Open Access

This study explores the potential of generative AI, specifically Long Short-Term Memory (LSTM) networks, to advance collaborative choreographic composition within the framework of the Body Logic (BL) Method—a choreographic approach grounded in cognitive science designed to challenge inherited habits and practices in contemporary dance. Through five cognitive tasks that emphasize different movement types and their qualities, we investigate how LSTM networks recognize established movement patterns and innovate by combining them in novel ways, mirroring the processes of human creativity. Furthermore, we examine how LSTM-generated sequences, derived from learned data, convey expressive qualities through a variety of movements. The AI-generated movements closely follow the original movement trajectory but exhibit minor deviations attributable to the LSTM model’s inherent prediction uncertainty. These variations illustrate the model’s capability to introduce fresh elements while maintaining learned patterns, akin to human creativity. This research contributes novel perspectives on how technology can enrich artistic expression and challenge habitual decision-making in dance.