Smart Course Construction for Signal Analysis and Processing: A Three-Dimensional Ecological Framework Based on Knowledge Graphs and AI Tutoring

Authors

  • Junmei Guo Faculty of Electronics, Electrical Engineering and Control, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China Author
  • Qingchun Chen Faculty of Electronics, Electrical Engineering and Control, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China Author

DOI:

https://doi.org/10.70088/9q7ny236

Keywords:

smart course, Signal Analysis and Processing, knowledge graph, AI teaching assistant

Abstract

Digital transformation in higher education relies heavily on smart course development as its core practical carrier. Many existing smart course practices for Signal Analysis and Processing merely integrate standalone technical tools while overlooking the holistic integration of curriculum elements and the in-depth reconstruction of teaching processes. Drawing on the inherent pedagogical characteristics of this engineering course, this paper proposes a smart course development framework anchored in knowledge graphs, empowered by AI teaching assistants for interactive tutoring, and sustained by data-driven teaching diagnostic and feedback mechanisms. This study argues that smart course construction is far more than a simple upgrade of technical equipment; it represents a comprehensive reshaping of the ecosystem of knowledge organization, instructional interaction and formative assessment within curriculum design. The practical value of such courses does not hinge on the richness of AI functions; instead, it depends on how deeply intelligent technologies are embedded into disciplinary teaching logic.

References

L. Zhang, J. X. Yin, and B. Yang, "Exploring systematic AI integration into a series of courses: A case study of signal-related foundational course clusters," Military Higher Education Research, vol. 48, no. 3, pp. 53–57, 107, 2025, doi: 10.3969/j.issn.2097-4043.2025.03.007.

R. Wang and H. Y. Zhou, "Exploring smart teaching for 'Signal Analysis and Processing' in the digital information era," Industry and Information Education, no. 3, pp. 31–35, 2023.

L. Y. Huang, X. Y. Feng, L. W. Guo, et al., "Learning design innovation in the intelligent age: Concepts, focuses and key technologies," Open Education Research, vol. 30, no. 1, pp. 55–64, 2024.

R. S. Baker, "Artificial intelligence in education: Adaptive tutoring and learning analytics," Int. J. Artif. Intell. Educ., vol. 31, no. 3, pp. 415–438, 2021.

Y. Chen et al., "Knowledge graph-based personalised learning path recommendation for STEM courses," Comput. Educ., vol. 189, p. 104567, 2022.

T. Zhu and S. Gao, "Simulation-assisted intelligent teaching for signal processing courses," IEEE Trans. Educ., vol. 66, no. 2, pp. 112–119, 2023.

Q. Wang et al., "Visual knowledge graph construction for engineering foundational courses," Computer Education, vol. 29, no. 7, pp. 76–81, 2022.

B. Hu and X. Lin, "Research on digital teaching reform of signal and system courses," J. Electr. Electron. Eng. Educ., vol. 32, no. 6, pp. 102–107, 2020.

Downloads

Published

28 September 2026

Issue

Vol. 3 No. 4 (2026)

Section

Article

License

Copyright (c) 2026 Junmei Guo, Qingchun Chen (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Guo, J., & Chen, Q. (2026). Smart Course Construction for Signal Analysis and Processing: A Three-Dimensional Ecological Framework Based on Knowledge Graphs and AI Tutoring. Artificial Intelligence and Digital Technology, 3(4), 65-71. https://doi.org/10.70088/9q7ny236