Abstract
With the impressive enhancement and development of computer technology, artificial intelligence (AI) and machine learning (ML) are implemented in every field of life. Music is one of these sectors where AI and ML have been applied and gained traction in recent years. Both AI and ML are cutting-edge fields that are utilized to create and manipulate sounds in games, music, and other applications. Innovative and sophisticated approaches based on AI and machine learning are being used to improve music teaching. Furthermore, by employing these methods, the sounds in games can be made more efficient and effective. Evaluation of the role of AI and ML in music education is one of the most difficult and challenging areas for teaching and learning researchers due to the use of these approaches. The Fuzzy Analytical Hierarchy Process (Fuzzy AHP) approach was used to assess the role of AI and machine learning in music instruction. Fuzzy AHP is a basic and straightforward way of making better decisions based on criteria and options. In the proposed study, we used Fuzzy AHP to determine the weightages of seven criteria and five alternatives. When we tested these paradigms, we got good results that let us move forward and improve the principles and framework for AI and ML to help music education grow creatively.
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Hong Yun, Z., Alshehri, Y., Alnazzawi, N. et al. A decision-support system for assessing the function of machine learning and artificial intelligence in music education for network games. Soft Comput 26, 11063–11075 (2022). https://doi.org/10.1007/s00500-022-07401-4
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DOI: https://doi.org/10.1007/s00500-022-07401-4