An Empirical Study on How People Perceive AI-generated Music

Chu, Hyeshin; Kim, Joohee; Kim, Seongouk; Hongkyu, Lim,; Lee, Hyunwook; Jin, Seungmin; Lee, Jong-Eun; Kim, Tae‐Hwan; Ko, Sungahn

doi:10.1145/3511808.3557235

Cited by 5 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, this study is a preliminary exploration of integrating AIGC into the learning environment, which has theoretical significance for further research on the role of AIGC in learning. Most of the current literature focuses on applications of AIGC in entertainment and content creation, such as AI news copy generation (Kreps et al, 2020), AI poetry writing (Köbis & Mossink, 2021), and AI lyrics and composition (Chu et al, 2022). Our research expands AIGC integration into learning.…”

Section: Discussionmentioning

confidence: 99%

“…AI tools cannot only cooperate with untrained newcomers to create music (Louie et al, 2020), but also generate music automatically (Ma et al, 2021). AI-generated songs can stimulate participants' musical creativity, including writing lyrics or dancing (Chu et al, 2022). In education, AIGC is believed to be beneficial for facilitating students' learning and improving instructors' lessons when it is used appropriately (Yu, 2023).…”

Section: Ai-generated Contentmentioning

confidence: 99%

See 1 more Smart Citation

Empowering learners with AI‐generated content for programming learning and computational thinking: The lens of extended effective use theory

Shanshan,

Sen

2024

Computer Assisted Learning

View full text Add to dashboard Cite

BackgroundArtificial intelligence–generated content (AIGC) has stepped into the spotlight with the emergence of ChatGPT, making effective use of AIGC for education a hot topic.ObjectivesThis study seeks to explore the effectiveness of integrating AIGC into programming learning through debugging. First, the study presents three levels of AIGC integration based on varying levels of abstraction. Then, drawing on extended effective use theory, the study proposes the underlying mechanism of how AIGC integration impacts programming learning performance and computational thinking.MethodsThree debugging interfaces integrated with AIGC by ChatGPT were developed for this study according to three levels of AIGC integration design. The study conducts a between‐subject experiment with one control group and three experimental groups. Analysis of covariance and a structural equation model are employed to examine the effects.Results and ConclusionsThe results show that the second and third levels of abstraction in AIGC integration yield better learning performance and computational thinking, but the first level shows no difference compared to traditional debugging. The underlying mechanism suggests that the second and third levels of abstraction promote transparent interaction, which enhances representational fidelity and consequently impacts learning performance and computational thinking, as evidenced in test of the mechanism. Moreover, the study finds that learning fidelity weakens the effect of transparent interaction on representational fidelity. Our research offers valuable theoretical and practical insights.

show abstract

Section: Discussionmentioning

confidence: 99%