The emergence of ChatGPT has sparked new expectations for AI-empowered educational transformation. However, it remains unknown which factors affect its effectiveness in empowering learners to solve programming problems. Therefore, this study employed a quasi-experimental research design and used Python graphing in programming education as an example to investigate the factors influencing the effectiveness of learners in applying ChatGPT to problem-solving. Findings: AI literacy significantly influences learners’ effectiveness in using ChatGPT to solve problems, with AI awareness and AI usage being key factors. The knowledge base of programming language significantly affects learners’ effectiveness in applying ChatGPT to solve programming problems. Learners’ cognitive level of ChatGPT significantly influences their effectiveness in applying ChatGPT to problem-solving, while usage intention does not have a significant impact. However, learners’ intention to use ChatGPT significantly improves after application. Based on these findings, this study proposes that in the process of empowering education with Artificial Intelligence Generated Content (AIGC) products, the focus on learners should shift from cultivating their AI usage to AI literacy, laying the foundation for empowering learning with AIGC products. It is suggested to shift from mastering specific knowledge to graph-based rules as a method for empowering learning with AIGC products. Additionally, the focus should shift from enhancing learners’ intention to use the technology to strengthen their technological awareness, thereby creating practical pathways for empowering learning with AIGC products.

ChatGPT的出现，引发了AI赋能教育变革的新期待。然而，尚不清楚哪些因素会影响其帮助学习者解决编程问题的有效性。因此，本研究采用准实验的研究设计，以编程教育中的Python绘图为例，探讨影响学习者应用ChatGPT解决问题的有效性的因素。研究结果：人工智能素养显着影响学习者使用 ChatGPT 解决问题的有效性，其中人工智能意识和人工智能使用是关键因素。编程语言的知识库显着影响学习者应用ChatGPT解决编程问题的有效性。学习者对ChatGPT的认知水平显着影响其应用ChatGPT解决问题的有效性，而使用意愿则没有显着影响。然而，学习者使用ChatGPT的意愿在应用后显着提高。基于这些发现，本研究提出，在人工智能生成内容（AIGC）产品赋能教育的过程中，学习者的关注点应从培养人工智能使用转向人工智能素养，为AIGC产品赋能学习奠定基础。建议从掌握特定知识转向基于图的规则作为AIGC产品赋能学习的方法。此外，重点应从增强学习者使用技术的意图转向强化他们的技术意识，从而为利用 AIGC 产品增强学习能力创造实用途径。