Tasks with high complexity levels, such as cooking, surgical suturing, and the operation of sophisticated machinery, typically entail various learning stages. These stages may include individual study, observation of task execution, and hands-on practice under expert supervision. However, this supervision demands the presence and commitment of an expert, which is both time-consuming and expensive. This paper presents an automated student supervisor system that addresses these challenges, making supervision more cost-effective and scalable. We formally specify the behaviour of the supervisor system as a transition system that models how the configuration of the system changes with each action performed by the learner according to a given pattern/model that synthesises how the task must be executed. In this way, our system can supervise new student’s executions by using conformance checking, thus comparing the current student action with the model. This allows the system to identify the correct and incorrect actions performed by the student during the execution of the task. We have implemented a prototype of the supervision system using the programming language Maude. Maude is a declarative formalism (based on rewriting logic) that has been shown to be suitable for specifying and reasoning about systems. The prototype was evaluated using a small dataset comprising samples of surgical suturing.