Integration of AI-Based Inquiry Based Learning Model in Cultural Arts Education on Appreciative Attitudes and Traditional Music Skills
DOI:
https://doi.org/10.33394/jollt.v13i4.17241Keywords:
Inquiry based learning, Artificial intelligence, Appreciative attitudes, Traditional music skillsAbstract
his study aims to explore the effect of the Artificial Intelligence (AI)-based Inquiry Based Learning (IBL) model on appreciative attitudes towards traditional music and traditional music skills among language learners. This study used a quasi-experimental design with a non-equivalent control group design. The research sample consisted of 60 language learners divided into two groups: an experimental group that used the AI-based IBL learning model and a control group that followed conventional learning. The data were analysed using multivariate analysis of variance (MANOVA) to test the effect of the learning model on the two dependent variables. The findings indicate that the AI-based IBL model has a significant effect on both dependent variables. The experimental group experienced a greater increase in appreciative attitudes and traditional music skills compared to the control group. These findings suggest that the application of AI in arts education can increase student engagement and learning outcomes. In addition, the use of AI in the IBL model allows for instant feedback, which accelerates learning and improves students' practical skills. This study has significant pedagogical implications, showing that AI-based technology can transform the way arts learning is conducted by creating a more personalised and adaptive learning experience. These findings contribute to the development of technology-based education, particularly in the context of traditional music learning, and open up opportunities for the wider use of smart technology in arts education in the future.
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