The inherently subjective notion of quality in creative artefacts gives rise to both technical and philosophical questions regarding AI-generated art. In this thesis, the development and evaluation of AI-generated dance forms the context within which these questions are explored. Dance is a multifaceted art form. In rituals and celebrations, improvised or choreographed, a social glue and personal expression, dance plays a role in all human cultures and many aspects of life. Dance movement is also a rich, complex data source. The main objectives of this thesis are to understand more about how deep learning can be used to capture salient features of movement, and especially dance movement, using full-body motion capture data. We further explore how such technology might be aimed to benefit the creative practice of dancers.Our work includes the collection of a data set of improvisation performed by individual dancers and the implementation of generative models trained on this data. We evaluate the model's ability to learn the intricate relationship between music and movement and investigate the trained model's generative versatility. We also examine the generated dance through subjective assessment in a survey, and through dialogue and embodiment exercises with experienced dancers and choreographers. This work broadens our understanding of how leveraging the ability of AI to go beyond a mere replica of human movement can itself lead to engagement and inspiration. By engaging in research with dancers, we provide a stepping stone for building a thorough understanding of the impact of AI-generated art on a creative field.This thesis consists of six publications and contributes an open-access motion capture data set of improvised dance as a resource to be used in future research and artistic practice.