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Artificial Intelligence and Sustainability

Module name (EN):
Name of module in study programme. It should be precise and clear.
Artificial Intelligence and Sustainability
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Applied Informatics, Bachelor, SO 01.10.2026
Module code: PIB-KIN
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P221-0214
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
2V+2PA (4 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
5
Semester: 5
Mandatory course: no
Language of instruction:
German
Assessment:
Project work

[updated 05.11.2025]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

KIB-KIN Computer Science and Communication Systems, Bachelor, ASPO 01.10.2021 , semester 5, optional course
PIB-KIN (P221-0214) Applied Informatics, Bachelor, ASPO 01.10.2022 , semester 5, optional course
PIB-KIN (P221-0214) Applied Informatics, Bachelor, SO 01.10.2026 , semester 5, optional course
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 150 hours (equivalent to 5 ECTS credits).
There are therefore 105 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Christoph Tholen
Lecturer: Prof. Dr. Christoph Tholen

[updated 25.09.2025]
Learning outcomes:
After successfully completing this module, students will understand the concept of sustainable development (its environmental, social, and economic dimensions) and its relevance to the field of artificial intelligence (AI). They will be familiar with the most common approaches to assessing the sustainability of products and services (e.g., life cycle assessment, LCA) and modeling interactions between different sustainability sectors (e.g., water-energy-food-ecosystem nexus).
Students will have acquired an overview of current fields of application for AI in support of sustainable development and will have gained an in-depth understanding of at least one selected topic, which they will have analyzed in detail as part of a group project. They will understand the role AI can play in achieving the Sustainable Development Goals (SDGs) and the challenges associated with this.
Students will understand the different sustainability dimensions of the development, implementation, and use of AI itself. They will be familiar with current challenges and approaches to measuring the sustainability of different types of AI systems (e.g., their carbon footprint). They will also be familiar with current methods and strategies for managing the environmental impact of AI development, implementation, and use.


[updated 05.11.2025]
Module content:
This module approaches the topic of AI and sustainability from two different but interrelated perspectives:
1.        AI for sustainability and
2.        the sustainability of AI.
AI for sustainability deals with selected approaches and applications of AI to support sustainable development (e.g., biodiversity monitoring, sustainable management of natural resources, education for sustainability).
The sustainability of AI highlights the environmental and social impacts of the development, implementation, and use of AI.
This module covers the following topics:
Introduction to sustainability
•        Sustainability definitions & challenges
•        Sustainable development goals
•        Participatory approaches to sustainability (e.g., involvement of stakeholders and consumers)
AI applications for sustainability
•        AI for climate (for example, energy efficiency)
•        AI for biodiversity
•        AI in remote sensing for environmental monitoring
•        AI for education in the field of sustainable development
•        Specific focus topics are selected with the students according to their interests.
Sustainabile AI
•        CO₂-The footprint of AI
•        Energy and water consumption through AI
•        Social risks of AI
•        Sustainable design and the sustainable use of AI: Challenges and approaches
•        Specific focus topics are selected with the students according to their interests.


[updated 05.11.2025]
Teaching methods/Media:
Slides, group work on selected topics, working with scientific literature

[updated 05.11.2025]
Recommended or required reading:
Ertel, W.: Grundkurs Künstliche Intelligenz: Eine praxisorientierte Einführung. Springer Fachmedien, Wiesbaden (2021). https://doi.org/10.1007/978-3-658-32075-1
Mensah, J.: Sustainable development: meaning, history, principles, pillars, and implications for human action: literature review. Cogent. Soc. Sci. 5(1), 1653531 (2019). https://doi.org/10.1080/23311886.2019.1653531
van Wynsberghe, A. Sustainable AI: AI for sustainability and the sustainability of AI. AI Ethics (2021). https://doi.org/10.1007/s43681-021-00043-6
Vinuesa, R., Azizpour, H., Leite, I. et al. The role of artificial intelligence in achieving the Sustainable Development Goals. Nat Commun 11, 233 (2020). https://doi.org/10.1038/s41467-019-14108-y
Rohde, F., Wagner, J., Meyer, A., Reinhard, P., Voss, M., Petschow, U., Mollen, A.: Broadening the perspective for sustainable artificial intelligence: sustainability criteria and indicators for Artificial Intelligence systems. Current Opinion in Environmental Sustainability. 66, 101411 (2024). https://doi.org/10.1016/j.cosust.2023.101411.
Anthony LFW, Kanding B, Selvan R.: Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models. ArXiv:2007.03051 (2020).
Henderson P., Hu, J., Romoff J., Brunskill, E., Jurafsky, D., Pineau, J.: Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning. ArXiv:2002.05651 (2020).
Brum, D., Teylo, L., Silva, F.P. da, Vasconcellos, F.C., Breder, G.B., Azevedo, L. de, Bezerra, E., Porto, F., Ferro, M.: Towards Sustainable Nowcasting: Assessing the Environmental Costs of AI-Driven Extreme Rainfall Prediction, https://www.researchsquare.com/article/rs-6751218/v1, (2025). https://doi.org/10.21203/rs.3.rs-6751218/v1.
Tholen, C., Leluschko, C., Nolle, L., Stahl, F.: Measuring and Comparison of the Energy Consumption of different Machine Learning Methods, (2025).


[updated 05.11.2025]
 
[Thu Nov 20 04:27:10 CET 2025, CKEY=pkiun, BKEY=pi3, CID=PIB-KIN, LANGUAGE=en, DATE=20.11.2025]