In order to promote the AI competencies of students from all three Hohenheim faculties - Agricultural Sciences, Natural Sciences, and Economics and Social Sciences - an interdisciplinary, study-guiding qualification program is being established that is aimed at advanced Bachelor's and Master's students. After successful participation in the qualification program, students will be awarded the "AI & Data Science Certificate Hohenheim", certifying that they have acquired basic as well as subject-specific knowledge in the field of AI, in particular machine learning, data science and scientific computing.
It is planned to offer the courses online, but also in the form of blended learning formats, in which, for example, face-to-face events are supplemented by online self-learning programs: This allows students to determine their own learning pace and adapt it to their life circumstances. Tests and quizzes provide students with feedback on their current level of knowledge and instructors with feedback on the effectiveness of their teaching. In addition, special emphasis is placed on offerings that facilitate social exchange among students.
The teaching program of the AIDAHO certificate is to be regularly discussed and reviewed with personnel managers of the regional economy in order to be able to meet the needs of the companies in a custom-fit manner. Further cooperations with the regional economy promote the contact of students to the companies and final theses with practice-relevant AI and Data Science topics. Beyond the duration of this initiating funding project, the goal is to develop the AIDAHO teaching program into an independent degree program with an AI and Data Science focus.
For more information, please visit the AIDAHO project website.
Participation of the Department of Food Informatics
The main task is to design and initialize a learning platform for automated assessment of student programming submissions. This platform shall be based on code versioning systems like GitLab in integration with tools for Continuous Integration (CI) and shall allow the versioning of source code as well as the automated execution of defined tasks as soon as a new version of the source code has been uploaded. Part of the tasks is the configuration of these CI pipelines to automatically check the source code of the students.
Furthermore, a didactic concept should be created for the generation of individual learning plans for the students, which are adaptively created based on the students' submissions. The concept should support the flexibility to be able to map the specific teaching contents of the respective faculties.
