To address the key challenges in traditional Polymer Physics education

such as the difficulty in comprehending abstract theoretical concepts

constraints in experimental training

and insufficient development of higher-order competencies

this study introduces a “teacher-student-AI” collaborative education model that deeply integrates artificial intelligence (AI) technology to redesign the entire teaching process. A closed-loop instructional framework of “AI-assisted knowledge deconstruction-teacher-student collaborative inquiry-innovative practice verification” was established

supported by visualization tools

virtual experiment platforms

and AI-based analytical systems

effectively overcoming the spatiotemporal and cognitive limitations inherent in conventional teaching. Over a two-year period

the model was implemented among students majoring in functional materials (2022 and 2023 cohorts) at the North China Institute of Aerospace Engineering. A comparative analysis between the experimental and control classes revealed that the experimental group demonstrated significantly better performance across multiple metrics: core knowledge point mastery (89.6% versus 68.3%)

annual output of innovative experiment designs (12 versus 5 projects per year)

and award rates in disciplinary competitions (31.2% versus 12.5%). Furthermore

the integration of AI tools enhanced the efficiency of teacher-student collaboration by more than 40%. This model

with “high-order thinking

innovation

and challenge” at its core

establishes a “teacher-student-AI” collaborative theoretical framework. It not only provides a replicable practical path for building a “golden course” in polymer physics but also serves as a paradigmatic reference for the deep integration of intelligent technology and specialized curricula.