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11.11.2021 10:42

AI skills for students of economics - extensive new joint project at the University of Bayreuth

Jennifer Opel Pressestelle
Universität Bayreuth

    Artificial intelligence (AI) as a central key technology in almost all areas of life is to be taught in greater depth to students of economics in future. The University of Bayreuth, together with its partners and with funding from the federal and state governments, will develop a cross-university, modular programme to boost AI skills in economists. The project funding amounts to almost € 4.24 million, and will run for four years.

    "ABBA: AI for Business | Business for AI" is the name of this innovative project. "Artificial intelligence is penetrating economic sectors more than ever, and is opening up promising economic potential. Economists in particular, as bridge builders, are increasingly becoming designers and critical success factors in AI innovation," explains Prof. Dr. Torsten Eymann, Vice President for Digitalisation & Innovation at the University of Bayreuth. Yet the use of artificial intelligence in business requires specific skills. In addition to technical expertise, business particularly needs knowledge of how to evaluate technical systems, embed them in operational processes, work environments, products and services, and control them on a permanent basis.

    Consequently, the target group of the joint project includes business administration and related business courses such as business informatics and industrial engineering, etc., which together make up about 22 % of students at German universities. "Our aspiration of providing competency-oriented, student-centred, and didactically high-quality teaching is of central importance in the context of this funding project. Students will be enabled to deal with AI in a knowledge-based and responsible manner through a focus on learning in authentic and challenging situations," says Eymann.

    Specifically, they are now working on the development and provision of a teaching module kit for artificial intelligence, which teaches business students interdisciplinary AI skills in a scientifically sound and practical manner. It comprises three pillars: AI-related teaching content that is oriented towards the students' professional background, skills and interests, as well as career-relevant requirements. New didactically high-quality content is being created, and established Open Education Resources (OER) are also being used. In addition, an AI learning factory is to be created, where AI content will be developed "hands-on" together with students. Finally, an organisational and technical exchange platform, on which universities, corporate partners, and students can network, will exploit synergies and build up competences in an efficient and targeted manner.

    The professorships of the University of Bayreuth will be involved in all sub-areas. On the one hand, high-quality AI courses will be designed, especially on the management of AI, and take ethical, legal, and social issues into account. In the conception of the AI learning factory, AI competencies are to be taught to students in an application-oriented and "hands-on" manner. Open hardware, demonstrators, and the provision of data sets are planned for this purpose. In setting up networking components, various formats such as cross-university hackathons, summer schools, and other events with close links to practice are to take place.

    The network will be funded to the tune of € 4.24 million, of which € 1.25 million will be allocated to the University of Bayreuth. The funding will be provided by the federal government and the state in which the respective university is located on a 9:1 basis.


    Wissenschaftliche Ansprechpartner:

    Prof. Dr. Torsten Eymann
    Vice-President for Digitalisation & Innovation of the University of Bayreuth
    Chair of Information Systems Management
    Fraunhofer FIT project group Business & Information Systems Engineering
    University of Bayreuth
    Phone: +49 (0) 921 55-7660
    E-mail: torsten.eymann@uni-bayreuth.de


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