Bridging awareness and behavior: decoding implicit metacognitive behaviors in AI-assisted programming via fine-grained log analysis

= 60) within a Python programming course. Using a customized Jupyter environment, an integrated autonomous AI agent monitored real-time behavioral logs and triggered non-directive, process-oriented prompts based on specific algorithmic thresholds. Data collection integrated fine-grained log analysis with standardized assessments to quantify implicit planning, monitoring, and regulation processes. The AI intervention significantly optimized learning behaviors, facilitating a shift from impulsive "trial-and-error" approaches to deliberate planning and superior debugging precision. These behavioral improvements were accompanied by significant gains in both academic performance and subjective metacognitive awareness compared to the control group. The findings confirm that when designed as a process-oriented scaffold, AI functions as a catalyst for self-regulated learning rather than a passive crutch. This study highlights the role of AI as a psychological scaffold that supports metacognitive regulation, providing an evidence-based blueprint for the design of effective learning environments in educational psychology.