ARTICLE
Volume 10,Issue 8
18 May 2026
Research on AI-Enabled Single-Chip Microcomputer Teaching Quality Based on Neural Networks
Yuanyuan Zhang ,
Guiqiang Zhang ,
Jianqin Liu ,
Meng Yan ,
Qiong Shen
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1
School of Computer and Software Engineering, Anhui Institute of Information Technology, Wuhu 241100, Anhui, China,
2
State-owned Wuhu Machinery Factory, Wuhu 241007, Anhui, China,
JCER 2026 , 10(4), 202–206;
https://doi.org/10.26689/jcer.v10i4.14772
© 2026 by the Authors. Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract
With the gradual penetration of artificial intelligence technology into the field of education, to explore the application value of Generative Artificial Intelligence (AIGC) in traditional teaching, this paper sorts out the innovative application models of AIGC with the single-chip microcomputer course as the research object. By comparing and analyzing the teaching achievements of this course in the two academic years of 2024–2025, the effectiveness of AI empowerment in improving the teaching quality of single-chip microcomputers is verified. Meanwhile, based on the Back Propagation neural network algorithm, a prediction model for students’ final exam scores is constructed by integrating multidimensional data such as students’ classroom performance, experimental report scores, and phased test results. After training and verification, the prediction accuracy of the model on the test set reaches 76.9%.
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