Städte sehen sich durch den Klimawandel einer zunehmenden Komplexität und einer Vielzahl an Krisen gegenüber. Steigende Temperaturen und häufigere Extremwetterereignisse belasten die urbane Infrastruktur erheblich. Verstädterung und Bevölkerungswachstum verschärfen diese Herausforderungen, indem sie den Flächenbedarf erhöhen und die Ressourcennutzung intensivieren. Versiegelte Flächen verhindern die natürliche Wasserversickerung und verschärfen Hochwassergefahren. Zudem führen urbane Wärmeinseln zu erhöhtem Energieverbrauch und Gesundheitsrisiken.

During the corona pandemic, the role of science was seen as something very important in broad sections of society. Unfortunately, this appears to be less the case with regard to climate change. Increasingly, subjective and emotionally based statements are dominating the discussion, which must be viewed extremely critically in view of the complex interactions and extreme effects. On the other hand, great hope is being placed in science to master the complex challenges with the help of artificial intelligence (AI). With the public appearance of generative AI (GenAI) in the form of ChatGPT, a new, partly critical discussion is being held, although AI-based technologies have already made impressive progress as a result of scientific research for many years and numerous developments are already in real use. On the one hand, science is expected to provide insights and recommendations for the responsible use of AI. On the other hand, the use of AI in research work is also discussed critically because it is used, for example, in the application phase of research projects or to evaluate the data obtained and to write down and communicate the results. This gives rise to questions such as “How valid are scientific achievements that are produced with (the help of) AI?” Especially when using generative AI, there is a risk that the own creation process will degenerate due to uncontrolled use, excessive dependency and distortion of results [1], because activities that go beyond routine processes are conveniently left to generative AI, which can be a problem especially when processing complex tasks. An analysis of the authors of around 30 master’s theses from 2023 and 2024 in an international engineering master’s program showed that around 1/3 of the theses are strongly influenced by GenAI results, recognizable by the usual listing form, the text style and the lack of reference to the task. These papers were also among the 1/3 with the lowest grading. Without guidance on the effective use of GenAI, its use does not appear to lead to improved performance, but instead encourages the unthinking copying of text modules.

On the other hand, observations of the use of generative AI in everyday teaching and research show that GenAI can promote complexity competence in particular when used in a targeted manner on the basis of appropriate training. The authors use GenAI in various contexts of their research, increasingly to support the solution of complex problems and tasks in complex environments. The main area of application is water management and increasingly the analysis of urban areas to adapt to water-related challenges caused by climate change. The main focus here is on the massive impact of extreme weather events such as heavy rainfall and prolonged periods of drought on urban and regional infrastructure as well as forest and agricultural areas. These main areas of application are not only highly complex in thematic terms. Solutions must be implemented at urban and municipal level, and here too, the spatial, infrastructural and organizational environment is highly complex. It is against this background that the observations described in the use of generative AI were made, the theoretical framework described in this article was created and the hypothetical analysis was carried out, for which empirical evidence is still pending, but in preparation.

Um den Herausforderungen der immer komplexer werdenden globalen Umwelt- und Gesellschaftskrisen gerecht zu werden, steigt die Notwendigkeit, Nachhaltigkeit auch im Projektmanagement zu verankern. Die Methode OktoPus führt die Projektleitung mit Team strukturiert während des gesamten Projektes zur ganzheitlichen Nachhaltigkeitsintegration im Projektmanagement, gegliedert in die acht OktoPus-Cluster Nachhaltigkeitsverständnis, Mindset, Stakeholder-Integration und Kommunikation sowie Projektziele, -planung, -durchführung und -abschluss.

Die Anwendung von KI-Tools beschleunigt die Umsetzung von mehr Nachhaltigkeit sowohl durch mehr Effizienz als auch eine gesteigerte Ergebnisqualität. Je nach OktoPus-Cluster kommen verschiedene KI-Teilbereiche und Funktionen, wie Natural Language Processing, Research & Optimization, Data Analytics und Image Understanding zum Einsatz. Insbesondere Natural Language Processing dominiert in allen OktoPus-Clustern.