Building a "Generative AI + Piano Education" Scenario: Teaching Applications from Score Generation to Improvisation

Abstract

With the deepening of aesthetic education and advancements in digital technologies, piano education faces practical challenges such as inadequate resource allocation, a disconnect between skill development and creativity cultivation, and a monolithic evaluation system. Generative AI, characterized by its generative capabilities, interactivity, and personalization, offers technological solutions for transforming piano education practices. This paper examines the entire teaching process—including score generation, skill training, improvisation, and evaluation feedback—and first identifies three core issues in current piano education: resource scarcity, rigid teaching methodologies, and ineffective assessment mechanisms. It then proposes four strategies: using customized score generation to address resource imbalances; employing intelligent practice assistance to break training rigidity; implementing tiered support systems to lower improvisation barriers; and adopting multidimensional dynamic evaluation for precise diagnostic feedback. The study aims to provide a theoretical framework and practical guidance for the digital transformation of piano education, fostering a new educational paradigm that integrates technological empowerment with enhanced aesthetic literacy.