Developing and Implementing a Teaching Model for Personalized Learning in Governmental Accounting Supported by an AIGC Knowledge Base
DOI:
https://doi.org/10.54097/dh9nmj24Keywords:
AIGC, Knowledge Base, Governmental Accounting, Personalized Learning, Teaching Model, Self-efficacyAbstract
In response to the teaching challenges posed by the abstract standards, complex practical rules, and substantial individual differences among students in the Governmental Accounting course, this study constructs and implements a five-stage personalized teaching model supported by the AIGC knowledge base, centered on the knowledge system of the course and students' differentiated learning needs. The model comprises pre-class preview diagnosis, in-class teaching by learning, in-class practice linking theory to practice, after-class personalized consolidation and extension, and outcome evaluation based on multi-source data, forming a dynamic closed loop in which the AIGC knowledge base functions as a hub throughout all stages to provide differentiated support for each student. Using the ima knowledge base platform, a course-specific Governmental Accounting knowledge base was constructed, and an eight-week teaching intervention was conducted with a single-group pretest–posttest quasi-experimental design (n = 88). The results showed that the posttest mean scores for learning self-efficacy (M = 4.08, SD = 0.52) and task value (M = 4.18, SD = 0.48) were significantly higher than the corresponding pretest means, with a large effect size for self-efficacy (d = 1.19) and a large effect size for task value (d = 1.03). Knowledge-base usage frequency was moderately and positively correlated with learning self-efficacy (r = 0.44, p < 0.001) and task value (r = 0.30, p < 0.01). Learning self-efficacy showed a gradient increase from low- to high-use groups, and objective grades were also higher in the intervention cohort than in the historical cohort. The findings suggest that the AIGC knowledge base can provide a feasible practical pathway for personalized learning in professional courses such as Governmental Accounting, and that the five-stage teaching model can effectively translate the technological affordances of AIGC into teachers' instructional decisions and students' learning processes.
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[1] Jiang, C., Wei, Y., & Wu, L. (2025). Empowering personalized learning with generative AI: Mechanisms, transformations, and pathways. China Educational Technology, (3), 56–64.
[2] Li, T., & Zhu, J. (2026). The internal mechanism and path optimization of AI-driven personalized learning for students. e-Education Research, 47(1), 78–85.
[3] Wu, D., Li, H., & Chen, X. (2023). An analysis of the educational impact of general-purpose AI large models. Open Education Research, 29(3), 31–40.
[4] Liu, B., Yu, Y., Wang, T., et al. (2024). Educational large models for artificial intelligence: System architecture, core technologies, and typical applications. Journal of East China Normal University (Educational Sciences), 42(5), 67–79.
[5] Lu, X., Xu, A., & Zhang, J. (2026). Exploring the "explanation singularity" of explainable artificial intelligence (XAI) in educational settings. Modern Educational Technology, 36(1), 45–53.
[6] Sun, F., Peng, Y., & Feng, R. (2025). A theoretical framework and technical path for verifying educational AIGC. Modern Distance Education Research, 37(4), 21–30.
[7] Tu, S., Xie, C., Tang, X., et al. (2026). Exploration and practice of a Chaoxing-integrated IMA agent in smart biochemistry classroom. University Chemistry, 41(3), 112–120.
[8] Chen, Y., & Wei, L. (2025). How does AI-driven adaptive learning affect students' personalized learning outcomes? A meta-analysis. Distance Education Journal, 43(2), 88–97.
[9] Dong, Y., Yang, Y., Zhang, H., et al. (2026). An empirical study of AI-companion-supported personalized collaborative learning for college students. Modern Educational Technology, 36(4), 78–87.
[10] Yin, Y., Yang, X., Xu, X., et al. (2025). Reform and practice of a large-model-enabled experimental teaching model in colleges and universities. Experimental Technology and Management, 42(8), 156–164.
[11] Zhu, Y., Zhan, Z., & Yan, S. (2026). A new form of self-directed learning based on generative artificial intelligence: Connotation, framework, and pathways. Modern Distance Education, (2), 34–42.
[12] Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). University of Michigan.
[13] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/2490088.
[14] Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250951
[15] Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4211.
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