This work in progress proposes an automatic evaluation pipeline for dental surgery gestures based on teacher’s demonstrations and observation needs. This pipeline aims at supporting learning in preclinical situations for the first years of study in the dental school. It uses the Random Forest (RF) algorithm to train a model based on specific descriptors for each gesture component, that are designed to cover the evolution of the observation needs. The inputs are the captured motion parts whose labels are defined by the teachers with their own vocabulary, to represent expected or no-wanted geometrical or kinematic features. The overall evaluation (for example, weighted average of each component) and the component evaluation can be given to students to improve their postures and motor skills. A preliminary test correctly classifies a back correct posture and three main flaws (”Twisted Back and Bent Head”, ”Leaning Back”, ”Leaning Back and Bent Back”) by the RF model, for the posture component. This approach is designed for the adaptation to the expert’s evolving observation needs while minimizing the need for a heavy re-engineering process and enhancing the system acceptance