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<p>The advancement of the high-quality development of the blended teaching of ideological and political courses in colleges and universities, as well as the thorough integration of artificial intelligence with this teaching approach, are imperative demands of the modern era. In order to analyze the specific applications of artificial intelligence technology in the teaching of ideological and political courses in colleges and universities, this paper first establishes the general framework for their application. It then proceeds, module by module, from the individual courses and examines how they are taught. Data mining techniques are used to extract the characteristics of the ideological and political course teaching resources, determine the similarity between learners, and complete the personalized intelligent recommendation of ideological and political course teaching resources in the personalized ideological and political course learning module. The ideological and political course teaching resources recommendation model is constructed through artificial intelligence technology. The text responses from the students are processed using natural language technology, and the learning effect is predicted using a logistic regression model. To build the BF-BKT knowledge tracking model, which tracks student feedback during the learning process of ideological and political courses, incorporating behavioral and forgetting elements. The purpose of the teaching framework is to examine the results of combining political and ideological courses with AI. The results indicate that the student’s critical thinking score is 46.6245 after incorporating artificial intelligence, which is roughly 6 points higher than the traditional technique. Following the use of AI fusion, the students’ three viewpoints and their cognition of family and national sentiment improved, with increases of 4.9 and 4.7 points, respectively, in the pre-and post-tests. It is evident that the integration of political and ideological courses at the university with artificial intelligence aids in students’ formation of their three points of view and emotional development, ultimately leading to cognitive characterization.</p>
</abstract>

The advancement of the high-quality development of the blended teaching of ideological and political courses in colleges and universities, as well as the thorough integration of artificial intelligence with this teaching approach, are imperative demands of the modern era. In order to analyze the specific applications of artificial intelligence technology in the teaching of ideological and political courses in colleges and universities, this paper first establishes the general framework for their application. It then proceeds, module by module, from the individual courses and examines how they are taught. Data mining techniques are used to extract the characteristics of the ideological and political course teaching resources, determine the similarity between learners, and complete the personalized intelligent recommendation of ideological and political course teaching resources in the personalized ideological and political course learning module. The ideological and political course teaching resources recommendation model is constructed through artificial intelligence technology. The text responses from the students are processed using natural language technology, and the learning effect is predicted using a logistic regression model. To build the BF-BKT knowledge tracking model, which tracks student feedback during the learning process of ideological and political courses, incorporating behavioral and forgetting elements. The purpose of the teaching framework is to examine the results of combining political and ideological courses with AI. The results indicate that the student’s critical thinking score is 46.6245 after incorporating artificial intelligence, which is roughly 6 points higher than the traditional technique. Following the use of AI fusion, the students’ three viewpoints and their cognition of family and national sentiment improved, with increases of 4.9 and 4.7 points, respectively, in the pre-and post-tests. It is evident that the integration of political and ideological courses at the university with artificial intelligence aids in students’ formation of their three points of view and emotional development, ultimately leading to cognitive characterization.

Exploration on the Integration of Artificial Intelligence and Ideological and Political Teaching in Colleges and Universities

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The advancement of the high-quality development of the blended teaching of ideological and political courses in colleges and universities, as well as the...

Exploration on the Integration of Artificial Intelligence and Ideological and Political Teaching in Colleges and Universities

Publié en ligne: 05 août 2024

Pages: -

Reçu: 17 avr. 2024

Accepté: 01 juil. 2024

DOI: https://doi.org/10.2478/amns-2024-2038

Mots clés
Artificial intelligence technology, Data mining, Natural language, Logistic regression, BF-BKT knowledge tracking, Ideological and political course teaching

© 2024 Jing Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploration on the Integration of Artificial Intelligence and Ideological and Political Teaching in Colleges and Universities

Publié en ligne: 05 août 2024

Pages: -

Reçu: 17 avr. 2024

Accepté: 01 juil. 2024

DOI: https://doi.org/10.2478/amns-2024-2038

Mots clésArtificial intelligence technology, Data mining, Natural language, Logistic regression, BF-BKT knowledge tracking, Ideological and political course teaching

© 2024 Jing Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Artificial intelligence technology, Data mining, Natural language, Logistic regression, BF-BKT knowledge tracking, Ideological and political course teaching