Neural networks are powerful machine learning models that could be applied to various problems, like classification or function approximation, in many different fields of science. However, education about neural networks is often either only aimed at students of the computer science field, or is kept very simple, to be applicable to a broader public. For many students, this makes learning about neural networks either a subchallenging or an overwhelming experience. With this thesis we intend to close this gap by introducing an educational application aimed at people who attend or have completed tertiary education in any field. Our application gives an in-depth but comprehensible view on the structure of neural networks, i.e. the different components and their relations that make up a neural network. One core feature of our application is the usage of gamification elements to keep the users motivated while simultaneously increasing the concepts’ comprehensiveness and convey a sense of accomplishment. We conducted an evaluative user research and found that the application moderately increases both the users’ motivation and their sense of accomplishment. However, we also found that the application does not increase the users’ perceived competence. We conclude that the implemented concepts, in particular the gamified learning environment, show beneficial effects on motivation of PhD students when learning about neural networks.