Digital and AI-supported music learning tools are increasingly adopted in higher education, yet their selection is often driven by informal preferences rather than transparent, evidence-based procedures. This study implements a multi-criteria decision-making framework to rank AI-enabled music-learning technology options using the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Six technology categories were evaluated across 25 sub-criteria grouped into five dimensions, based on judgments from a 20-member expert panel and questionnaire responses from 100 higher-education music learners. The weighting results indicate that pedagogical value and learner experience received the greatest emphasis, followed by technical fit, implementation feasibility, and governance and ethics. The resulting shortlist ranked notation/score-based learning platforms highest overall, followed by LMS-integrated assessment and feedback tools and ear-training/musicianship applications, while the lowest-ranked category consistently underperformed on governance and technical-fit requirements. Sensitivity and scenario analyses indicated that the top tier remained stable, although the top choice can shift under governance-led or resource-constrained priorities. The framework supports defensible procurement and curriculum design by integrating expert and learner evidence, validated weighting, and robust ranking outputs.
Min Xu (Tue,) studied this question.
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