Quantitative Analysis of Influencing Factors on AI-Integrated Teaching in Animation Major of Private Colleges and Universities

Dandan Cong

doi:10.54097/ry3mef85

Authors

Dandan Cong

DOI:

https://doi.org/10.54097/ry3mef85

Keywords:

Private Colleges and Universities, Animation Major, AI-Integrated Teaching, Influencing Factors, Structural Equation Modeling (SEM), University-Enterprise Cooperation

Abstract

Amid global AI education transformation, Chinese private animation majors have low AI teaching implementation and insufficient quantitative research. This study (5 Liaoning private colleges, 312 samples) used SEM/Bootstrap/group regression based on TAM-UTAUT. Results: Faculty (β=0.42, core), technical infrastructure (β=0.32), student characteristics (β=0.21) positively impact effectiveness; university-enterprise cooperation mediates (32.6%); high hardware raises faculty’s impact (β=0.28→0.51); juniors use independent learning (β=0.32) to alleviate constraints. This fills China’s research gap, providing references for global resource-constrained colleges.

Downloads

Download data is not yet available.

References

[1] European Commission. AI for Education: A European Policy Framework [R]. Brussels: European Commission Publications Office, 2023.

[2] National Science Foundation. National AI R&D Strategic Plan: 2024 Update [R]. Washington, DC: NSF, 2024.

[3] LI M, ZHANG H. Research on the reform path of art design education in private colleges and universities in the AI era [J]. Research in Educational Development, 2024, 44(5): 78-85.

[4] VENKATESH V, NEVES C, OLIVEIRA T, CRUZ-JESUS F. Extending the unified theory of acceptance and use of technology for sustainable technologies context [J]. International Journal of Information Management, 2025, 80: 102838.

[5] LI H J, ZHANG F. An empirical study on the acceptance model of college students' mobile learning based on UTAUT model [J]. Information Science, 2017, (6): 98-103.

[6] ZHAO Y, CHEN J. Advanced application of structural equation modeling in educational quantitative research [J]. Psychological Development and Education, 2025, 41(2): 256-263.

[7] BROWN J, WILSON S. Factors influencing AI integration in animation education: Evidence from UK private colleges [J]. Computers & Education, 2024, 198: 105432.

[8] WHITE A, et al. Industry-university collaboration in AI animation training: Evidence from California [J]. Animation Studies, 2024, 18: 67-89.

[9] SUN Y, LI G. Application standards and case analysis of Bootstrap method in educational research [J]. Journal of Educational Studies, 2024, 20(1): 79-88.

[10] SMITH L, et al. AI-assisted creativity in art schools: A cross-national study of 12 countries [J]. Journal of Educational Technology & Society, 2023, 26(2): 45-62.