While generative artificial intelligence offers transformative potential for programming education, its impact on students' internal cognitive and behavioral patterns remains underexplored. This study aims to address this “black box” issue by investigating how AI-driven interventions influence metacognitive regulation and self-regulated learning. A randomized controlled trial was conducted with 122 Computer Science undergraduates (mean age = 19.6 years; 28.2% female) from a university in China. Participants were assigned to an AI-assisted intervention group ( n = 62) or a control group ( n = 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.

Over-representation analysis (ORA) is widely used to interpret selected-gene lists, including differentially expressed genes and cell-type marker genes. Unlike ranked-list gene-set enrichment analysis (GSEA), ORA tests whether selected genes are over-represented in predefined gene sets under an explicit background. Repeated analysis of large gene-set libraries, however, can be computationally costly. GPU-accelerated methods such as rapidGSEA mainly target ranked-list GSEA and do not address repeated exact-ORA processing. We developed paraORA to accelerate repeated ORA by preparing a gene-set library once and reusing its compressed representation across queries. In the GPU-exact path, overlap counting, exact hypergeometric testing, Benjamini–Hochberg correction, and odds-ratio calculation are performed on the GPU. Across 425 numerical validation cases on three GPU models, paraORA agreed with CPU-reference results within the prespecified tolerance while preserving statistical decisions and rankings. On an RTX 4090, the CPU/GPU runtime ratio for the complete in-memory workflow increased from 1.89 for one query to 60.10 for 100 queries against 141,002 gene sets, including one-time preparation but excluding file writing. In a separate complete file-to-file benchmark, GPU-exact was 2.55-fold faster than CPU execution. An exploratory limma reanalysis showed direction-specific Hallmark enrichment. paraORA is available as a web service and an open-source package.