The “black-box” nature of the vocal organ renders the learning process highly dependent on the establishment of proprioception. Traditional vocal music teaching models often suffer from a lack of feedback during after-class practice, leading to inefficient repetition and cognitive discontinuity in skill acquisition. Focusing on the “Artificial Intelligence (AI) Recursive Learning Method” emerging against the backdrop of generative artificial intelligence technology, this paper systematically interprets its internal mechanism and application value from the perspective of learning science. Combining the theory of Productive Failure with the interactive, constructive, active, passive (ICAP) Framework for cognitive engagement, the study reveals that the AI Recursive Learning Method, through its cyclical mechanism of “task-driven practice, gap exposure, instant explanation, and revision and reconstruction”. It can transform learners’ failure experiences in individual practice into explicit cognitive resources, and promote the shift of learning engagement from shallow passive imitation to in-depth constructive reflection. The research demonstrates that as a dynamic “cognitive scaffold”, this method effectively bridges classroom teaching and individual practice, alleviating the structural contradiction between “teaching” and “practice” in vocal music education. Finally, this paper clarifies the boundaries of technological application, and proposes that a new human-machine collaborative ecosystem in vocal music education should be constructed on the premise of affirming the dominant role of teachers in aesthetic guidance.
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Li, K., Zhu, Z., Ji, W. (2025) A Pedagogical Interpretation of the AI Recursive Learning Method and Its Application in Vocal Music Learning. Global Education Bulletin, 2(6), 1-10. https://doi.org/10.71052/grb2025/OIOK7223