全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

人工智能时代高校思想政治教育精准化干预的动态响应机制研究
Research on the Dynamic Response Mechanism of Precise Intervention in Ideological and Political Education in Colleges and Universities in the Era of Artificial Intelligence

DOI: 10.12677/ass.2026.151025, PP. 201-209

Keywords: 人工智能(AI)技术,思想政治教育,精准化干预,动态响应机制
Artificial Intelligence (AI) Technology
, Ideological and Political Education, Precise Intervention, Dynamic Response Mechanism

Full-Text   Cite this paper   Add to My Lib

Abstract:

人工智能(AI)技术的飞速发展,导致高校思政教育工作机遇与挑战并存。高校思政教育必须主动探索与AI技术的深度融合,推动思政教育从“经验驱动”向“数据驱动”、从“群体覆盖”向“个体精准”转型。为此,推进人工智能时代高校思政教育精准化干预的动态响应机制建设,需要探索其理论内涵、技术架构、关键算法、潜在挑战、应对策略、效果评估、优化机制、伦理风险与隐私保护等问题,以期构建从实时感知到闭环反馈的智能化生态,显著提升思政教育的个性化、即时性和科学性。
The rapid development of artificial intelligence (AI) technology has led to the coexistence of opportunities and challenges in ideological and political education in colleges and universities. Ideological and political education in colleges and universities must take the initiative to explore the deep integration with AI technology, and promote the transformation of ideological and political education from “experience-driven” to “data-driven”, and from “group coverage” to “individual precision”. Therefore, to promote the construction of a dynamic response mechanism for precise intervention in ideological and political education in colleges and universities in the era of artificial intelligence, it is necessary to explore its theoretical connotation, technical architecture, key algorithms, potential challenges, response strategies, effect evaluation, optimization mechanisms, ethical risks and privacy protection, in order to build an intelligent ecology from real-time perception to closed-loop feedback, and significantly improve the personalization, immediacy and scientific nature of ideological and political education.

References

[1]  丁凯, 宋林泽. 论高校思想政治教育精准化的机理及实现路径[J]. 思想理论教育, 2020(6): 101-105.
[2]  西安电子科技大学: AI赋能 + 多元协同, 构建“一站式”学生社区心理健康智慧防线[EB/OL].
https://yurenhao1.sizhengwang.cn/a/tjbz/250919/2222814.shtml, 2025-09-19.
[3]  张忠怡. 生成式人工智能赋能高校思想政治教育的运行逻辑与实践路径[J]. 社会科学前沿, 2025, 14(3): 347-353.
[4]  张家惠, 丁敬达. 基于BERTopic和LSTM模型的新兴主题预测研究[J]. 情报科学, 2025, 43(1): 98-105.
[5]  网信办, 学工部. 我校一项目入选教育部首批“人工智能 + 高等教育”应用场景典型案例[EB/OL]. 华中科技大学新闻网.
https://news.hust.edu.cn/info/1002/51914.htm, 2024-04-19.
[6]  成都理工大学: AI+知识图谱赋能“形势与政策”课程改革创新[EB/OL].
https://sichuan.eol.cn/scgd/202505/t20250513_2668016.shtml, 2025-05-13.
[7]  裴雷, 詹希旎, 陈述. 迈向智适应治理: AIGC 背景下的内容生态安全挑战与治理模式[J]. 图书与情报, 2025(5): 1-11.
[8]  盘大清, 李白杨, 任尚升. 人工智能生成内容(AIGC)深度伪造信息风险管理能力评估方法研究[J]. 图书与情报, 2025(5): 12-23.
[9]  陈述, 李白杨, 詹希旎. 数字蝶变环境下生成式人工智能风险点位识别与阶段治理研究[J]. 图书与情报, 2025(5): 24-33.
[10]  童云峰. 走出科林格里奇困境: 生成式人工智能技术的动态规制[J]. 上海交通大学学报(哲学社会科学版), 2024, 32(8): 53-67.
[11]  叶明, 王岩. 人工智能时代数据孤岛破解法律制度研究[J]. 大连理工大学学报(社会科学版), 2019, 40(5): 69-77.
[12]  宋凡, 龚向和. 替代还是赋能: 人工智能教学对教师教学权的冲击及其应对[J]. 中国远程教育, 2024, 44(4): 15-27.
[13]  张杰, 樊改霞. 人工智能时代的教师角色再定位[EB/OL]. 中国社会科学网.
https://www.cssn.cn/skgz/bwyc/202503/t20250310_5856610.shtml, 2025-03-10.
[14]  科技部. 新一代人工智能伦理规范[EB/OL].
https://www.most.gov.cn/kjbgz/202109/t20210926_177063.html?ref=salesforce-research, 2021-09-26.
[15]  杨强. AI与数据隐私保护: 联邦学习的破解之道[J]. 信息安全研究, 2019, 5(11): 961-965.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133