Volume 4,Issue 3
A Study on the Micro-Mechanisms of AI-Enabling Transformation in Psychology Education Classroom Teaching Under the Guidance of Core Competencies
Artificial intelligence (AI) technology is gradually moving from the edge of educational management to the core of classroom teaching. However, current research mostly focuses on macro policy interpretation or technical system development, lacking sufficient in-depth insight into how technology truly functions in the microenvironment of the classroom, this crucial “black box” phenomenon. Based on distributed cognition theory and the teaching structure analysis framework, this study cuts into three micro dimensions: teacher-student interaction, cognitive processing, and evaluation feedback, attempting to reveal the inherent mechanism of AI-driven classroom transformation. The research finds that avoiding the superficial application of technology and the data masking effect is key to achieving deep teaching transformation. AI does not replace macro teaching methods, but rather silently promotes the reconstruction of teaching structure by reshaping classroom information flow, redistributing cognitive load, and shortening feedback cycles.
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