Haoming Wang

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.

With the rapid development of artificial intelligence technology in the field of education, AI-Agents have shown tremendous potential in collaborative learning. However, traditional Computer-Supported Collaborative Learning (CSCL) methods still have limitations in addressing the unique demands of programming education. This study proposes an innovative AI-Agent-supported Collaborative Learning (AI-CL) approach, aimed at enhancing students’ programming learning experience and effectiveness through intelligent assistance from AI-Agents. The research developed an AI-Agent system based on large language models and employed a quasi-experimental design to investigate the effects of the AI-CL method on students’ learning achievement, self-efficacy, cognitive load, and learning interest. The experiment was conducted at a university in Shanghai, recruiting 45 undergraduate students who were randomly assigned to the AI-CL experimental group (n = 24) and the CSCL control group (n = 21). The experimental context was set in an ACM programming competition teaching environment, lasting for 6 weeks with 70-min sessions each week. Results showed that the AI-CL group significantly outperformed the CSCL group in learning achievement, self-efficacy, and learning interest. Regarding cognitive load, the AI-CL group demonstrated significantly lower mental effort compared to the CSCL group, while there was no significant difference in mental load. These findings not only provide new theoretical perspectives for the application of cognitive load theory and self-efficacy theory in AI-assisted learning environments but also offer strong practical guidance for higher education institutions to introduce AI-Agent-assisted collaborative learning models in programming courses.

Vocabulary acquisition is the foundation of language learning, and Computer-Assisted Vocabulary Learning (CAVL) environments offer new possibilities for enhancing learning efficiency. However, conventional CAVL environments often employ static learning content and limited interaction mechanisms, making it difficult to accommodate individual learner differences and cognitive needs, which leads to insufficient motivation and suboptimal memory retention. This study proposed an Intelligent Interactive Companion-assisted Vocabulary Learning (IIC-VL) approach, which was based on multi-agent technology and dynamically adjusted learning content according to learners’ proficiency levels, learning styles, and emotional states. An experimental design was adopted, with 100 students recruited from a high school and randomly assigned to an experimental group (n = 49) and a control group (n = 51). The experimental group used the IIC-VL approach for vocabulary learning, while the control group used the conventional CAVL approach. The intervention lasted for 12 wk, with two learning sessions per week, each lasting 45 min. The results showed that, compared to the control group, the experimental group demonstrated significant improvements in vocabulary tests (both receptive and productive), motivation for learning, and flow experience. Subsequent semi-structured interviews revealed students ’ positive attitudes and perceptions toward the IIC-VL approach. These findings not only provided empirical support for the application of multi-agent technology in CAVL environments but also offered a new practical direction for personalized learning paths in foreign language vocabulary instruction.