IKILeUS

Integrierte KI in der Lehre der Universität Stuttgart

With over 23,000 students in 73 bachelor's and 95 master's degree programs, the field of artificial intelligence (AI) is already one of the central building blocks of the University of Stuttgart. This ranges from intelligent assistants in smartphones to adaptive planning of logistics flows with real-time data to research on molecular compounds in the laboratory. Nevertheless, other areas and user groups have been identified that can benefit significantly from a stronger integration of AI topics and AI software solutions. At the same time, the University of Stuttgart, with its technical focus, offers outstanding scientific and teaching potential to both provide these solutions and implement an expansion and adoption of the new AI-based materials and concepts. The IKILeUS project is therefore designed to combine the existing AI expertise of many collaborating departments in order to both communicate AI to the breadth of the student body in an interdisciplinary view and to use AI-based technologies in teaching to relieve the burden on teachers and to improve teaching. Topics will be addressed with a focus on specific courses and software solutions, while simultaneously looking at roll-out potential to additional user groups and areas.

We strengthen both sides (teachers and students) of the everyday study experience, with a special focus on students. The project "Integrated AI in Teaching at the University of Stuttgart" (IKILeUS) combines diverse uses of AI in a sustainable way in teaching, in order to strengthen the University of Stuttgart in its pursuit of a holistic teaching of AI. The use of AI in and for teaching takes place over the entire period of a degree program, from the first to the last semester, and even goes beyond that in the form of continuing education offerings.

Website: IKIeUS.de

Operating Time: 12/2021 - 11/2024

Source of Funding: BMBF

Team:
 
Partner Institutes at the University of Stuttgart:

 

Publications

  1. 2024

    1. Das, A., Fathallah, N., & Obretincheva, N. (2024). Navigating Nulls, Numbers and Numerous Entities: Robust Knowledge Base Construction from Large Language Models. In Knowledge Base Construction from Pre-trained Language Models Challenge Workshop, ISWC’24.
    2. Fathallah, N., Bhole, M., & Staab, S. (2024). Empowering the Deaf and Hard of Hearing Community: Improving Video Captions with Large Language Models. In In Proceedings of the 11th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion.
  2. 2022

    1. Hedeshy, R., Kumar, C., Lauer, M., & Steffen, Staab. (2022). All Birds Must Fly: The Experience of Multimodal Hands-free Gaming with Gaze and Nonverbal Voice Synchronization. INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (ICMI ’22), November 7--11, 2022, Bengaluru, India. https://doi.org/10.1145/3536221.3556593
    2. Sengupta, K., N., F., & Staab, S. (2022). Accessibility of Online Educational Platforms. MPDAS Workshop (Multidisciplinary Perspectives on Designing Accessible Systems for Users with Multiple Impairments: Grand Challenges and Opportunities for Future Research Workshop, The 24th International ACM SIGACCESS Conference on Computers and Accessibility), 23. October 2022.

Project Members

Former researchers

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