Proceedings of the 2025 4th International Conference on Educational Science and Social Culture (ESSC 2025)

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AI-Driven Real-Time Feedback Mechanism in the Innovative Practice and Deep Integration of Blended Teaching

Authors
Dengxia Zhang1, Liming Hu1, *, Guifeng Xiao1, Xinliang Hu1
1Army Arms University of PLA, Hefei, 230031, China
*Corresponding author. Email: 13637064031@163.com
Corresponding Author
Liming Hu
Available Online 25 March 2026.
DOI
10.2991/978-2-38476-553-9_29How to use a DOI?
Keywords
Artificial Intelligence Technology; Real-Time Feedback Mechanism; Blended Teaching; Teaching Model
Abstract

Conducting online and offline blended teaching models relying on internet platforms has become an important teaching model widely adopted by universities. However, traditional blended teaching models lack precise and real-time feedback mechanisms, leaving shortcomings in areas such as personalized learning path recommendations, higher-order thinking feedback, and complex practical scenario feedback. Taking the mechanical and electronic engineering course as an example, AI-driven real-time feedback mechanisms not only promotes the transformation of blended teaching from “standardized transmission” to “personalized empowerment”, but also addresses the issues of “slow error correction and difficult guidance” in practical course teaching, providing new perspectives and strategies to improve the quality of blended teaching.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2025 4th International Conference on Educational Science and Social Culture (ESSC 2025)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
25 March 2026
ISBN
978-2-38476-553-9
ISSN
2352-5398
DOI
10.2991/978-2-38476-553-9_29How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Dengxia Zhang
AU  - Liming Hu
AU  - Guifeng Xiao
AU  - Xinliang Hu
PY  - 2026
DA  - 2026/03/25
TI  - AI-Driven Real-Time Feedback Mechanism in the Innovative Practice and Deep Integration of Blended Teaching
BT  - Proceedings of the 2025 4th International Conference on Educational Science and Social Culture (ESSC 2025)
PB  - Atlantis Press
SP  - 253
EP  - 261
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-553-9_29
DO  - 10.2991/978-2-38476-553-9_29
ID  - Zhang2026
ER  -