Research on Quality Evaluation of Ideological and Political Education Resources Based on Artificial Intelligence Technology
Main Article Content
Abstract
The rapid expansion of digital ideological and political education resources has increased resource quantity and diversity, but has also produced problems such as value-orientation deviation, content homogenization, loose theoretical logic, uneven quality, and inconsistent manual evaluation. To meet the normalized quality evaluation needs of large-scale digital resources, this study proposes an artificial-intelligence-enabled evaluation framework. Large language model semantic analysis, deep learning feature extraction, analytic hierarchy process, and fuzzy comprehensive evaluation are integrated to build a multi-level indicator system covering political orientation, content quality, teaching adaptation, technical standards, communication experience, and compliance-homogenization control. The model supports intelligent collection, semantic mining, indicator scoring, comprehensive diagnosis, classification, and precise resource recommendation. It can improve evaluation efficiency while retaining expert-derived political and educational standards through procedural consistency. In engineering-supported education environments, the framework is also relevant to wireless learning platforms, electromagnetic information infrastructures, and secure data transmission systems that deliver ideological and political resources across campuses. The study provides methodological support for the quality governance, optimization, and standardized dissemination of digital educational resources.
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
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, pp. 115104-115104, 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, pp. 115401-115401, 2021, doi: 10.1016/j.eswa.2021.115401.
L. Lai and M. He, “Research on the Evaluation of the Effectiveness of Ideological and Political Education in Higher Vocational Courses Based on CMOEA-TD Algorithm,” International Journal of Cognitive Informatics & Natural Intelligence, pp. 20(1), 2026, doi: 10.4018/IJCINI.397323.
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.
Y. Wang and J. Zhang, “Research on the Quality Evaluation and Optimization of Ideological and Political Education in Universities Driven by Artificial Intelligence,” Communications in Computer and Information Science, pp. 309-318, 2024, doi: 10.1007/978-981-97-4396-4_29.
W. Zhang, “The quality evaluation and development of university ideological and political teaching based on wireless network artificial intelligence,” Journal of Combinatorial Mathematics and Combinatorial Computing, vol. 125, pp. 11, 2024, doi: 10.61091/jcmcc125-13.
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.
K. Shi, “An efficient Model for Satisfaction Evaluation of College Students Online Ideological and Political Education with Single-Valued Neutrosophic Numbers,” Neutrosophic Sets & Systems, vol. 76, 2025.
J. Tang, “The Implementation Path and Effect Evaluation of Curriculum Ideological and Political Education in Professional Courses in Higher Education Institutions,” Journal of Contemporary Educational Research, vol. 9, no. 2, pp. 32-38, 2025.
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.
S. Fang and J. He, “Analysis of the Path of Artificial Intelligence Ideological and Political Education Supported by the Internet of Things and Big Data,” International Journal of High Speed Electronics & Systems, pp. 35(2), 2026, doi: 10.1142/S0129156425500028.
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.