PBL Teaching Transformation Based on AI Collaborative Education: A Two-Way Reconstruction Path of Teacher Roles and Student Abilities

Dan Yao; Ke Liu

doi:10.26689/jcer.v9i11.12866

Dan Yao Changchun College of Electronic Technology, Changchun, Jilin, China
Ke Liu Changchun College of Electronic Technology, Changchun, Jilin, China

DOI: https://doi.org/10.26689/jcer.v9i11.12866

Keywords: Artificial intelligence, Project-based learning, Teacher role, Student ability, Collaborative education

Abstract

This paper focuses on the transformation of the project-based learning (PBL) teaching model driven by artificial intelligence (AI), and explores the two-way reconstruction path of teacher roles and student abilities. Combining metacognitive theory to analyze the pain points of traditional PBL, this paper systematically sorts out the functional reconstruction path of AI in the dimensions of teaching design, process monitoring, and evaluation feedback. Then, starting from the social role theory, this paper deeply analyzes the transformation of teacher identity and the reconstruction of student abilities in the AI-PBL fusion scenario. AI not only reshapes the logic of cultivating students’ abilities but also prompts teachers to achieve deep changes in their roles at the cognitive, relational, and ethical levels. Human-machine collaboration should not replace teachers’ emotional values and educational judgments, but should become a key support for optimizing the educational ecology and realizing personalized education.

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