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Innovative AI-Driven Teaching Model for Agricultural Mechanics: Design, Implementation, and Evaluation

Author(s)

Dongming Zhang1, Yuting Yan1, Shujuan Yi1,*, Xiaoming Fu1, Yifei Li1,2

Affiliation(s)

1College of Engineering, Heilongjiang Bayi Agricultural University, Daqing, Heilongjiang, China 2College of Engineering, Northeast Agricultural University, Harbin, Heilongjiang, China *Corresponding Author

Abstract

This study proposes an AI-driven teaching model for Agricultural Mechanics that emphasizes intelligent guidance, virtual simulation, and personalized assessment. The model integrates smart recommendations, 3D virtual labs, and big-data feedback to facilitate a more profound integration of theory and practice. In the initial implementation phase, there was a significant improvement in students' theoretical scores, which increased by 18.5%, and a significant improvement in their practical skills, which increased by 23.3%. This implementation also resulted in significant improvements in interaction and learning autonomy among the students. The study proposes a replicable paradigm for higher agricultural machinery education reform and application-oriented talent development by addressing challenges in platform adaptation, algorithm accuracy, simulation-to-reality transfer, and resource investment. The proposed solutions include staged training, algorithm enhancement, and MR-based hybrid laboratories.

Keywords

AI-Driven Teaching Model; Virtual Simulation; Personalized Assessment; Agricultural Mechanics Education

References

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