Programming education has become increasingly vital within global K–12 curricula, and large language models (LLMs) offer promising solutions to systemic challenges such as limited teacher expertise and insufficient personalized support. Adopting a human-centric and systems-oriented perspective, this study employed a six-week quasi-experimental design involving 103 Grade 7 students in China to investigate the effects of instruction supported by the iFLYTEK Spark model. Results showed that the experimental group significantly outperformed the control group in programming performance, cognitive interest, and programming self-efficacy. Beyond these quantitative outcomes, qualitative interviews revealed that LLM-assisted instruction enhanced students’ self-directed learning, a sense of real-time human–machine interaction, and exploratory learning behaviors, forming an intelligent human–AI learning system. These findings underscore the integrative potential of LLMs to support competence, autonomy, and engagement within digital learning systems. This study concludes by discussing the implications for intelligent educational system design and directions for future socio-technical research.

This study explored the relationship between technology acceptance and programming self-efficacy in the context of AI-supported programming learning, with learning engagement, reflective use, learning interest, and learning satisfaction acting as potential mediators. A total of 131 high school students participated in three weeks of AI agent-assisted programming learning and completed a questionnaire after the intervention. A cross-sectional, nonexperimental design was adopted, and PROCESS v5.0 Model 82 was used to examine multiple serial mediation effects. The results showed that technology acceptance did not have a significant direct effect on programming self-efficacy, whereas significant indirect effects were identified. Mediation analysis revealed that learning interest may play a critical mediating role in relation to programming self-efficacy. In addition, a significant serial mediating pathway was found through learning engagement and reflective use, indicating that technology acceptance was indirectly associated with programming self-efficacy through increased learning engagement and reflective use. These findings contribute to a deeper understanding of the mechanisms underlying the development of students’ programming self-efficacy in AI-supported programming learning and provide practical implications for the design and implementation of AI-assisted programming instruction.