Technical Evaluations in the Field of AI Music Composition

AI-based music composition has progressed from symbolic melody generation to systems that produce audio from natural-language prompts, melodic references, or combinations of both. However, the perceived usefulness of a generated track cannot be captured by audio realism alone. A system may sound convincing yet ignore the requested mood, lose musical structure over time, offer little editing control, or provide insufficient information about the provenance of its training data. This paper develops a practical, multidimensional framework for evaluating AI music composition systems. The framework organizes evaluation into five dimensions: audio and musical quality, prompt and structure alignment, controllability and editability, workflow usefulness, and responsible-use transparency. It proposes a staged evaluation protocol combining listening tests, task-based assessment, technical checks, and documentation review. The framework is a conceptual synthesis informed by published work on text-to-music generation, controllable music models, synthetic-music detection, and the supplied systematic review of AI music composition. No new experimental dataset or performance results are claimed. The paper's contribution is an evaluation blueprint that researchers, developers, educators, and music practitioners can adapt before conducting empirical validation