Explainability, Fairness and Acceptability of Intelligent Systems (EFA)

ED3 - Enabling data-driven decisions

Despite the existence of knowledge gained through business analytics, decisions are made on the basis of subjective criteria in almost half of all cases. Reasons are behavior due to a lack of acceptance and unconscious prejudices and distortions (biases). In the research project, implications for increasing acceptance and reducing biases were derived and prepared for SMEs using IT demonstrators and training measures.

https://ipri-institute.com/forschungsprojekte/ed%C2%B3/

Use of language assistance systems in the value creation of small and medium-sized enterprises (SMEs) in mechanical and plant engineering

The overarching goal of the research project is to contribute to better exploiting the possibilities of digital transformation. The research goal is the identification and evaluation of useful application scenarios for language assistance systems. In somme cases it is analyzed which acceptance hurdles are associated with the use of language assistance systems.

https://ipri-institute.com/forschungsprojekte/smart-speaker/

Artificial Intelligence for Individualised Learning Path and Processes

The basic project idea is based on the experience, not least in the context of the Corona crisis, that the heterogeneity of the students require a much more individualized provision of learning content or learning support than is possible in the context of traditional teaching formats. In particular, students starting their master’s degree with a change of subject or university need individualised offers in order to compensate for the heterogeneous previous knowledge and experience. This applies even more to courses for further education or international degree programmes.

In 2LIKE,we plan to develop individualised digital learning contents on two different levels: On the macro level, individualised learning paths offer the opportunity to compensate for deficits or to deepen learning content based on competence, and on the micro level, personalised feedback supports individual learning processes individually. Both adaptivity approaches use AI-supported methods and best practices of automated teaching and learning systems. Concrete individualised digital learning offers are initially implemented for an introductory course in the MSc Cognitive Systems and the new MSc Artificial Intelligence, which specifically addresses the heterogeneity of the initial qualification. We plan to offer the developed AI learning content and the acquired knowledge to other actors in university teaching, e.g., in the form of certificate courses (from compact nuggets in the sense of micro-learning to Diplomas of Advanced Studies). This can also help to a sustainable financing of the developed methods.

Expertise from the fields of computer science, teaching and learning research, the School of Advanced Professional Studies (SAPS, part-time master’s degree programs) and the productive operation of teaching / learning infrastructure are available to implement the project.

The project is managed by Professor Dr. Birte Glimm, Institute of Artificial Intelligence, and Prof. Dr. Stefan Wesner, Communication and Information Center / Institute of Informstion Resource Management.

https://www.uni-ulm.de/en/in/ki/research/projects/2like-artificial-intelligence-for-individualised-learning-path-and-processes/