Curriculum ideological and political education (CIPE) has emerged as a critical approach to fostering moral character and ideological awareness in higher education, yet its implementation often suffers from superficial integration and fragmented practices. This study proposes a generative artificial intelligence (AI)-supported assessment framework for evaluating the deep integration of CIPE across disciplinary courses and develops targeted support strategies to enhance integration effectiveness. Drawing upon cognitive transfer theory, value internalization models, and the Technological Pedagogical Content Knowledge (TPACK) knowledge framework, this study first constructs a multi-dimensional evaluation indicator system encompassing four core dimensions: political guidance, cultural identity, ethical norms, and practical innovation. Employing the Analytical Hierarchy Process combined with the Delphi method, this study establishes a three-level indicator structure equipped with adaptive quantification algorithms to accommodate disciplinary differences. Subsequently, this study develops a multimodal assessment methodology leveraging large language models through prompt engineering, implementing a dual-cycle assessment system enhanced by proximal policy optimization reinforcement learning. The framework integrates Bidirectional Encoder Representations from Transformers and Long Short-Term Memory (BERT-LSTM) hybrid neural networks to generate self-explanatory integration index cloud maps. Furthermore, this study designs an intelligent support strategy framework that dynamically generates personalized prompts and triggers adaptive intervention mechanisms. A quasi-experimental study conducted across six university courses demonstrates that the proposed assessment method achieves 87.3% consistency with expert ratings (Cohen’s κ=0.82), and the intervention strategies yield a 23.7% improvement in CIPE integration depth compared to control groups. This research contributes both theoretically and practically to the intelligent evaluation and targeted enhancement of curriculum’s ideological and political integration in the digital education era.
References
[1] Wang, J. (2021) Analysis of challenges and countermeasures of ideological and political education in colleges and universities in the new era. Journal of Higher Education Research, 2(6), 35.
[2] Cheng, P. (2022) Evaluation method of ideological and political classroom teaching quality based on analytic hierarchy process. Scientific Programming, 2022(1), 6554084.
[3] Yan, L., Greiff, S., Teuber, Z., Gašević, D. (2024) Promises and challenges of generative artificial intelligence for human learning. Nature Human Behaviour, 8(10), 1839-1850.
[4] Miseliunaite, B., Kliziene, I., Cibulskas, G. (2022) Can holistic education solve the world’s problems: a systematic literature review. Sustainability, 14(15), 9737.
[5] Du, J., Chen, X., Dou, Y. (2024) Evaluating the practical effectiveness of college counselors’ ideological and political education using big data video streaming. Wireless Networks, 30(1), 1-15.
[6] Yang, Y., Long, Y., Sun, D., Van Aalst, J., Cheng, S. (2020) Fostering students’ creativity via educational robotics: an investigation of teachers’ pedagogical practices based on teacher interviews. British Journal of Educational Technology, 51(5), 1826-1842.
[7] Liu, W., He, C. (2022) Curriculum-based ideological and political education: research focuses and evolution. International Education Studies, 15(5), 28-35.
[8] Ruan, X., Yang, X. (2024) Integrating ideological and political education into curriculum development in the new era: perspectives from English as a foreign language teaching in universities of China. Open Journal of Modern Linguistics, 14(03), 344-352.
[9] Wu, J., Wang, X. (2022) Research on the mode of specialized and integrated education in colleges and universities based on the perspective of curriculum ideology and politics. International Journal of Education and Humanities, 3(1), 66-69.
[10] Chiva-Bartoll, O., Fernández-Rio, J. (2022) Advocating for service-learning as a pedagogical model in physical education: towards an activist and transformative approach. Physical Education and Sport Pedagogy, 27(5), 545-558.
[11] Leary, M. P., Sherlock, L. A. (2020) Service‐learning or internship: a mixed-methods evaluation of experiential learning pedagogies. Education Research International, 2020(1), 1683270.
[12] Ren, W., Wang, R., Mohamad, S. N. A., Xie, Y., Chen, L., Ning, H., Shi, J. (2024) A quantitative analysis of the influence of ideological and political education on students’ learning satisfaction. Journal of Infrastructure, Policy and Development, 8(1), 1-14.
[13] Zhang, X., Zhang, P., Shen, Y., Liu, M., Wang, Q., Gašević, D., Fan, Y. (2024) A systematic literature review of empirical research on applying generative artificial intelligence in education. Frontiers of Digital Education, 1(3), 223-245.
[14] AlAli, R., Wardat, Y. (2024) Opportunities and challenges of integrating generative artificial intelligence in education. International Journal of Religion, 5(7), 784-793.
[15] Alasadi, E. A., Baiz, C. R. (2023) Generative AI in education and research: opportunities, concerns, and solutions. Journal of Chemical Education, 100(8), 2965-2971.
[16] Ren, M., Chen, N., Qiu, H. (2023) Human-machine collaborative decision-making: an evolutionary roadmap based on cognitive intelligence. International Journal of Social Robotics, 15(7), 1101-1114.
[17] Batista, J., Mesquita, A., Carnaz, G. (2024) Generative AI and higher education: trends, challenges, and future directions from a systematic literature review. Information, 15(11), 676.
[18] Ng, D. T. K., Chan, E. K. C., Lo, C. K. (2025) Opportunities, challenges and school strategies for integrating generative AI in education. Computers and Education: Artificial Intelligence, 8, 100373.
[19] Kurtz, G., Amzalag, M., Shaked, N., Zaguri, Y., Kohen-Vacs, D., Gal, E., Zailer, G., Barak-Medina, E. (2024) Strategies for integrating generative AI into higher education: Navigating challenges and leveraging opportunities. Education Sciences, 14(5), 503.
[20] Vo, N. N., Vu, Q. T., Vu, N. H., Vu, T. A., Mach, B. D., Xu, G. (2022) Domain-specific NLP system to support learning path and curriculum design at tech universities. Computers and Education: Artificial Intelligence, 3, 100042.
[21] Luo, Y., Yang, Y. (2024) Large language model and domain-specific model collaboration for smart education. Frontiers of Information Technology & Electronic Engineering, 25(3), 333-341.
[22] Mohammadi, M., Tajik, E., Martinez- Maldonado, R., Sadiq, S., Tomaszewski, W., Khosravi, H. (2025) Artificial intelligence in multimodal learning analytics: a systematic. Computers and Education, 8, 100426.
[23] Chango, W., Lara, J. A., Cerezo, R., Romero, C. (2022) A review on data fusion in multimodal learning analytics and educational data mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(4), e1458.
[24] Guettala, M., Bourekkache, S., Kazar, O., Harous, S. (2024) Generative artificial intelligence in education: Advancing adaptive and personalized learning. Acta Informatica Pragensia, 13(3), 460-489.
[25] Mohamed, F. N., Azhar, J., Yasmeen, S., Hussain, I., Khawar, M. (2025) Generative artificial intelligence and personalized learning environment: challenges and opportunities. Southern Journal of Computer Science, 1(01), 1-36.
[26] Yu, J., Song, J., Lu, Y. (2025) Harnessing generative artificial intelligence to construct multimodal resources for Chinese character learning. Systems, 13(8), 692.
[27] Lu, X. (2024) The internal mechanisms and practical paths for leveraging generative artificial intelligence to empower high-quality development in university ideological and political education. Adult and Higher Education, 6(8), 80-86.
[28] Hoadley, C., Campos, F. C. (2022) Design-based research: What it is and why it matters to studying online learning. Educational Psychologist, 57(3), 207-220.
[29] Li, Y., Cheng, H., Qin, Q. (2025) Evaluations and improvement methods of deep learning ability in blended learning. International Journal of e-Collaboration (IJeC), 21(1), 1-17.
[30] Zhang, B., Velmayil, V., Sivakumar, V. (2023) A deep learning model for innovative evaluation of ideological and political learning. Progress in Artificial Intelligence, 12(2), 119-131.
[31] Kooli, C., Yusuf, N. (2025) Transforming educational assessment: insights into the use of ChatGPT and large language models in grading. International Journal of Human-Computer Interaction, 41(5), 3388-3399.
[32] Fagbohun, O., Iduwe, N. P., Abdullahi, M., Ifaturoti, A., Nwanna, O. M. (2024) Beyond traditional assessment: Exploring the impact of large language models on grading practices. Journal of Artificial Intelligence, Machine Learning and Data Science, 2(1), 1-8.
Share and Cite
Cai, Q., Xiang, Y., Zhan, F. (2026) Generative AI-supported Deep Integration Assessment and Support Strategies for Curriculum Ideological and Political Education. Global Education Bulletin, 3(3), 32-46. https://doi.org/10.71052/grb2025/ZXRO4406