Long title: AI in Science & Engineering

Duration: 10.2025 - 08.2028

Abstract: 

AI4All is a project funded by TU Wien aimed at introducing and integrating Artificial Intelligence (AI) as a key enabling technology into the teaching portfolio of multiple faculties. The objective of the project is to equip students from different study programs with fundamental competencies in AI and to promote the application of these methods in various disciplinary contexts.

As pilot faculties, the Faculty of Technical Chemistry, the Faculty of Civil and Environmental Engineering and the Faculty of Mechanical and Industrial Engineering are directly involved in the development and implementation of the project. The project is developed under the coordination of the Faculty of Informatics.

Within the framework of the project, a 12-ECTS blended-learning module “AI4All” is being developed and made available to students of all Bachelor’s and Master’s programs. The module enables students to acquire fundamental knowledge of AI in a practical and application-oriented manner and to apply these concepts directly within their respective disciplines. As part of elective courses and transferable skills, students can earn an additional qualification in AI, which is highly relevant both for academic specialization and for future professional practice.

The courses are developed in close collaboration between the participating faculties to ensure interdisciplinary relevance as well as scalability for large student cohorts. At the Faculty of Mechanical and Industrial Engineering, the project is supported by the Institute of Management Science, Research Unit of Production and Maintenance Management (PIM).

Practical exercises, case studies and project-based assignments are aligned with real industrial challenges and enable students to apply AI, machine learning and data-driven analytical methods in complex application scenarios. By combining theoretical knowledge with practical exercises, AI4All fosters a deeper understanding of the functioning and potential applications of AI systems across different disciplines.

Results:

  • Institutional integration of a cross-faculty 12-ECTS AI module for all non-computer-science study programs at TU Wien.

  • A didactically validated blended-learning concept with proven scalability and a quality-assured exercise structure for large student cohorts.

  • Interdisciplinary curriculum integration through discipline-specific adaptation of AI methods in engineering and natural sciences.

  • Measurable competence development in machine learning, data-driven analysis and AI applications in both scientific and industrial contexts.