Auto-generative learning objects (AGLOs) are learning objects designed as generative learning templates. The process of content generation is not always straightforward. The generative models are usually complex and their design is difficult. In order to simplify this creation process, we intend to organize the steps of auto-generative learning objects design. Thus, we identified each creation step from the concrete problem model to the generative problem model. We made a description of the eleven proposed steps. We started with the choice of the learning concept, then with the realization of some concrete exercises that would lead us to the realization of a template. The determination of the necessary variables, the relations between them, as well as the identification of the specific necessary functions have also an important role in the abstraction process. We consider these steps as a guide for auto-generative content designers. We exemplify our creation algorithm with a use case from the Data Structures discipline, namely the steps of the selection-sorting algorithm. We performed a study on seven tutors to find out their availability to create and use AGLOs in the instructional-educational process. The conclusion was that they would need such objects in the classroom with the students but they also require programming basic skills.

auto-generative learning objects, the abstraction process, e-learning