Selecting AI-Enabled Music Learning Technologies in Higher Education Using AHP and TOPSIS

March 5, 2026Open Access

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Key Points

The research aims to identify and select the most effective AI-enabled music learning technologies for higher education using decision-making methodologies.
Utilized the Analytical Hierarchy Process (AHP) for decision-making
Employed the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)
Conducted a questionnaire survey to gather necessary data
Analyzed raw data using Python for processing results
Identified key technologies that facilitate music learning effectively
Provided a structured framework for technology selection in educational settings
Demonstrated the applicability of AHP and TOPSIS for technology assessment

Demonstrates the selection of AI-enabled music learning technologies in higher education, suggesting effective decision-making tools for educators.

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Xu Min (2026) studied this question.

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