Research on the Construction and Application of Planning Intelligent Agent Model for Personalized Ideological and Political Learning Path Generation of College Students from the Perspective of Ideological and Political Education
Main Article Content
Abstract
High-quality ideological and political education in universities requires a shift from homogeneous supply and standardized promotion to personalized and precise learning support. Planning intelligent agents, with autonomous perception, dynamic planning, and closed-loop optimization capabilities, are well aligned with the generation of individualized ideological and political learning paths for college students. Based on ideological and political education theory, constructivist learning theory, and intelligent agent theory, this study defines personalized ideological and political learning paths and builds a four-layer planning agent model consisting of perception, decision, execution, and feedback layers, supported by ideological guidance, data security, and user interaction modules. The model collects multidimensional learner data, constructs dynamic learner portraits, generates differentiated learning paths, matches ideological and political resources, evaluates learning effects, and optimizes paths through feedback. Empirical testing shows significant improvement in knowledge mastery, ability development, value internalization, and learning behavior. The model also provides a reference for secure wireless campus platforms, intelligent data transmission, and electromagnetic-compatible learning environments.
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
Z. Wang, “Using Deep Neural Networks to Evaluate Ideological and Political Learning in Educational Environments,” International Journal of High Speed Electronics and Systems, vol. 35, no. 02, 2025, doi: 10.1142/S0129156425405649.
S. Yihan and S. Xiaoxi, “Analysis of Undergraduates’ Learning Gains of Curriculum Ideology and Politics in General Elective Courses and Their Influencing Factors: Based on the Mixed Research of 11 Public Universities in Yunnan Province,” Frontiers in Educational Research, vol. 7, no. 10, 2024, doi: 10.25236/FER.2024.071025.
L. Songrui, L. Jinzhu, and K.Mahmoud, “Statistical Model of College Ideological and Political Learning Based on Fractional Differential Equations,” Applied Mathematics and Nonlinear Sciences, vol. 8, no. 1, pp. 1087-1096, 2022, doi: 10.2478/AMNS.2022.2.0093.
P. Wei, T. Qianni, C. Aiping, et al., “University Ideological and Political Learning Model Based on Statistical Memory Curve Mathematical Equation,” Applied Mathematics and Nonlinear Sciences, vol. 8, no. 1, pp. 1117-1124, 2022, doi: 10.2478/AMNS.2022.2.0098.
Y. Yang and Q. Li, “Artificial Intelligence Technology and Ideological Political Education: Navigating Employment Challenges and Opportunities for College Graduates,” International Journal of Cognitive Informatics and Natural Intelligence, vol. 19, no. 1, pp. 1-21, 2025, doi: 10.4018/IJCINI.385726.
L. Dong and W. Hui, “Study on the ideological and behavioral characteristics and influencing factors of contemporary college students,” Advances in Social Behavior Research, vol. 15, no. 1, 2025, doi: 10.54254/2753-7102/2025.20515.
Zhang, Baojing, Velmayil, et al., “A deep learning model for innovative evaluation of ideological and political learning,” Progress in Artificial Intelligence, no. prepublish, pp. 1-13, 2021, doi: 10.1007/S13748-021-00253-3.
S. Chen L, “Application of Information Technology and Printing Materials in Ideological and Political Learning,” Advanced Materials Research, vol. 951, pp. 181-184, 2014, doi: 10.4028/www.scientific.net/AMR.951.181.
J. J. Xia, F. Guo, J. X. Wang, et al., “Study on Ideological and Political Learning and Employment Ability in Manufacturing Engineering,” Applied Mechanics and Materials, vol. 484-485, pp. 286-288, 2014, doi: 10.4028/www.scientific.net/AMM.484-485.286.
H. Reshad M Z, S. Zaini, and N. Yusof A, “Content Analysis of Human Anatomy Mobile Apps for Undergraduate Student Learning using MARuL,” Medical Science Educator, no. prepublish, pp. 1-10, 2026, doi: 10.1007/S40670-026-02649-Z.
D. Alwawi A, H. Madi, S. Dahab A N M, et al., “Beyond learning preferences: exploring the relationship between learning styles and sensory processing among university students,” BMC medical education, 2026, doi: 10.1186/S12909-026-08655-4.
“Bulletin,” vol. 52, no. Supplement_1, pp. S188-S189, 2026.
X. Wu, X. Wu, W. Hu, et al., “The Impact of 3D Interactive Prompts on College Students’ Learning Outcomes in Desktop Virtual Learning Environments: A Study Based on Eye-Tracking Experiments,” Journal of eye movement research, vol. 19, no. 1, 2026, doi: 10.3390/JEMR19010019.
M. Addin S A and S. Shehabi A M, “A Qualitative Analysis of Motivational Factors and Learning Challenges Impacting Proficiency Among Yemeni Female EFL Undergraduates at Thamar University,” Theory and Practice in Language Studies, vol. 16, no. 2, pp. 516-526, 2026, doi: 10.17507/TPLS.1602.18.
J. Chen and C. Dong, “An empirical study on the impact of artificial intelligence tools on college students’ learning behaviors: An analysis based on the technology acceptance model,” Resources Data Journal, vol. 5, pp. 31-67, 2026, doi: 10.50908/RDJ.5.0_31.
T. Løken K, N. Hanssen B, K. Olsen, et al., “Norwegian validation of the University Student Engagement Inventory,” Frontiers in Psychology, Art. no. 1734409, 2026, doi: 10.3389/FPSYG.2026.1734409.
C. Galarza R, D. Guerra D D, M. Lugo H C L D, et al., “Integrating executive functions: Exploring inter relations and implications for learning in university students,” Applied neuropsychology. Adult, pp. 1-9, 2026, doi: 10.1080/23279095.2026.2614390.
J. Zeng, D. Fan, H. Hu, et al., “Research on the influence of personalized principles in AR educational resources on the learning effectiveness of college students,” Frontiers in Psychology, Art. no. 1618990, 2026, doi: 10.3389/FPSYG.2025.1618990.
H. Chen, Z. Peng, H. Ma, et al., “Development and Validation of Learning Behaviour Scale for University Students in Blended Learning: A Technology Acceptance Model Perspective,” European Journal of Education, vol. 61, no. 1, Art. no. e70406, 2026, doi: 10.1111/EJED.70406.
D. Majauskiene, N. Istomina, D. Valanciene, et al., “Depression and self-esteem as key factors related to academic motivation in university students,” BMC public health, vol. 26, no. 1, pp. 513-513, 2026, doi: 10.1186/S12889-025-26172-9.
H. Shen, X. Ye, C. Bai, et al., “The impact of peer relationships and employment pressure on the learning wellbeing of university students in the post-2000s generation,” Frontiers in Psychology, pp. 161688537-1688537, 2026, doi: 10.3389/FPSYG.2025.1688537.
M. Huang, S. Wang, W. Yao, et al., “The impact of digital teaching on learning burnout in college students: the moderating role of self-efficacy and the mediating effect of learning adaptability,” BMC medical education, vol. 26, no. 1, pp. 194-194, 2026, doi: 10.1186/S12909-025-08330-0.
K. Silva D, P. Cruz A J, N. Santos C F, et al., “Speech speed in audiovisual resources in the learning process of university students: a scoping review,” CoDAS, vol. 38, no. 1, Art. no. e20240326, 2026, doi: 10.1590/2317-1782/E20240326EN.
S. Guo and W. Huang, “Research on the path of improving undergraduate students’ learning adaptability by empowering college archives resources from the perspective of new media,” Probe - Media and Communication Studies, vol. 7, no. 4, 2025, doi: 10.59429/PMCS.V7I4.12786.
Y. Ding, “Research on Reshaping College Students’ Learning Motivation and Teaching Pathways in the AI Era-Taking Python Course as an Example,” Advances in Educational Technology and Psychology, vol. 9, no. 6, 2025, doi: 10.23977/AETP.2025.090615.
C. Liu, “Pathways and Influencing Factors of Motivational Changes among Chinese University Students Majoring in Japanese,” Journal of Education and Educational Research, vol. 16, no. 3, pp. 19-24, 2025, doi: 10.54097/CNM5BV89.
R. Ma and L. Xiao, “Application of deep learning to the development of a prediction model for college students’ learning outcomes,” Discover Artificial Intelligence, vol. 6, no. 1, pp. 33-33, 2025, doi: 10.1007/S44163-025-00607-Z.
M. Spengler and N. Kelp, “Examination of undergraduate learning assistants’ science communication and science teaching approaches,” Journal of microbiology & biology education, Art. no. e0025425, 2025, doi: 10.1128/JMBE.00254-25.
W. Yang, Z. Lu, Z. Li, et al., “The impact of human-AIGC tools collaboration on the learning effect of college students: a key factor for future education?” Kybernetes, vol. 54, no. 15, pp. 7746-7763, 2025, doi: 10.1108/K-03-2024-0613.