In today's knowledge-intensive world, learners face the challenge of navigating rigid learning systems. Standardized learning approaches ignore individual strengths, weaknesses and learning styles, which can lead to frustration and inefficient learning. AI-supported, personalized learning offers a solution: it adapts to the needs of learners, offers tailor-made content and thus promotes not only motivation but also long-term learning success. However, the people involved in the development of AI systems are often unable to correctly assess the impact of their own decisions on the subsequent reality of the users. It is therefore important to sensitize interested people in software development to the effects of their decisions and to show them possible decisions and their consequences. This not only improves the quality of the decisions made, but also ensures user satisfaction in the long term.

The aim of this use case is to develop offers for academic specialists from various disciplines in order to 1.) enable personalized learning and 2.) to raise awareness of AI in software development.
1. personalized learning
The use of an AI-supported learning kiosk enables the individual adaptation of complex learning materials to the needs of learners. Users can load PowerPoint slide sets into the learning kiosk and choose between different languages, simplified explanations, additional information or a read-aloud function. This makes learning more comprehensible and tailored to personal needs.
2. sensitization for AI systems
The use of human-centered AI software is taught in a practice-oriented way within an adaptive teaching-learning concept and differences to classic software projects are shown. The entire development process is mapped and a holistic view is taken instead of a pure focus on AI elements.

Based on a generative Large Language Model (LLM), the so-called learning kiosk is being developed, which enables linguistic, visual and auditory adaptations of learning materials. This will initially be used within various lectures at Karlsruhe University of Applied Sciences in order to make more adaptive use of the learning content provided and to facilitate learning. Use in companies for internal documents is also conceivable.
In addition, a teaching-learning concept is being developed that is characterized by an alternation between self-directed learning phases and face-to-face events. Topics such as standard procedure models, design patterns for the development of AI-based software, agile methods of product and software development and methods for developing innovative solutions are covered. Model airplane construction serves as a use case to illustrate the effects of different decisions during software development.

Thanks to intelligent learning support, learners receive customized content that corresponds to their level of knowledge and learning style. The learning process is therefore individualized. This increases motivation and learning success on the one hand and reduces excessive demands and frustration on the other. By automating the preparation of content or providing additional explanations, learners can concentrate on the learning content and teachers on providing support instead of preparing different materials with the same content. This leads to more efficient knowledge transfer, shorter learning times, greater skills development in the long term and more active and independent learning. Raising awareness of AI systems and training employees and interested parties will also increase the satisfaction of software users and constructively reduce fears of using an AI solution.