The Fellowship Programme of the AI Campus

The AI Campus Fellowship Programme is aimed at university teachers from all departments who want to bring the future topic of AI into their teaching and are testing the integration of AI Campus learning opportunities for this purpose.  

The fellowship programme provides didactic, professional and financial support as well as interconnecting and access to networks,  
... to achieve effective integration of learning opportunities of the AI Campus into university teaching
... evaluate the impact of using open educational resources for teaching AI competencies, and
... ensure a connectable knowledge transfer towards these objectives.   

The fellowship programme incentivises the further development of university teaching to include digital learning opportunities provided by the AI Campus. 

 

 

The Current Teaching Fellows and Their Projects

A jury selected 10 projects from 18 fellows out of 41 applicants. The second fellowship programme runs from October 2021 until the end of the summer semester 2022.   

AI in Public Administration

In public administration, the level of digitisation is still comparatively low, but in some cases, there are very large databases, the use of which promises particular potential through artificial intelligence methods. In the Public Management bachelor's programme at HAW Hamburg, the necessary application-related knowledge and skills of graduates are sustainably improved through new course formats. The strong interdisciplinarity of the applications and the students' subsequent professional field requires that the teaching is also interdisciplinary. For this reason, the programme involves expert faculty members from computer science, psychology, law, economics and ethics.
 

Fellow Björn Gehlsen
Prof. Dr. Björn Gehlsen
HAW Hamburg
Fellow Christian Warneke
Prof. Dr. phil. Christian Warneke
HAW Hamburg

Artificial Intelligence and Neuroscience

This fellowship project aims to strengthen students' AI competences in the interdisciplinary field between artificial intelligence and neuroscience. The project seminar is primarily aimed at (master's) students of psychology, neuroscience and computer science, and aims to equip participants with the necessary knowledge and tools to solve their research problems using neurophysiological data, such as electroencephalography (EEG). Open Education Resources of the AI Campus are used for the preparation and during the project work. In addition to actively implementing what they have learned in research-related projects, this should also reflect on the use of AI in medical and neuropsychological research. 

Fellow Sebastian Stober
Prof. Dr.-Ing. Sebastian Stober
Otto-von-Guericke-Universität Magdeburg
Fellow Johannes Schleiß
Johannes Schleiß
Otto-von-Guericke-Universität Magdeburg

Deep Learning for Audio Event Detection

The lecture provides practical knowledge about machine and deep learning methods for audio data. The course content is taught through live coding sessions in Python with Jupyter Notebook, using widely available ML libraries such as Scikit-Learn and TensorFlow. Students work in groups on a thematically relevant project, which results in a code repository, project report in the form of a paper and short presentation. The blended learning format of the course allows new content to be flexibly integrated into the curriculum in order to do justice to the rapidly changing field of research.

Fellow Corvin Jaedicke
Corvin Jaedicke
TU Berlin
Fellow Fabian Seipel
Fabian Seipel
TU Berlin

Research Learning - Applied AI in the Field of Data Science

The project combines the basics of data science in a lecture with a practical seminar, the so-called "Tracking Olympiad". In the basic lecture, practical examples are used to show which tools belong in the toolbox of AI specialists. In the subsequent Tracking Olympiad, established and modern AI tools are selected and compiled by the students themselves, so that each student develops their own algorithm to track a randomly moving object. A Hexbug – a small, beetle-like robot – is used for this purpose. The aim of the seminar is for the students to work out an ensemble solution in teams that is competitive with the other solutions. 

Fellow Andreas Kist
Prof. Dr. Andreas Kist
Friedrich-Alexander-Universität Erlangen-Nürnberg

Application Competence in Machine Learning and Artificial Intelligence for Engineers

The qualification profile of mechanical engineers is developing in line with technical progress. Already today, software to support processes in the product lifecycle and the product itself accounts for the lion's share of value creation. The application possibilities of machine learning methods are likely to increase this importance in all phases of the lifecycle. Analogous to the education in engineering informatics, which does not aim to train software developers but enable students to work together with the corresponding subject domains on an equal footing, the fellowship project strives to establish a corresponding practice-oriented introduction in the compulsory area of undergraduate mechanical engineering courses. The courses in engineering informatics are already organised according to the principle of the inverted classroom model, so that the courses of the AI Campus can be well integrated here. 

Fellow Jörn Schlingensiepen
Prof. Dr.-Ing. Jörn Schlingensiepen
Technische Hochschule Ingolstadt

Artificial Intelligence Meets Human Resource Management

This project aims to introduce students to the integration possibilities of artificial intelligence in the human resource management of companies in an application and practice-oriented manner. The use case consists of the prototypical and project-oriented conceptual design and implementation of an AI-based service for the assessment of job references. The AI service to be implemented identifies relevant formulations (e.g., "to the fullest satisfaction"; "separation by mutual agreement"; "sociable employee", "worked correctly and punctually according to instructions") in job references by means of suitable procedures, such as text mining, and classifies them. The AI successively learns the quality and the assessment statements of job references and translates them into a school grade.

Fellow Maximilian Wolf
Prof. Dr. Maximilian Wolf
Hochschule Albstadt-Sigmaringen
Fellow Stefan Ruf
Prof. Dr. Stefan Ruf
Hochschule Albstadt-Sigmaringen

Design Meets AI - Cooperation Project Between Students of Visual Communication and Computer Scientists

Technology meets design – a connection that plays a central role in the communication of digital content and in the contact between users and digital applications: Technology creates new possibilities and spaces for action, design creates the interface to people, conveys content and interaction. In this joint project of Heilbronn and Pforzheim universities, students of computer science ("Software Engineering" and "Applied Computer Science") at Heilbronn University and students of "Visual Communication" at Pforzheim University will deal with AI technology, its applications, possibilities and future potential in a project. They will work together on the basics and in the end each interdisciplinary group will present an aspect of AI in a digital communication medium/exhibition. The choice of the AI topic, the concrete application and the implementation in a concrete art object is left to the students. The computer scientists provide the technological core –the programming with a chosen framework for a concrete use case – while the "Visual Communication" students contribute the design of the exhibit.

Fellow Nicole Ondrusch
Prof. Dr. Nicole Ondrusch
Hochschule Heilbronn
Fellow Dagmar Korintenberg
Dagmar Korintenberg
Hochschule Pforzheim

How Computers Learn to Talk: Practical Introduction to Artificial Intelligence Using Your Own Chatbot

Project includes the design of a one-day teaching-learning lab in which participants are introduced to the basics of artificial intelligence using a chatbot. The target groups are groups of school children, executives, students from non-informatics courses with little or no knowledge of computer science. The teaching-learning lab is intended to present essential concepts of artificial intelligence and speech recognition and to offer participants the opportunity to experience them themselves "playfully" by expanding and adjusting parameters of the chatbot.

Fellow Anke Hutzschenreuter
Prof. Dr. Anke Hutzschenreuter
DHBW Heilbronn
Fellow Torsten Harms
Prof. Dr. Torsten Harms
DHBW Karlsruhe

User-Centredness and Responsibility in the Software Development Process

The project takes place within the framework of the "Laboratory for Software Projects and Project Skills", the learning stage for professional software development in the "Software Engineering” bachelor's degree programme. Using innovative learning concepts, students here can develop a deeper understanding of the meaning, functions and implications of human-machine interaction in the context of software product development. The goal is to sensitise and enable students to design high-quality digital products with a focus on the interfaces between users and technical artefacts. This should both increase the quality of the products and ensure an improved experience of the users in their diversity and their needs. At the same time, ethical aspects in the handling of data and in the development of algorithmic systems should be taken into account.

Fellow Nicola Marsden
Prof. Dr. Nicola Marsden
Hochschule Heilbronn
Fellow Kerstin Raudonat
Dr. Kerstin Raudonat
Hochschule Heilbronn

AI in the Perspective of Digital Humanities

In an interdisciplinary approach, the courses and materials of the AI Campus are to be used to provide a comprehensive perspective on the topic of artificial intelligence in courses of the Digital Humanities department. The seminar primarily conveys theoretical content, while the exercise complements practical programming exercises on the topic of Deep Learning. In addition to the conceptual and technical basics, the critical reflection of the technology through methods of interpretability will also be discussed. The heterogeneous group of participants in the courses promises a lively discussion involving different perspectives.

Fellow Claes Neuefeind
Dr. Claes Neuefeind
Universität Köln
Fellow Timothée Schmude
Timothée Schmude
Universität Köln

Contact

Cordula Torner
Cordula Torner
Community Manager
Stifterverband
Stefan Goellner
Stefan Göllner
Innovation Manager
Stifterverband