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| Innovative Research on AI-empowered Experimental Teaching in Light Chemical Engineering: A Case Study of the Course Pulp and Paper Testing |
| Received:January 24, 2026 Revised:February 27, 2026 |
| DOI：10.11980/j.issn.0254-508X.2026.07.027 |
| Key Words:artificial intelligence (AI) light chemical engineering experimental teaching Pulp and Paper Testing personalized learning |
| Fund Project:国家级大学生创新创业训练计划项目（X2025031）；四川轻化工大学教学改革研究项目（AL202402）。 |
| Author Name | Affiliation | Postcode | | Li Zhiguo* | 1School of Artificial Intelligence, Neijiang Normal University, Neijiang, Sichuan Province, 641100 | 641100 | | Gao Hongxia* | 2School of Food and Liquor Engineering, Sichuan University of Science & Engineering, Yibin, Sichuan Province, 644000 | 644000 | | Zhou Zaifeng* | 3China National Pulp and Paper Research Institute Co., Ltd., Beijing, 100102 | 100102 | | Wang Jian | 1School of Artificial Intelligence, Neijiang Normal University, Neijiang, Sichuan Province, 641100 | 641100 | | Liu Jianrong | 2School of Food and Liquor Engineering, Sichuan University of Science & Engineering, Yibin, Sichuan Province, 644000 | 644000 | | Liu Chun | 2School of Food and Liquor Engineering, Sichuan University of Science & Engineering, Yibin, Sichuan Province, 644000 | 644000 |
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| Abstract:Against the backdrop of digital transformation in education and intelligent industrial upgrading, this paper addresses persistent challenges in experimental teaching for light chemical engineering (pulp and paper engineering), including rigid content, inefficient feedback, one-sided assessment, and insufficient digital integration. Using the course Pulp and Paper Testing as a vehicle, this paper explored pathways for deep AI-pedagogy integration. The research constructed personalized learning paths based on multimodal perception and domain knowledge graphs, built an “edge-cloud-terminal” collaborative intelligent diagnostic feedback platform, and established a three-dimensional, nine-indicator evaluation system. Comparative experiment results demonstrated that the experimental group achieved a 9.0% increase in academic performance of the students, a 57.8% improvement in teaching efficiency, a 34.3% rise in learning satisfaction, while effectively reducing polarization. This model could provide a valuable reference for the digital reform of engineering experimental teaching, contributing to the cultivation of high-caliber engineering and technical talent. |
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