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Bridging awareness and behavior: decoding implicit metacognitive behaviors in AI-assisted programming via
Xiaojing Hou1, Zhichun Liu1, Guoqiang Wang1
1School of Computer Science, Luoyang Institute of Science and Technology, Luoyang, China.
Frontiers in Psychology
|April 27, 2026
Summary
Generative artificial intelligence (AI) interventions significantly improved computer science students' learning behaviors and metacognitive awareness in programming. AI acts as a scaffold, promoting deliberate planning and debugging over trial-and-error.
Area of Science:
- Educational Psychology
- Artificial Intelligence in Education
- Computer Science Education
Background:
- Generative AI's potential in programming education is significant but its impact on students' cognitive and behavioral patterns is not well understood.
- Existing research often overlooks the "black box" of how AI interventions influence internal learning processes.
- Metacognitive regulation and self-regulated learning are crucial for effective programming, yet their interplay with AI tools needs further investigation.
Purpose of the Study:
- To investigate the influence of AI-driven interventions on metacognitive regulation and self-regulated learning in computer science undergraduates.
- To address the "black box" issue by analyzing how AI impacts students' internal cognitive and behavioral patterns during programming tasks.
- To quantify the effects of AI interventions on implicit planning, monitoring, and regulation processes.
Main Methods:
- A randomized controlled trial involving 122 Computer Science undergraduates was conducted.
- Participants were assigned to either an AI-assisted intervention group or a control group within a Python programming course.
- An AI agent in a customized Jupyter environment monitored behavioral logs and provided process-oriented prompts, with data collected via log analysis and standardized assessments.
Main Results:
- The AI intervention group demonstrated optimized learning behaviors, shifting from "trial-and-error" to deliberate planning and improved debugging.
- Significant gains in academic performance were observed in the AI intervention group compared to the control group.
- Students in the AI group reported enhanced subjective metacognitive awareness.
Conclusions:
- AI interventions, when designed as process-oriented scaffolds, act as catalysts for self-regulated learning in programming education.
- AI serves as a psychological scaffold, effectively supporting metacognitive regulation and enhancing learning behaviors.
- The study provides an evidence-based framework for designing effective AI-powered learning environments in educational psychology.
Keywords:
generative AI in educationhuman–AI interactionmetacognitive scaffoldingprocess data analysisprogramming educationself-regulated learningMore Related Videos
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