Research on the Intrinsic Mechanism and Innovative Mechanism of Artificial Intelligence Empowering the Value Guidance of Ideological and Political Education in Universities
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Abstract
In the context of deep integration between education digitization and artificial intelligence in higher education, ideological and political education, as the core carrier for cultivating morality and talents, faces changing educational environments, diversified youth thinking, and the limitations of traditional paradigms. Value guidance is the essential goal of ideological and political education in universities, involving the shaping of worldviews, life philosophies, values, ideals, patriotism, and moral character. Traditional value guidance often shows strong indoctrination, insufficient precision, fixed content forms, weak process tracking, neglect of individual differences, and limited emotional resonance. This study analyzes the intrinsic mechanism and innovative mechanism through which artificial intelligence empowers ideological and political value guidance. Based on survey and interview data from universities and vocational colleges, it constructs a theoretical model including value cognition adaptation, value identity infiltration, value internalization and transformation, and value behavior regulation. It further proposes innovation mechanisms for content supply, teaching implementation, communication guidance, evaluation feedback, and institutional guarantee. In digital campus environments, wireless communication platforms and electromagnetic information systems provide the infrastructure for intelligent ideological profiling, precise delivery, and compliant data governance.
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References
S, “P,” D. News Sentiment Informed Time-series Analyzing AI (SITALA) to curb the spread of COVID-19 in Houston. Expert Systems With Applications, vol. 180, Art. no. 115104, 2021, doi: 10.1016/j.eswa.2021.115104.
A. Krishnaswamy, “R., Krishnan H,” R. A preliminary analysis of AI based smartphone application for diagnosis of COVID-19 using chest X-ray images. Expert Systems With Applications, vol. 183, Art. no. 115401, 2021, doi: 10.1016/j.eswa.2021.115401.
B. Narjes, M. Ramzi, Z. Soraya, et al., “Lack of AI-based method for pneumocystis pneumonia classification in radiological diagnosis of SARS-CoV-2,” Clinical Imaging, vol. 79, pp. 94-95, 2021, doi: 10.1016/j.clinimag.2021.03.037.
B. Narjes, M. Ramzi, Z. Soraya, et al., “SARS-CoV-2 diagnosis using medical imaging techniques and artificial intelligence: A review,” Clinical Imaging, vol. 76, pp. 6-14, 2021, doi: 10.1016/j.clinimag.2021.01.019.
N. Robert, “D., D,” S. B. Collective action on artificial intelligence: A primer and review. Technology in Society, pp. 66, 2021, doi: 10.1016/j.techsoc.2021.101649.
L. Bin, F. Yuhao, X. Zenggang, et al., “Research on AI security enhanced encryption algorithm of autonomous IoT systems,” Information Sciences, vol. 575, pp. 379-398, 2021, doi: 10.1016/j.ins.2021.06.016.
L. Wanyue, Z. Xinyue, and Y. Qian, “Designing medical artificial intelligence for in-and out-groups,” Computers in Human Behavior, vol. 124, Art. no. 106929, 2021, doi: 10.1016/j.chb.2021.106929.
L. Jing, H. Bei, C. Xi, et al., “An effective AI integrated system for neuron tracing on anisotropic electron microscopy volume,” Biomedical Signal Processing and Control, pp. 69, 2021, doi: 10.1016/j.bspc.2021.102829.
E. (Christine), “S., Sujin B., Danny D,” H., et al. Consumer engagement via interactive artificial intelligence and mixed reality. International Journal of Information Management, pp. 60, 2021, doi: 10.1016/j.ijinfomgt.2021.102382.
M. Kypros and J, “M,” B., Victor G., et al. Review of application of AI techniques to Solar Tower Systems. Solar Energy, vol. 224, pp. 500-515, 2021, doi: 10.1016/j.solener.2021.06.009.
D. Sercan and H. Gaye, “G., Selcuk B,” How do Artificial Intelligence and Robotics Stocks co-move with traditional and alternative assets in the age of the 4th industrial revolution? Implications and Insights for the COVID-19 period. Technological Forecasting & Social Change, pp. 171, 2021, doi: 10.1016/j.techfore.2021.120989.
N. Emanuel, “D., Vincenzo S., Alfredo P,” Emotion recognition at the edge with AI specific low power architectures. Microprocessors and Microsystems, pp. 85, 2021, doi: 10.1016/j.micpro.2021.104299.
K. Ignat, “The role of artificial intelligence in business transformation: A case of pharmaceutical companies,” Technology in Society, pp. 66, 2021, doi: 10.1016/j.techsoc.2021.101629.
S. David, P. Vinit, P. Maximilian, et al., “How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops,” Journal of Business Research, vol. 134, pp. 574-587, 2021, doi: 10.1016/j.jbusres.2021.05.009.
B. Scout, “M., Karen M., A,” J. G., et al. The future of bone regeneration: integrating AI into tissue engineering. Biomedical Physics & Engineering Express, pp. 7(5), 2021, doi: 10.1088/2057-1976/ac154f.
K. Edward and B. Mariana, “G., Saad M,” K., et al. Clinician-driven artificial intelligence in ophthalmology: resources enabling democratization. Current Opinion in Ophthalmology, 2021, doi: 10.1097/ICU.0000000000000785.
V. Nizam and A. Aslekar, “Challenges of Applying AI in Healthcare in India,” Journal of Pharmaceutical Research International, pp. 203-209, 2021, doi: 10.9734/jpri/2021/v33i36B31969.
M, “T,” W., Pietro M., Amin M., et al. Surgical data science and artificial intelligence for surgical education. Journal of Surgical Oncology, vol. 124, no. 2, pp. 221-230, 2021, doi: 10.1002/jso.26496.
H. David, “AI-guided combat drone swarm used in Gaza attacks,” New Scientist, vol. 250, no. 3342, pp. 17-17, 2021, doi: 10.1016/S0262-4079(21)01178-7.
K. Costa, “B,” V. E. Promoting Trustworthy AI in Government. Nextgov.com (Online), 2021.