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人工智能-智能化信息技术融入高校思想政治教育的探索

Exploration on College Ideological and Political Education Integrating Artificial Intelligence-Intellectualized Information Technology.

机构信息

Faculty of Chemistry and Material Science, Langfang Normal University, Langfang 065000, Hebei, China.

Party School of the Langfang Municipal Committee of C.P.C, Langfang 065000, Hebei, China.

出版信息

Comput Intell Neurosci. 2022 May 18;2022:4844565. doi: 10.1155/2022/4844565. eCollection 2022.

DOI:10.1155/2022/4844565
PMID:35634053
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9132633/
Abstract

In recent years, with the vigorous development and application of Artificial Intelligence (AI), the application of AI in education is becoming more and more extensive. This study makes a theoretical analysis of AI-Intellectualized Information Technology (IT). Discrete Cosine Transform (DCT)-Based Speech Recognition (SR) and Genetic Algorithm (GA)-Based Image Recognition (IR) are used to analyze the College Ideological and Political Education (IAPE). The research findings prove that the advantages of integrating AI-intellectualized IT on College IAPE outweigh the disadvantages. The improvement of technological development, which accounts for 71.17% of undergraduate gains, is the most significant, and the smallest gain is technology coverage, which is 36.80%. Overall, 57.21% are interested in new technology, and the students' enthusiasm accounts for 30.77%. Most of the students focus on the innovation performance of technology, accounting for 75.92%. With an average influence of 89.04% on undergraduates, technology has the largest impact, followed by 85.78% on students with masters or higher degrees. The largest impact of diversified teaching methods for all students is 62.48%. This study provides some reference values for AI-intellectualized IT research and analysis, as well as students' IAPE.

摘要

近年来,随着人工智能(AI)的蓬勃发展和应用,人工智能在教育中的应用越来越广泛。本研究对 AI-智能化信息技术(IT)进行了理论分析。使用离散余弦变换(DCT)的语音识别(SR)和遗传算法(GA)的图像识别(IR)来分析高校思想政治教育(IAPE)。研究结果证明,将 AI-智能化 IT 整合到高校 IAPE 中的优势大于劣势。技术发展的提高,占本科生收益的 71.17%,是最显著的,而技术覆盖范围的收益最小,为 36.80%。总的来说,57.21%的学生对新技术感兴趣,学生的积极性占 30.77%。大多数学生关注技术的创新表现,占 75.92%。技术对本科生的平均影响为 89.04%,影响最大,其次是对硕士或以上学历学生的影响,为 85.78%。对所有学生而言,多元化教学方法的最大影响是 62.48%。本研究为 AI-智能化 IT 研究和分析以及学生的 IAPE 提供了一些参考价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc9/9132633/d51494f28b87/CIN2022-4844565.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc9/9132633/5ec4e8d52310/CIN2022-4844565.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc9/9132633/7565fdb7cb88/CIN2022-4844565.008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc9/9132633/d51494f28b87/CIN2022-4844565.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc9/9132633/5ec4e8d52310/CIN2022-4844565.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc9/9132633/629e6b115586/CIN2022-4844565.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc9/9132633/e116cc08c322/CIN2022-4844565.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc9/9132633/63c4ab2806d3/CIN2022-4844565.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc9/9132633/c30d8ae02f44/CIN2022-4844565.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc9/9132633/e29838cd5ee9/CIN2022-4844565.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc9/9132633/3eac0a5a3f6a/CIN2022-4844565.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc9/9132633/7565fdb7cb88/CIN2022-4844565.008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcc9/9132633/d51494f28b87/CIN2022-4844565.010.jpg

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