<![CDATA[This study examines how generative artificial intelligence participates in the creation and interpretation of musical meaning, using Suno AI’s text-to-music system as a focused case. The research explores how machine-generated sound can be understood hermeneutically, particularly how linguistic prompts, probabilistic modeling, and audio generation processes shape meaning, emotion, and musical intention. The study aims to determine the extent to which generative AI functions as an epistemic collaborator rather than a passive tool and how its outputs align with or diverge from human interpretive expectations. Using a digital epistemological hermeneutic framework operationalized through prompt-based observation, semantic interpretation, and comparative listening, the study conducted controlled experiments varying genre, instrument, mood, and tempo. Each output was evaluated in terms of expressive quality, emotional valence, stylistic coherence, and prompt response fidelity. The findings indicate that generative AI constructs musical meaning through representational inference, producing sonic forms that partially reflect the semantic cues embedded in linguistic prompts. Although the system does not exhibit human-like intentionality, its probabilistic structures generate patterns that resonate with human affective and interpretive frameworks, creating a co-creative space where human prompts and machine inference jointly shape musical expression. These insights demonstrate the usefulness of hermeneutics as a methodological lens for understanding AI-mediated creativity and highlight the importance of prompt design, model transparency, and human-machine interpretive dynamics in future computational musicology and creative AI research.]]>
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