Amidst the rapid advancement of artificial intelligence (AI)

this study presents a significant teaching innovation for the “Polymer Materials and Engineering” curriculum

specifically within the course of “Polymer Material Forming and Processing”. To address the limitations of traditional one-way theoretical instruction

we implemented a project-based learning framework centered on real-world industrial challenges

including process optimization and quality control. Student teams were tasked with leveraging Python frameworks and corporate database resources to implement machine learning algorithms. Their objective was to investigate the correlation between processing parameters and product performance

leading to the construction of predictive models for intelligent parameter adjustments. This methodology

which involves stages of data collection

model training

and deployment

successfully achieved deep integration of AI technology with core course principles. Consequently

students’ interdisciplinary problem-solving capabilities and engagement were markedly enhanced. Furthermore

the AI-assisted solutions developed by students demonstrated a significant improvement in the pass rate of experimental products

with successful validation conducted on enterprise pilot production lines. Through the renewal of both content and pedagogy in the “Polymer Material Forming and Processing” course

this initiative establishes an effective pathway for fostering versatile talents prepared for smart manufacturing in the polymer industry. Furthermore

it has substantial significance by driving the digital transformation of “Polymer Materials and Engineering” education in universities.