AIM@VET-Inspired University Level Education Strategies for Teaching Comp-Uter Vision and Biometrics

Computer vision and biometrics are increasingly important in many AI-driven applications, yet teaching these fields poses challenges in balancing theory and hands-on practice. This paper presents a structured approach implemented for the technical skills course at the Faculty of Computer and Information Science, University of Ljubljana, designed for Computer Science students. The course integrates guided Jupyter Notebook exercises while allowing students to complete coding tasks while leaning on AI assistance. In-person presentations and discussions reinforce understanding by requiring students to explain their implementations and problem-solving strategies. The 15-week curriculum progresses from basic image processing to deep learning-based biometric recognition. Teaching materials are derived from the AIM@VET EU project, which focuses on adapting AI education to labor market needs, but adapted here for university students. We hope that AI-assisted, structured coding exercises combined with interactive discussions will enhance engagement and comprehension, better preparing students for a variety of applications in computer vision and biometrics.