Research on Path Innovation and Quantitative Effectiveness of Generative AI Empowering Precise Ideological and Political Education in Universities
Main Article Content
Abstract
Against the dual backdrop of digital transformation in education and the improvement of the quality and efficiency of ideological and political education, traditional ideological and political education in universities is facing structural difficulties such as homogenization of content supply, low accuracy of audience matching, insufficient interactive experience, and vague evaluation of effectiveness, making it difficult to meet the personalized and diversified growth needs of college students in the new era. Generative AI, with its core technological advantages such as content generation, intelligent interaction, personalized recommendation, and data analysis, provides breakthrough technical support for the precision, intelligence, and scenario-based upgrading of ideological and political education.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
H. Stein, “Toward a taxonomy of generative AI use cases in business contexts: Integrating complexity, risk, and strategy,” Journal of Computational Science, vol. 96, pp. 102826-102826, 2026, DOI: 10.1016/j.jocs.2026.102826.
F. Alshammari, “Pedagogical transformation through generative AI: a hybrid deep learning comparison with traditional digital learning materials based on GenAI-EduNet framework,” Quality & Quantity, to be published, pp. 1-32, 2026, DOI: 10.1007/s11135-026-02699-w.
M. Pham V, W. Ha, H. Ullah, et al., “Time-series prediction on thermal performance of energy slab using LSTM network with generative AI-based data augmentation,” Applied Thermal Engineering, vol. 294, pp. 130581-130581, 2026, DOI: 10.1016/j.applthermaleng.2026.130581.
J. Bauerdick, L. Streibel, M. Milde, et al., “Chatbots für die Produktion,” Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 121, no. 3, pp. 194-198, 2026, DOI: 10.1515/zwf-2026-1010.
M. Chung E, A. Choi E, and L. Sun Y, “Perception of sex and race diversity in critical care medicine by generative AI: biases, measurement and implications,” Critical Care, vol. 30, no. 1, pp. 108-108, 2026, DOI: 10.1186/s13054-026-05897-y.
S. Simaiya, V. Singh, P. Challa, et al., “AI driven system for enhancing consumer electronics through maintenance personalization and security,” Scientific reports, 2026, DOI: 10.1038/s41598-026-37401-5.
N. Asl S, L. Osapoetra O, G. Czarnota J, et al., “AI-enabled RF data synthesis for breast ultrasound: efficacy in quantitative ultrasound tissue characterization,” Scientific reports, 2026, DOI: 10.1038/s41598-026-43319-9.
A. Siddik B, “Navigating the funding landscape: how later-stage investments propel generative AI startups,” Journal of Entrepreneurship in Emerging Economies, vol. 18, no. 2, pp. 371-396, 2026, DOI: 10.1108/JEEE-08-2025-0471.
O. Miller K and Z. Elyoseph, “Breaking bad news with reflective AI virtual experience (BRAVE): development and evaluation of a generative AI-based simulation tool,” Advances in simulation (London, England), 2026, DOI: 10.1186/s41077-026-00429-8.
R. R K and S. S, “Generative AI-driven topology optimization for mass reduction in aerospace drone structures,” Results in Engineering, vol. 29, pp. 109745-109745, 2026, DOI: 10.1016/j.rineng.2026.109745.
N. Murtaza, A. Rezzoug, M. Sikandar A, et al., “A Systematic Review of Flood Management Evolution, with Emphasis on How Generative AI Reshapes Prediction-to-Decision Pathways,” Water, vol. 18, no. 5, pp. 582-582, 2026, DOI: 10.3390/w18050582.
J. Steuer and A. Kahraman, “Unlocking the undruggable spliceosome: generative AI and structural dynamics in cancer therapy,” Frontiers in Cell and Developmental Biology, vol. 14, pp. 1774239-1774239, 2026, DOI: 10.3389/fcell.2026.1774239.
Y. Ye and T. Li, “Large-scale winter streetscape perception assessments via integrating street view images with generative AI technique,” Building and Environment, vol. 293, pp. 114335-114335, 2026, DOI: 10.1016/j.buildenv.2026.114335.
B. Lledo, P. Carbone, J. Ortiz A, et al., “Assessing the performance of generative AI chatbots in preimplantation genetic testing: a comparative study of expert evaluations,” Reproductive BioMedicine Online, vol. 52, no. 3, pp. 105275-105275, 2026, DOI: 10.1016/j.rbmo.2025.105275.
T. Németh, S. Irandoust, and A. Dávidovics, “AI in medical and dentistry education: perspectives from international students, educators and physicians,” BMC medical education, 2026, DOI: 10.1186/s12909-026-08886-5.
R. Song, A. Alias, and K. Jamaludin B A, “Heutagogy and generative AI: an empirical investigation of self-determined learning on deep learning outcomes,” Frontiers in Education, vol. 11, pp. 1760298-1760298, 2026, DOI: 10.3389/feduc.2026.1760298.
H. Shao, S. Zhu, C. Zhang, et al., “Determinants of healthcare professionals’ acceptance of DeepSeek-assisted clinical decision support applications: a cross-sectional study,” BMC medical informatics and decision making, 2026, DOI: 10.1186/s12911-026-03404-5.
L. Streibel, J. Geiger, O. Bernhard, et al., “Bewertung von Chatbots für die Produktion,” Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 121, no. 1-2, pp. 78-82, 2026, DOI: 10.1515/zwf-2026-1012.
L. Koc Z, S. Koc G, A. Inci, et al., “Guidelines vs,” generative AI in CKD patient education: the role of prompt engineering and expert blinded evaluation. BMC nephrology, 2026, DOI: 10.1186/s12882-026-04814-3.
T. Yin, J. Chen, T. Yao, et al., “Development and initial validation of a video-mediated listening test based on multimodal generative AI,” Acta psychologica, vol. 264, Art. no. 106521, 2026, DOI: 10.1016/j.actpsy.2026.106521.