Innovating Blended Teaching in Channel Management: Integrating AIGC and Curriculum Ideological-Political Education
DOI:
https://doi.org/10.6918/IJOSSER.202609_9(9).0029Keywords:
AIGC; blended teaching; channel management; curriculum ideological-political education; marketing education.Abstract
Generative AI is changing Chinese business education at the same time that curriculum ideological-political education asks values to grow out of subject content rather than sit at the end of class. We report a Channel Management reform that brings the two together in a four-stage blended model built on AI-generated scenarios, problem-based learning, ideological micro-debates, and AI-supported feedback. Channel fairness organizes the ideological thread. The workflow uses commercial chatbots, the existing learning platform, and teacher review; no API or custom software is involved. A two-class pilot shows gains in participation, relevance, decision complexity, and value reflection. AI hallucination, uneven digital literacy, and approximate value measurement also emerged. The constraint is less technical than it looks: it lies in goal design, prompt quality, pacing, and the teacher’s final judgment.
Downloads
References
[1] Guha, A., Grewal, D., & Atlas, S. (2024). Generative AI and marketing education: What the future holds. Journal of Marketing Education, 46(1), 6–17. https://doi.org/10.1177/02734753231215436.
[2] Pan, G., & Ni, J. (2024). A cross sectional investigation of ChatGPT-like large language models application among medical students in China. BMC Medical Education, 24, 908. https://doi.org/10.1186/s12909-024-05871-8
[3] Williams, R. T. (2023). The ethical implications of using generative chatbots in higher education. Frontiers in Education, 8, Article 1331607. https://doi.org/10.3389/feduc.2023.1331607
[4] Kaul, A., Malhotra, S., Bathula, H., & Dhir, A. (2026). A scoping review of generative artificial intelligence in business education: Implications for pedagogy and organisational decision making. Journal of Management & Organization, 1–20.
[5] Álvarez-Álvarez, C., & Falcon, S. (2023). Students’ preferences with university teaching practices: analysis of testimonials with artificial intelligence. Educational Technology Research and Development, 71, 1709–1724. https://doi.org/10.1007/s11423-023-10239-8
[6] Yu, Q., Yu, K., Li, B., & Wang, Q. (2025). Effectiveness of blended learning on students' learning performance: a meta-analysis. Journal of Research on Technology in Education, 57(3), 499–520. https://doi.org/10.1080/15391523.2023.2264984
[7] Kapur, M., Hattie, J., Grossman, I., & Sinha, T. (2022). Fail, flip, fix, and feed – Rethinking flipped learning: A review of meta-analyses and a subsequent meta-analysis. Frontiers in Education, 7, Article 956416. https://doi.org/10.3389/feduc.2022.956416
[8] Wang, Y., & Zheng, Y. (2022). Human-machine collaborative learning in the intelligent era: Value connotation, representational forms and practical approaches. China Educational Technology, (9), 90–97. (In Chinese)
[9] Fu, X., Zeng, M., & Zhang, Y. (2023). Overall framework and key link design of human-machine collaborative precision teaching. Open Education Research, 29(2), 91–102. (In Chinese)
[10] Guo, K., Pan, M., Li, Y., & Lai, C. (2024). Effects of an AI-supported approach to peer feedback on university EFL students' feedback quality and writing ability. The Internet and Higher Education, 63, Article 100962. https://doi.org/10.1016/j.iheduc.2024.100962
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Social Science and Education Research

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




