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The Innovation Path of Experimental Teaching in Built Environment Disciplines Driven by AI Technology

Author(s)

Xiangming Cheng*, Erlin Meng, Feiyue Qian

Affiliation(s)

School of Environmental Science and Engineering, Suzhou University of Science and Technology, Jiangsu, Suzhou, China *Corresponding Author.

Abstract

This paper focuses on the application of AI technology in the experimental teaching of building environment majors, and takes DeepSeek as the entry point to deeply discuss how it drives the innovation of experimental teaching. By analyzing the cognitive reconstruction and ethical framework under its empowerment, as well as the specific practice in teaching content, form and efficiency improvement, this paper reveals the core value of experimental teaching of built environment under the reconstruction, proposes the development model of teachers' intelligent ability, and looks forward to the potential impact of the integration of AI, Internet of Things and digital twin technology on professional experimental teaching in the future. The research aims to solve the "last mile" problem of intelligent technology landing in the experimental teaching of built environment majors, and provide reference teaching reform ideas for college teachers.

Keywords

AI Teaching Integration; Building Environment Experiment; DeepSeek; Virtual Simulation; Teaching Ethics; Personalized Learning

References

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