Abstract: Due to the influence of education in the development of society, several methods have been proposed to improve learning. In particular, digital Adaptive Learning Systems (ALSs) have been developed to tailor content to the students’ needs. In this work, we define Group-based Adaptation for Learning (GAL), a methodology which aims to automatically adjust game mechanics based on the characteristics of a group of students so that their collective ability is improved. A complete validation of our model would require its application to a suitable game and the execution of user studies. In this paper, we present several agent-based simulations that attempt to validate certain aspects of the model and inform us about additional refinements and improvements before exploring such route in the future. More specifically, we compared the impact of our group formation strategy as opposed to an optimal and a baseline random one. We checked that the average ability increases and engagement of the students rapidly converged to high, near optimal values when using GAL, as opposed to the random strategy which maintained low values. Moreover, unlike the random strategy, GAL managed to considerably approximate the promoted learning profiles to the preferences of the students. Finally, we describe how our method could function in a specific domain by defining a music serious game which allows students to learn to play instruments.