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AI-Empowered Real-Time Classroom Assessment in Industry–Education Integration: Practical Explorations and Implications for Loose-Leaf Teaching Material Optimization

Author(s)

Dejiao Wang, Yuan Xia*

Affiliation(s)

Chongqing Industry & Trade Polytechnic, Chongqing, China *Corresponding Author

Abstract

The evaluation of industry-education integration within classroom settings typically encounters challenges such as delays, pronounced subjectivity, and the inability to provide real-time feedback on pedagogical issues. This study utilises the Mechanical Drawing course from a specific vocational college as a case study, introducing a lightweight AI classroom analysis tool. It collects and analyses real-time evaluation data, including AI-generated metrics on classroom participation, the frequency of interactions between educators and students, and heat maps of knowledge mastery across four practical training classes focused on industry-education integration. Experience has demonstrated that AI tools can produce visual evaluation reports within five minutes of class completion, thereby objectively highlighting the weaknesses present in the classroom and assisting teachers in promptly adjusting their teaching strategies. This method markedly enhances evaluation efficiency in comparison to traditional approaches that rely on listening to and assessing classes. Furthermore, AI evaluation has revealed limitations in the recognition of deeper competencies, including the standardisation of skill operations and adherence to professional ethics. This study proposes a hybrid evaluation model comprising "AI real-time evaluation, key point assessment by enterprise mentors, and self-evaluation by students". Furthermore, it explores the viable pathway for utilising AI evaluation data as a foundation for the dynamic revision of loose-leaf textbooks. Practical verification indicates that this approach significantly improves the timeliness and accuracy of classroom assessments within the context of industry-education integration, all without imposing additional burdens on teachers. Consequently, it offers a replicable and streamlined solution for similar courses.

Keywords

AI Classroom Analysis Tools; Industry–Education Integration; Real-Time Assessment; Loose-Leaf Teaching Materials; Vocational Colleges

References

[1]Xu, G. Q. Industry–education integration community: Toward the institutionalized construction of school–enterprise relations. Journal of East China Normal University (Educational Sciences), 2025(11). [2]Pei, X. J. Research on the talent cultivation model for new energy vehicle majors in higher vocational colleges from the perspective of industry–education integration. Shaanxi Education (Higher Education), 2026(6): 64-66. DOI:10.16773/j.cnki.1002-2058.2026.06.022. [3]Xie, B. X. Value implications, practical dilemmas and implementation paths of AI-empowered evaluation reform in vocational education. Vocational and Technical Education, 2025(35): 31-36. [4]Guo, X. D., Zeng, F. L., & Li, L. X. Exploration of vocational education teaching evaluation pathways under the background of industry–education integration. Jiaoyu Kexue Wenxian, 2025(10): 100-106. [5]Zhang, L., Jiang, X. Q., & Wang, D. Y. Research on the innovative path of vocational education evaluation driven by big data. Guizhou Education News, 2025-10-17(A12). [6]Guo, J. N. Value, risk and strategy: A three-dimensional exploration of generative artificial intelligence empowering classroom teaching evaluation in higher vocational education. Vocational Education Research, 2025(12): 47-56. [7]Zhang, Y. P. On logical mechanism and application model of AIGC-enabled classroom teaching evaluation. Journal of Yellow River Conservancy Technical University, 2026, 38(1): 73-79. [8]Li, Z. New form of teaching materials in vocational education: Connotations, characteristics, and compilation strategies. Journal of Vocational Education Forum, 2020(4). [9]Deng, B. Research on the construction of loose-leaf teaching materials for the computer network application major in secondary vocational schools under the background of the "three education" reform. Hongshulin, 2024(19): 97-99. [10]Liu, Q. T., Wu, L. J., & Li, M. Research on the application of artificial intelligence in teaching evaluation: From the perspective of classroom behavior analysis. China Educational Technology, 2021(8): 45-51. [11]Chen, M. X., & Lai, Z. L. The turn of classroom evaluation in the intelligent era: Real-time feedback based on process data. Modern Distance Education Research, 2022, 34(4): 56-64.