Mathematical modeling is one of the most useful tasks of applied mathematics in science and engineering. A good mathematical model can represent the main features of a phenomenon or an artificial process. Actually, dynamical study and simulations of the model could predict the evolution of such phenomena or process modeled, without the (high) costs of reproducing experiments in a control population group or in a laboratory with specific instruments produced ad hoc. With this in mind, the main purpose of this work is to analyze the utility of AI tools in order to set out mathematical models and study them. More specifically, we show how Copilot is able to generate different epidemiological models, which present deficiencies in its formulation, regarding both the variables considered and the equations governing the evolution of the models. Although, it could make us think that AI apps are not useful, this prove that they are helpful, mainly for young researchers, but only as researching support assistants. Actually, such AI apps suggest interesting issues to take into account when modeling. As main implications, this work reveals that they facilitate the autonomous learning and encourage the critical analysis, which will be fundamental for future researchers in its long-life learning, research and innovation challenges.

Keywords: AI assistance, technological innovations, autonomous learning, critical analysis, long-life learning and research.