute with their own specialized inputs on these topics.

  • Ability to conduct literature research based on a scientific question and present a specific topic.

  • Critical analysis and discussion of research papers on AI applications and their inter-, trans-, and multidisciplinary use in various scientific disciplines.

  • Promotion of interdisciplinary collaboration and development of scientific writing skills.


Methods

  • Lectures by course instructors introducing key concepts.

  • Guided instruction both in person and online via Moodle.

  • Independent literature research, analysis, and student presentations on specific topics based on concrete guidelines (PRISMA).

  • Group work on specific papers to deepen content understanding.

  • Peer feedback sessions to enhance scientific work.

Assessment and permitted materials

The following course requirements must be fulfilled:

  • Participation in the Journal Club

  • Presentation on a selected topic

  • Writing a manuscript on a selected topic

  • Active participation

Minimum requirements and assessment criteria

The assessment criteria and minimum requirements are as follows:

  • Participation in the Journal Club: 15%

  • Presentation on a selected topic: 15%

  • Writing a manuscript on a selected topic as the final assignment: 60%

  • Active participation through engagement in the course and active contribution to discussions: 10%


Students may miss up to three two-hour sessions. Absences must be reported to the course instructor in advance with a valid, verifiable reason (e.g., a doctor’s note).

Examination topics

The following course requirements must be fulfilled:

  • Participation in the Journal Club

  • Presentation on a selected topic

  • Writing a manuscript on a selected topic

  • Active participation

Reading list

• Bozkurt, A. (2024). Why Generative AI Literacy, Why Now and Why it Matters in the Educational Landscape? Kings, Queens and GenAI Dragons. Open Praxis, 16(3). https://doi.org/10.55982/openpraxis.16.3.739
• Caluori, L. (2024). Hey Alexa, Why Are You Called Intelligent? An Empirical Investigation on Definitions of AI. AI & SOCIETY, 39(4), 1905–1919. https://doi.org/10.1007/s00146-023-01643-y
• Dwivedi, Y. K., Hughes, L., Ismagilova, E., et al. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
• Editorial. (1987). AI & SOCIETY, 1(1), 3–4. https://doi.org/10.1007/BF01905884
• Gattiglia, G. (2025). Managing Artificial Intelligence in Archeology. An overview. Journal of Cultural Heritage, 71, 225–233. https://doi.org/10.1016/j.culher.2024.11.020
• Hagmann, D. (2025). Home is Where my Villa Is: A Machine Learning-based Predictive Suitability Map for Roman Features in Northern Noricum (ca. 50–500 CE/Lower Austria/AUT). Journal of Maps, 21(1). https://doi.org/10.1080/17445647.2025.2487444
• Henz, P. (2021). Ethical and legal responsibility for Artificial Intelligence. Discover Artificial Intelligence, 1(1), 2. https://doi.org/10.1007/s44163-021-00002-4
• Huerta, E. A., et al. (2023). FAIR for AI: An interdisciplinary and international community building perspective. Scientific Data, 10(1), 487. https://doi.org/10.1038/s41597-023-02298-6
• Hughes, L., Dwivedi, Y. K., Malik, T., et al. (2025). AI Agents and Agentic Systems: A Multi-Expert Analysis. Journal of Computer Information Systems, 65(4), 489–517. https://doi.org/10.1080/08874417.2025.2483832
• Jiang, Y., Li, X., Luo, H., et al. (2022). Quo vadis artificial intelligence? Discover Artificial Intelligence, 2(1), 4. https://doi.org/10.1007/s44163-022-00022-8
• Kaynak, O. (2021). The golden age of Artificial Intelligence. Discover Artificial Intelligence, 1(1), 1. https://doi.org/10.1007/s44163-021-00009-x
• Kusters, R., et al. (2020). Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities. Frontiers in Big Data, 3. https://doi.org/10.3389/fdata.2020.577974
• Lu, C. (2024). Rethinking artificial intelligence from the perspective of interdisciplinary knowledge production. AI & SOCIETY, 39(6), 3059–3060. https://doi.org/10.1007/s00146-023-01839-2
• Nims, R., & Butler, V. L. (2019). Increasing the Robustness of Meta-analysis Through Life History and Middle-Range Models: An Example from the Northeast Pacific. Journal of Archaeological Method and Theory, 26(2), 581–618. https://doi.org/10.1007/s10816-018-9383-1
• Pinski, M., & Benlian, A. (2024). AI literacy for users – A comprehensive review and future research directions of learning methods, components, and effects. Computers in Human Behavior: Artificial Humans, 2(1), 100062. https://doi.org/10.1016/j.chbah.2024.100062
• Schmallenbach, L., Bärnighausen, T. W., & Lerchenmueller, M. J. (2024). The global geography of artificial intelligence in life science research. Nature Communications, 15(1), 7527. https://doi.org/10.1038/s41467-024-51714-x
• Sheikh, H., Prins, C., & Schrijvers, E. (2023). Mission AI: The New System Technology. Springer International Publishing. https://doi.org/10.1007/978-3-031-21448-6
• Wang, P. (2019). On Defining Artificial Intelligence. Journal of Artificial General Intelligence, 10, 1–37. https://doi.org/10.2478/jagi-2019-0002
• Zheng, M., Andrade, C. H., & Bajorath, J. (2021). Introducing artificial intelligence in the life sciences. Artificial Intelligence in the Life Sciences, 1, 100001. https://doi.org/10.1016/j.ailsci.2021.100001

Additional literature will be made available on Moodle or developed collaboratively.

Association in the course directory

MAN 3, MAN W5, MEC-9, MBO 7, MNB6, MZO4, MES5, CoBeNe 4

Last modified: Th 05.03.2026 15:27