Date of Award
Summer 2026
Document Type
Thesis
Degree Name
Master of Fine Arts (MFA)
First Advisor
Shaunna Vella
Abstract
This thesis document is an exploration of the interaction between live performance art and digital content creation such as Generative A.I. In this work, I draw from several courses I completed during my time in Saint Mary’s College of California MFA in Dance and Creative Practice program, including Dance and Social Justice, Phenomenology, Somatic Movement Seminar and Dance and Performance Studies. Each offers a throughline of my scholarly explorations and embodied research into the spheres of adapting and challenging how concert dance spaces respond to digital information and creation. I utilized live movement collaboration and distorted videography through Generative A.I. that cultivated into my live thesis performance Machine (Un/Re) Learning.
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Shadley, Jordan, "Machine (Un/Re) Learning" (2026). MFA in Dance Theses. 10.
https://digitalcommons.stmarys-ca.edu/mfa-dance/10
