Motivating students to actively engage in their courses poses a common challenge for educators. Technical Mechanics, a foundational subject in engineering education, is often perceived as challenging by students due to its requirement for a solid grasp of mathematics, which not all students possess, and because the discussed models can appear abstract and disconnected from real-world applications. While solutions in mechanics seem logical during lectures, true comprehension is only tested when students independently work through problems. Therefore, it is essential to consistently encourage students to complete tasks throughout the semester to foster routine and confidence. However, the typically high enrollment numbers in these courses present difficulties for direct interaction and motivation.

This semester, a new approach was implemented using STACK (System for Teaching and Assessment using a Computer algebra Kernel) within the Moodle environment to create individualized problem sets that are automatically graded. At our institution, STACK assignments have been successfully used in mathematics for some time. However, in mechanics, it is not sufficient to simply vary numerical values or equation terms; the underlying model of a problem must be varied to create an interesting and hard-to-copy challenge for students. While it is fundamentally possible to generate parameterized graphics in STACK using JSXGraph, it is only with the Meclib library that this can be achieved with reasonable effort.

This contribution demonstrates how individual, parameterized problem sets were generated and utilized in a course on Statics. It discusses the methodology employed and shares insights gained from the initial implementation phase, including feedback from students and opportunities for enhancement. Preliminary results indicate improved student engagement and performance, supported by increased participation rates and enhanced understanding of key concepts.

However, the experiences collected were not universally positive. The query of numerical results proved to be problematic, highlighting the need for further refinement. It is necessary to consider how AI can be leveraged as a more effective tool in future implementations. Additionally, the automated grading provides instructors with insights into students' learning progress, while meaningful student interactions regarding these assignments further enrich the understanding of their learning journeys.

Keywords: Student engagement, automated grading, individualized problem sets.