Keywords
Generative Artificial Intelligence
Blended teaching
Back Propagation neural network
Prediction model
Submitted : 2026-04-18
Accepted : 2026-05-03
Published : 2026-05-18
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%.
References
Gao J, Liu H, Shi B, et al., 2025, Practical Teaching Reform of “Single-Chip Microcomputer” Course Design Integrating Competition and Teaching. Journal of Electrical and Electronic Teaching, 47(02): 49–52.
Mao D, Wang M, Shi Z, et al., 2022, Curriculum Design of Single-Chip Microcomputer Practical Teaching. Electronic Technology, 51(09): 79–81.
Wang W, 2024, Social Risks and Governance Paths of Generative Artificial Intelligence Applications. Journal of Nanjing University of Posts and Telecommunications (Social Science Edition), 26(05): 28–39.
Tan J, Zhao L, 2025, Reconstruction of Teaching Paradigm Based on Knowledge Graph Driven by “Golden Course” Construction and Digital Transformation. Heilongjiang Education (Theory and Practice), 1–5.
Guo X, 2025, Research on the Improvement Strategy of College Teachers’ Teaching Competence from the Perspective of Artificial Intelligence. China Management Informationization, 28(09): 233–236.
Ma D, Yu Z, Liu Y, 2014, Research and Application of BP Neural Network in the Prediction of College Graduates’ Employment Rate. Journal of Jiamusi University (Natural Science Edition), 32(05): 751–753.
Gao H, 2021, Analysis and Exploration of Score Prediction Based on Online Learning Behavior Data. Journal of Nanjing Radio and TV University, (01): 53–57.
Xing R, Chang S, He K, et al., 2025, Research on AIGC Image Quality Assessment Indicators. Journal of Nanjing University of Information Science and Technology, 17(01): 63–73.
Shan X, Zhang C, Li Q, et al., 2025, Development Trends of Knowledge Service Field in the AIGC Era. Library Theory and Practice, (01): 47–56.
Xiao L, 2026, Research on the Precise Ideological and Political Path of Counselors in Private Vocational Undergraduate Universities Empowered by Generative Artificial Intelligence. Journal of Yanbian Institute of Education, 40(01): 36–41.
Zhang X, Li Y, Weng Y, et al., 2024, Visualization Analysis of Research Hotspots in Artificial Intelligence Education Based on Big Data. Journal of Jiamusi University (Natural Science Edition), 42(06): 142–145 + 180.
Wang C, Chen S, Jiang X, et al., 2026, Multi-Dimensional Modeling of Brain-Like Artificial Neural Networks and Their Application in Medical Image Analysis. Chinese Journal of Medical Devices, 1–14.
Wang Y, 2025, Research on HFB DAC Aliasing Error Correction Method Based on Back Propagation Neural Network, dissertation, Guizhou University.
Liang X, Liu Y, Yang S, 2025, Prediction of Deformation Modes of Deep Composite Rock Mass Based on BP Neural Network. Technology Innovation and Application, 15(18): 57–61.
Hu B, Gao Y, Xi H, 2024, Research on Aircraft-Missile Separation Trajectory Prediction Based on Error Back Propagation Neural Network. Aerodynamic Research and Experiment, 2(02): 59–65.