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

Background: Artificial intelligence–generated content (AIGC) has stepped into the spotlight with the emergence of ChatGPT, making effective use of AIGC for education a hot topic. Objectives: This 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. Methods: Three 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 Conclusions: The 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. Lay Description: What is currently known about the subject matter?: In the present information era, programming and computational thinking are important.AIGC has attracted remarkable attention from both academics and managers.If it is appropriately utilised, AIGC can facilitate education. What this paper adds: Three forms of AIGC integration based on the level of abstraction, which enhance programming learning and computational thinking.Application of extended effective use theory to propose an underlying mechanism for how AIGC integration affects learning performance and computational thinking.Concrete information on the utilisation of AIGC in the education domain.Evidence that shows the importance of interaction transparency and representational fidelity for leveraging information technologies in education. Implications of the study findings for practitioners: AIGC can be an effective tool for teachers, learners, and institutions.Platform designers and teachers should carefully design AICG integration.Platform designers and teachers could make use of various methods and other forms of AIGC integration to promote interaction transparency. [ABSTRACT FROM AUTHOR]