Applied AI in Logistics: A YOLOv8 Case Study

This paper presents an educational demonstrator that integrates AI-based image processing into the Fischertechnik Learning Factory 4.0 to enhance practical and interdisciplinary education in the context of Industry 4.0. Using YOLOv8 for real-time object detection in a simulated high-bay warehouse, the work was motivated to make abstract AI concepts tangible and offers hands-on experience in logistics processes. The hybrid approach of digital and haptic learning elements fosters technical understanding, student engagement, and knowledge transfer between academia and industry. The focus lies on the didactic value of this combination rather than on advancing image recognition algorithms. While no quantitative evaluation of detection performance or learning outcomes has been conducted yet, the demonstrator shows potential as a low-threshold and scalable platform suitable for diverse educational backgrounds. Future extensions, such as predictive maintenance, underline its role as a flexible environment for teaching, research, and innovation in smart manufacturing.