Research on the Construction and Application of Hybrid Teaching Agent in Linux Operating System Curriculum
Abstract
classroom time and difficulty in personalized tutoring, this study developed a hybrid teaching agent of Linux operating system courses based
on deepseek+coze architecture, integrating real-time question answering, error diagnosis and personalized recommendation functions. Supports multi-modal interaction and dynamic knowledge base update. Through the teaching experiment of 112 students, the effectiveness of
this method in improving teaching efficiency and extending practical operation time is verified, but the limitations of semantic understanding
accuracy, knowledge base synchronization time and students dependence tendency are also found. The research provides a theoretical and
practical reference for the intelligent teaching of Linux and other professional courses.
Keywords
Full Text:
PDFReferences
[1] Qi Jing, Yuan Tangqing. 2019. Design and Implementation of Linux Network resource teaching Platform based on Vocational skills
training. Chinese educational technology and equipment. 2019, (13):30-32.
[2] Sun Jiadong, Wang Quan, Hong Tao, et al. 2025. Exploration and practice of course teaching reform for Vehicle Networking Industry: A
case study of "Linux Operating System" course. Times Automobile, 2025, (08):85-87.
[3] Hu Jianbo, Zhao Junjing. 2020. Systematic preparation and "platform-based management" of online teaching: A case study of Xi 'an
Eurasia University. Educational science, 2020, 36(02):17-23.
[4] Ding Zhihua, Mou Yupeng. 2024. Teacher Role Stress and its Coping Strategies in Mixed Education Model. Contemporary Educational
Theory and Practice, 2024, 16(04): 28-35.
[5] Huang Chunqing. 2023. Analysis on the Reconstruction of School-based curriculum System under the Background of New Curriculum
Reforml. Curriculum Teaching Research, 2023, (10):4-13+46.
[6] Zhang Xingwang, Li Jie, Li Sifan, et al. 2025. A Theoretical model, model innovation and important implications of DeepSeek enabling
Library knowledge service. Journal of Agricultural Library and Information, 1-13.
[7] Cao Lu, Sun Shiliang. 2024. Research on Operation and Maintenance of intelligent System of Oil and gas sales Enterprise based on
Large model and multi-agent Collaboration. Vehicle energy storage, transportation and marketing technology, 2024, 2(06):45-53.
[8] Sun Mengge, Fu Yun, Liu Xiwen. 2025. Analysis on the path of knowledge service for Intelligent Agent Enabled Research. Think Tank
Theory and Practice, 2025, 10(01): 3-18.
[9] Ma Fangming, Han Longgang, Guo Bao, et al. 2024. Research on Intelligent transformation and upgrading of Base station operation and maintenance number based on Agent Technology. Telecommunication Engineering Technology and Standardization, 2024,
37(09):48-52.
[10] Lu Haiou. 2025. Opening a New Chapter of High Level Self-intelligent Network with Intelligent Net. Communication World, 2025,
(02):12-15.
DOI: http://dx.doi.org/10.70711/aitr.v2i9.6882
Refbacks
- There are currently no refbacks.
