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Special Issue: Enhancing the Student Experience | Acta Pedagogia Asiana | Volume 5 - Issue SI - 2026 | 45−54 | https://doi.org/10.53623/apga.v5iSI.1050
© 2026 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Construction of an Educational Teaching Evaluation System under the Context of Deep Integration of Artificial Intelligence and Teaching Practices

Author(s): Tianru Zhang 1 , Huangjia Wu 2 , Yixin Feng 1 , Quang Zhang 1 , Myeongsu Seong 1 , Jie Sun 1 , Mark Leach 1 , Eng Gee Lim 1
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1 School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, China
2 College of Science & Technology, Ningbo University, Ningbo, China

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Acta Pedagogia Asiana

Acta Pedagogia Asiana

Volume 5 - Issue SI - 2026

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In an era marked by the deep integration of artificial intelligence (AI) with educational practices, this study explores the transformation and optimization of educational teaching evaluation systems. Recognizing the pivotal role of AI in reshaping teaching and learning environments, the research delves into the design of a comprehensive evaluation framework that aligns with the dynamic nature of contemporary education. It emphasizes the integration of AI-driven tools and methodologies to enhance the accuracy, efficiency, and fairness of educational teaching evaluation. The study proposes a multifaceted approach, including categorized indicator setting, process-oriented evaluation, multi-stakeholder involvement, broadened evaluation perspectives, and dynamic student performance monitoring. Through a critical analysis of existing practices and theoretical frameworks, a model is proposed to foster a more adaptive, equitable, and student-centered educational landscape. The ultimate goal is to harness AI’s potential to elevate educational outcomes and promote continuous improvement in teaching practices.

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Cirneanu, A.-L.; Moldoveanu, C.-E. (2024). Use of Digital Technology in Integrated Mathematics Education. Applied System Innovation, 7, 66. https://doi.org/10.3390/asi7040066.

Holmes, W.; Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542–570. https://doi.org/10.1111/ejed.12533.

Alenezi, A.; Alenezi, A. (2025). Evaluating the Effectiveness of Chatbot-Assisted Learning in Enhancing English Conversational Skills Among Secondary School Students. Education Sciences, 15, 1136. https://doi.org/10.3390/educsci15091136.

Zhou, S.; Xie, H.; Hu, Y. (2023). The Promotion of AI in the Digital Transformation of Education—Review of “Applications of Artificial Intelligence in Education.” Applied Chemistry, 52(07), 2267. https://doi.org/10.16581/j.cnki.issn1671 3206.2023.07.012.

Abatan, A.; Jacks, B.S.; Ugwuanyi, E.D.; Nwokediegwu, Z.Q.S.; Obaigbena, A.; Daraojimba, A.I.; Lottu, O.A. (2024). The role of environmental health and safety practices in the automotive manufacturing industry. Engineering Science & Technology Journal, 5(2), 531–542. https://doi.org/10.51594/estj.v5i2.830.

Ayorinde, O.B.; Daudu, C.D.; Okoli, C.E.; Adefemi, A.; Adekoya, O.O.; Ibeh, C.V. (2023). Reviewing the impact of LNG technology advancements on global energy markets. World Journal of Advanced Research and Reviews, 21(2), 335–345. https://doi.org/10.30574/wjarr.2024.21.2.0462.

Nwokediegwu, Z.Q.S.; Ugwuanyi, E.D.; Dada, M.A.; Majemite, M.T.; Obaigbena, A. (2024). Water energy nexus: A review of policy and practice in Africa and the USA. Magna Scientia Advanced Research and Reviews, 10(1), 286–293. https://doi.org/10.30574/msarr.2024.10.1.0031.

Lee, D.; Yeo, S. (2022). Developing an AI based chatbot for practicing responsive teaching in mathematics. Computers & Education, 191, 104646. https://doi.org/10.1016/j.compedu.2022.104646.

Shukla, A.; Pokhariya, H.S.; Michaelson, J.; Laxminarayanamma, K.; Kumar, M.; Krishna, O. (2023). Distributed deep reinforcement learning for autonomous IoT healthcare devices in the cloud. Proceedings of the 2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI). https://doi.ieeecomputersociety.org/10.1109/ICAIIHI57871.2023.10488976.

Abina, A.; Temeljotov Salaj, A.; Cestnik, B.; Karalič, A.; Ogrinc, M.; Kovačič Lukman, R.; Zidanšek, A. (2024). Challenging 21st-Century Competencies for STEM Students: Companies’ Vision in Slovenia and Norway in the Light of Global Initiatives for Competencies Development. Sustainability, 16, 1295. https://doi.org/10.3390/su16031295.

Chen, Y.; Li, S.; Chen, R. (2025). Impact of Industry and Education Integration on Employment Quality in Higher Vocational Colleges: Moderating Role of Faculty Qualifications and Curriculum Development Capacity. Education Sciences, 15, 1316. https://doi.org/10.3390/educsci15101316.

Wang, X.; Zhao, J.; Lu, Y.; Li, X. (2024). Spatial Pattern, Quality Evaluation, and Implications of Preschool Education Facilities in New Urban Areas Using Multi-Source Data: A Case Study from Lingui New District in West China. Buildings, 14, 1718. https://doi.org/10.3390/buildings14061718.

Wan, L.; Qiao, X.; Li, M. (2023). Research on the Application of Learning Pass in Vocational Blended Teaching. Science and Technology Information, 21(03), 171–175. https://doi.org/10.16661/j.cnki.1672 3791.2206 5042 9892.

Bao, L. (2024). “Learning Pass + Smart Vocational Education” to Optimize Vocational Teaching. Science and Technology Wind, 09, 72–74. https://doi.org/10.19392/j.cnki.1671 7341.202409024.

Martins, R.M.; Gresse Von Wangenheim, C. (2023). Findings on teaching machine learning in high school: A ten year systematic literature review. Informatics in Education, 22(3), 421–440. https://doi.org/10.15388/infedu.2023.18.

Haroud, S.; Saqri, N. (2025). Generative AI in Higher Education: Teachers’ and Students’ Perspectives on Support, Replacement, and Digital Literacy. Education Sciences, 15, 396. https://doi.org/10.3390/educsci15040396.

Ogor, E.N. (2007). Student academic performance monitoring and evaluation using data mining techniques. Electronics, Robotics and Automotive Mechanics Conference (CERMA 2007), 354–359. https://doi.org/10.1109/CERMA.2007.4367712.

Elbourhamy, D.M.; Najmi, A.H.; Elfeky, A.I.M. (2023). Students’ performance in interactive environments: An intelligent model. PeerJ Computer Science, 9, e1348. https://doi.org/10.7717/peerj cs.1348.

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SUBMITTED: 12 February 2026
ACCEPTED: 30 March 2026
PUBLISHED: 9 April 2026
SUBMITTED to ACCEPTED: 47 days
DOI: https://doi.org/10.53623/apga.v5iSI.1050

Cite this article
Zhang, T. ., Wu, H. ., Feng, Y. ., Zhang, Q. ., Seong, M. ., Sun, J. ., Leach, M. ., & Lim, E. G. . (2026). Construction of an Educational Teaching Evaluation System under the Context of Deep Integration of Artificial Intelligence and Teaching Practices. Acta Pedagogia Asiana, 5(SI), 45−54. https://doi.org/10.53623/apga.v5iSI.1050
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