Keywords
AI empowerment
Human-machine collaboration
Teaching practice
Submitted : 2026-05-09
Accepted : 2026-05-24
Published : 2026-06-08
Abstract
Aiming at the problems of interdisciplinary integration, difficulty in personalized learning, and lack of real-time feedback in the traditional teaching of compiler courses, this paper proposes an AI-enabled human-machine collaborative teaching mode and builds an intelligent AI teaching platform. This mode integrates students, teachers, and AI into an organic whole. AI provides students with personalized services such as relevant course knowledge recommendation, learning resource recommendation, and quiz push, and provides teachers with intelligent services such as automatic homework correction and learning data analysis feedback. Teaching practice analysis shows that this mode has improved students’ final exam scores and learning experience.
References
Wang B, Lan X, He Y, et al., 2026, Research on the Application of AI Large Models Empowering College Teachers’ Teaching. Software Guide, 1–9.
Fang Y, 2026, Research on Human-Machine Collaborative Teaching Mode of Automotive English Based on Post-Editing—Taking Geely University as an Example. Special Purpose Vehicle, (03): 92–94.
Li X, Peng L, Jin H, 2024, Research on the Construction of Technology-Enabled Human-Machine Collaborative Translation Teaching Mode. Foreign Language World, (03): 43–50.
Zhang X, Yao Z, 2026, Construction and Practice of “Six-Whole Penetration, Four-Intelligence Collaboration” Intelligent Teaching Mode for Computer Majors in Higher Vocational Colleges Based on Human-Machine Collaboration. Modern Vocational Education, (07): 156–159.
He S, 2025, Research and Practice of Digital Badge-Driven Human-Machine Collaborative Teaching Mechanism in Higher Vocational Colleges—Taking “Python Programming” as an Example. Journal of Guangdong University of Light Industry, 24(05): 62–66 + 80.
Zhou X, Xu Y, Guo Q, et al., 2023, Research on Process Assessment Teaching Mode Reform of “Big Data Technology Principles and Applications” under the Background of New Engineering. China New Communications, 25(22): 98–100 + 67.
Zhu L, Liu Q, Chen H, et al., 2025, Reconstruction and Practice of Digital Media Technology Major Teaching Mode from the Perspective of Human-Machine Collaboration. Computer Knowledge and Technology, 21(36): 103–106.
Wang Y, Mao M, Huang Y, et al., 2025, Application of AI Large Model Technology in Compiler Principles Courses. Software Guide, 24(08): 201–206.
Li S, Ye R, 2025, Five Application Paths of Generative Artificial Intelligence in Primary and Secondary School Classroom Teaching. Basic Education Curriculum, (11): 39–44.
Tang H, 2025, Research on Automatic Recommendation Method of Teaching Resources for Computer Courses in Colleges and Universities. Wireless Internet Technology, 22(20): 125–128.
Jiang W, 2025, Automatic Recommendation Method of Teaching Resources for Computer Courses Based on Deep Learning Algorithm. Wireless Internet Technology, 22(18): 61–64.
Luo Z, Long T, 2025, Research on Adaptive Teaching Platform Based on Knowledge Graph Recommendation Algorithm. Automation and Instrumentation, (12): 257–261 + 266.
Chen J, Zhou G, Zhong Y, 2026, Retraction Note: A Personalized Recommendation System for Teaching Resources in Sports Using Fuzzy C-Means Clustering Technique. Soft Computing, (prepublish): 1.
Gao L, 2026, Intelligent Recommendation Method of Digital Teaching Resources Integrating Knowledge Graph and Student Portrait. Computer Programming Skills & Maintenance, (01): 159–161 + 167.
Xu Z, Dong Y, Wang L, et al., 2025, Refined Outcome-Oriented Compiler Principles Courses Based on Knowledge Graph. Computer Education, (04): 75–79.