300407 UE AI and Interdisciplinary Research (2025S)

Basics, Potential and Limitations

Continuous assessment of course work

Moodle

Fr 27.06. 16:45-18:15 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).

Registration is open from Th 06.02.2025 14:00 to Th 20.02.2025 18:00
Deregistration possible until Sa 15.03.2025 18:00

Details

max. 20 participants

Language: German, English

Lecturers

Dominik Hagmann

Classes (iCal) - next class is marked with N

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

Friday 07.03. 16:45 - 18:15 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
Friday 21.03. 16:45 - 18:15 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
Friday 28.03. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
Friday 02.05. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
Friday 16.05. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
Friday 30.05. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
Friday 06.06. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
Friday 13.06. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
Friday 20.06. 16:45 - 19:00 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
N Friday 27.06. 16:45 - 18:15 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) in recent years has influenced virtually all scientific disciplines, while simultaneously opening up new interdisciplinary perspectives. Therefore, this interdisciplinary course focuses on the application of AI in interdisciplinary research. A key emphasis is placed on promoting Digital AI Literacy—understanding, critically reflecting on, and competently applying AI technologies in (interdisciplinary) scientific contexts. The course is particularly (but not exclusively) aimed at students from disciplines such as Archaeology, Evolutionary Anthropology, Botany, Ecology, Evolutionary Genomics and Systems Biology, Conservation and Biodiversity Management, Zoology, as well as Cognitive, Behavioral, and Neurosciences.

Aims and Content

In-depth knowledge of AI-related concepts, methods, approaches, and applications such as eXplainable AI (XAI), Human-In-The-Loop (HITL), Machine Learning (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

Below you will find initial literature references for the course:

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

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

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

Jiang, Y., Li, X., Luo, H., Yin, S., & Kaynak, O. (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: Fr 11.04.2025 09:48