Intermedial Transformation: Generative Artificial Intelligence-Based Choreography Learning Strategies for Students at Parongpong 1 Public High School
DOI:
https://doi.org/10.24036/jsu.v14i4.55Keywords:
generative AI, dance education, intermedial transformation, text-to-movement, motion interpolation, collaborative learningAbstract
The dominance of visual culture among Generation Z students often traps the dance learning process in mimesis practices that hinder the emergence of originality in movement. This study applies an Intermedial Transformation strategy through a Text-to-Movement Translation approach based on Generative AI to respond to this creative stagnation. Using a mixed Classroom Action Research (CAR) design on 34 students at Parongpong 1 Public High School, the intervention was carried out in two cycles: the use of textual AI (ChatGPT) and visual-kinesthetic AI (Living Archive). The results showed a significant increase in composition creativity scores: 63.5 (Pre-Cycle), 71.4 (Cycle I), and 83.8 (Cycle II). The main finding of this study was a shift in the mode of creation from dynamic imitation to static reconstruction. Through the AI-facilitated motion interpolation process, students activated their kinesthetic intelligence to arrange digital poses into complete dance compositions. This proves that targeted AI integration can function as a collaborative partner (co-creator) that stimulates originality without reducing human agency in the creative process.
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