Advancing Creative Thinking: Construction and Application of a GenAI-Based Multi-Agent Collaborative System

Abstract

Amid the rapid evolution of generative artificial intelligence (GenAI), how to effectively leverage its affordances to advance students’ creative thinking development has emerged as a frontier issue in educational research. However, existing studies generally encounterthe dilemma of unclear coupling pathways between GenAI and thinking development, and a lack of pedagogical implementation for technological applications. To tackle these issues, this study constructs a multi-agent collaborative system oriented toward creative problem solving. The system is designed based on the dual logics of cognitive processes and psychological drivers, incorporating six core agents, a dynamic scheduling mechanism, and a three-layer task–knowledge–thinking knowledge graph to support the development of students’ creative thinking. A quasi-experimental study with four task rounds is conducted to compare the effectiveness of this system with that of a single agent model in supporting undergraduates’ creative problem solving. The results indicate that students’ overall creative thinking ability improved significantly, with the experimental group outperforming the control group and the performance gap widening progressively across tasks. Post hoc comparison further reveals that advancing creative thinking occurs primarily in the middle-to-late stages of the tasks. These findings demonstrate that the support of the multi-agent collaborative system can effectively activate and reorganize students’ cognitive representations through sustained interaction, thereby facilitating a transition in creative thinking from gradual development to stage-based advancement.

Keywords

multimodal large language models (LLMs) / generative artificial intelligence (GenAI) / innovation-oriented talent / creative thinking

RIGHTS & PERMISSIONS

Higher Education Press