Integrating Generative AI into Ideological and Political Education in Higher Vocational Colleges
Abstract
generation, intelligent mediation and multi-actor collaboration, all the while maintaining ethical boundaries. However, there are also concrete
risks that must be addressed, such as content hallucination, algorithmic bias, the gradual erosion of student privacy, and an excessive reliance
on AI that could undermine teachers' professional judgement. To mitigate these risks, the paper proposes a four-layer architecture consisting
of context generation, intelligent mediation, relationship reconstruction, and ethical governance, with the governance layer cutting across the
other three. Building on this architecture, the paper outlines four practical pathways: scenario reconstruction, intelligent integration, relationship reshaping, and institutional governance.
Keywords
Full Text:
PDFReferences
[1] UNESCO. Guidance for generative AI in education and research[R]. Paris: UNESCO, 2023.
[2] OECD. OECD Digital Education Outlook 2023: Towards an effective digital education ecosystem[R]. Paris: OECD Publishing, 2023.
[3] Wayne Holmes, Maya Bialik, and Charles Fadel. Artificial intelligence in education: Promises and implications for teaching and
learning[M]. Boston: Center for Curriculum Redesign, 2019.
[4] Neil Selwyn. Should robots replace teachers? AI and the future of education[M]. Cambridge: Polity Press, 2019.
[5] Rose Luckin, Wayne Holmes, Mark Griffiths, Laurie B. Forcier. Intelligence unleashed: An argument for AI in education[M]. London:
Pearson, 2016.
[6] Feifei Zheng, Huadi Wang, Xueying Gu, Gongjing Wang. The impact of generative artificial intelligence literacy on higher-order thinking skills in Chinese higher vocational college students: Integrating variable-centered and person-centered approaches[J]. Acta Psychologica, 2026, 268: 107190.
[7] Hanhui Li, Yurui Zhang, Mingwen Chen, Tao Zhao, Min Jou. Creative personal identity in the age of generative AI: A social-cognitive
pathway of AI literacy, self-efficacy, and mindset[J]. Computers in Human Behavior, 2026, 175: 108838.
[8] Yangyang Luo, Guohui Zhou, Yiran Cui. Understanding Generative Artificial Intelligence Adoption in Higher Education Faculty: Evidence from Chinese Universities and Technical and Vocational Colleges[J]. Education and Information Technologies, 2026, 31(6): 1711-
1751.
[9] Yantao Shi, Haiyan Cui, Yuanchang Zhang, Xueli Hui, Guanghai Li, Mingkun Ouyang, Lingle Pan. The impact of artificial intelligence
perception on university students' academic engagement: the mediating role of academic motivation and the moderating role of academic self-efficacy[J]. Frontiers in Psychology, 2026, 17: 1735410.
[10] Isha Agrawal, Himanshi Verma. Human Creativity in the Age of Artificial Intelligence: A Review of Recent Literature[J]. Trinity Journal
of Management, IT & Media, 2025, 16(1): 37-45.
[11] Liangqing Li. Hybrid Ideas of Artificial Intelligence and Human: Growth of Creativity in College Educational Programs[J]. Psychology
of Aesthetics, Creativity & the Arts, 2026, 20(4): 945-958.
[12] Zongqiang Ren, Xin Shen, Shouhui Xia. Knowledge Co-Creation by Human-Machine Social Interaction: Based on Decision Field
Theory[J]. Systems Research & Behavioral Science, 2026, 1.
DOI: http://dx.doi.org/10.70711/frim.v4i9.10237
Refbacks
- There are currently no refbacks.
