Exploring Chinese Music Students' <scp>AI</scp> Familiarity and <scp>AI</scp> Self‐Efficacy: A Mixed‐Methods Study Based on Social Cognitive Theory ( <scp>SCT</scp> )

Abstract

ABSTRACT The role of artificial intelligence (AI) tools in various aspects of education has been examined in the literature. However, music education has sparsely focused on the interface of AI and students' education. To address the gaps, this study employed a mixed‐methods study to figure out Chinese music students' AI familiarity and AI self‐efficacy and their actual representations using two questionnaires and a semi‐structured interview. A total of 317 students took part in the study in two phases. The results of descriptive statistics revealed a moderate to moderately high level of both AI familiarity and AI self‐efficacy among the students. In the qualitative phase, it was found that AI familiarity was represented in the participants' “enhanced academic performance”, “enhanced critical thinking and problem‐solving skills”, and “provision of AI‐driven content”. Moreover, the students showcased their AI self‐efficacy in their “successful task completion”, “taking complex projects”, and “helping others by giving them feedback”. The findings are discussed and implications for theory and practice are provided to encourage educators for an AI‐powered music education.

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