Diffusion Model-Assisted Interactive Scene Generation and Student Participation Prediction for Ideological and Political Education in Textile Majors
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Abstract
To address the insufficient integration between textile engineering knowledge and ideological–political education, as well as the lack of real-time assessment of student engagement, this study proposes a diffusion model-assisted framework for interactive scene generation and multimodal participation prediction. A dual-encoder diffusion architecture driven by a Textile Knowledge Graph (TKG) is developed to establish semantic associations between textile process parameters and ethical objectives, enabling the automatic generation of process-aware educational scenarios. Eye-tracking and electromyography signals are jointly modeled using a Long Short-Term Memory network to predict participation status, while a dynamic feedback mechanism adaptively adjusts scene complexity according to learners’ cognitive responses. Considering the growing adoption of intelligent sensing and distributed information acquisition technologies in digital education environments, the proposed framework provides a scalable solution for multimodal data fusion and adaptive interaction, offering potential methodological references for future wireless sensing and smart educational infrastructures. Experimental results demonstrate that the proposed method achieves a process parameter trigger rate of 82.1%, an ethical scene coverage of 93.2%, classification accuracies exceeding 85% for highly engaged students, and interaction latency below 50 ms under low-complexity conditions. The framework effectively enhances scene authenticity, participation prediction accuracy, and adaptive instructional capability, providing a reliable technical foundation for intelligent textile education and interdisciplinary human–machine interaction systems.
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References
X. F. Lin, Z. Wang, W. Zhou, G. Luo, G. J. Hwang, Y. Zhou, et al., “Technological support to foster students’ artificial intelligence ethics: An augmented reality-based contextualized dilemma discussion approach,” Computers & Education, vol. 201, pp. 104813, 2023, doi: 10.1016/j.compedu.2023.104813.
A. Ferdman and E. Ratti, “What Do We Teach to Engineering Students: Embedded Ethics, Morality, and Politics,” Science and Engineering Ethics, vol. 30, no. 1, pp. 7, 2024, doi: 10.1007/s11948-024-00469-1.
S. Wang, “Status and Countermeasures Analysis of Online Ideological and Political Education for Higher Vocational College Students,” Advances in Vocational and Technical Education, vol. 6, no. 3, pp. 87-93, 2024, doi: 10.23977/avte.2024.060313.
S. Luo, “Construction of situational teaching mode in ideological and political classroom based on digital twin technology,” Computers and Electrical Engineering, vol. 101, pp. 108104, 2022, doi: 10.1016/j.compeleceng.2022.108104.
Y. Chen, “Research on the Mode Construction and Realization Path of Ideological and Political Education Scene Communication in Colleges and Universities in the Digital Era,” Journalism and Communications, vol. 12, no. 4, pp. 1001-1006, 2024, doi: 10.12677/jc.2024.124155.
R. Yang, P. Srivastava, and S. Mandt, “Diffusion Probabilistic Modeling for Video Generation,” Entropy, vol. 25, no. 10, pp. 1469, 2023, doi: 10.3390/e25101469.
H. Yu, C. Wang, P. Zhuang, W. Menapace, A. Siarohin, J. Cao, et al., “4Real: towards photorealistic 4D scene generation via video diffusion models,” Proceedings of the 38th International Conference on Neural Information Processing Systems (NIPS ′24); 10-15 December 2024; Vancouver, BC, Canada. Red Hook, NY, USA: Curran Associates Inc.; 2025. Vol. 37, Article 1438, pp. 45256-45280, doi: 10.52202/079017-1438.
B. Li, C. Zheng, W. Zhu, J. Mai, B. Zhang, P. Wonka, et al., “Vivid-ZOO: Multi-View Video Generation with Diffusion Model,” Advances in Neural Information Processing Systems, vol. 37, pp. 62189-62222, 2024, doi: 10.52202/079017-1987.
L. Lu and M. Li, “Development of a virtual interactive system for Dahua Lou loom based on knowledge ontology-driven technology,” Heritage Science, vol. 11, no. 1, pp. 178, 2023, doi: 10.1186/s40494-023-01027-x.
Y. Zhang and C. Liu, “Unlocking the Potential of Artificial Intelligence in Fashion Design and E-Commerce Applications: The Case of Midjourney,” Journal of Theoretical and Applied Electronic Commerce Research, vol. 19, no. 1, pp. 654-670, 2024, doi: 10.3390/jtaer19010035.
H. Chefer, Y. Alaluf, Y. Vinker, L. Wolf, and D. Cohen-Or, “Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models,” ACM transactions on Graphics (TOG), vol. 42, no. 4, pp. 1-10, 2023, doi: 10.1145/3592116.
F. A. Croitoru, V. Hondru, R. T. Ionescu, and M. Shah, “Diffusion Models in Vision: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 9, pp. 10850-10869, 2023, doi: 10.1109/TPAMI.2023.3261988.
Z. Zhou, D. Chen, C. Wang, C. Chen, and S. Lyu, “Simple and fast distillation of diffusion models,” Proceedings of the 38th International Conference on Neural Information Processing Systems (NIPS ′24), 10-15 December 2024; Vancouver, BC, Canada. Red Hook, NY, USA: Curran Associates Inc.; 2025. Vol. 37, Article 1291, pp. 40831-40860, doi: 10.52202/079017-1291.
A. D. Vuong, M. N. Vu, T. Nguyen, B. Huang, D. Nguyen, T. Vo, et al., “Language-driven scene synthesis using multi-conditional diffusion model,” Proceedings of the 37th International Conference on Neural Information Processing Systems (NIPS ′23); 10-16 December 2023; New Orleans, LA, USA. Red Hook, NY, USA: Curran Associates Inc.; 2023. Article 1346, pp. 30862-30884, [Online]. Available: https://livrepository.liverpool.ac.uk/id/eprint/3177439.
L. Yang, Z. Zhang, Y. Song, S. Hong, R. Xu, Y. Zhao, et al., “Diffusion Models: A Comprehensive Survey of Methods and Applications,” ACM Computing Surveys, vol. 56, no. 4, pp. 1-39, 2023, doi: 10.1145/3626235.
C. Tang, Z. Xu, E. Occhipinti, W. Yi, M. Xu, S. Kumar, et al., “From brain to movement: Wearables-based motion intention prediction across the human nervous system,” Nano Energy, vol. 115, pp. 108712, 2023, doi: 10.1016/j.nanoen.2023.108712.
Z. Zhang, B. Li, X. Nie, C. Han, T. Guo, and L. Liu, “Towards consistent video editing with text-to-image diffusion models,” Proceedings of the 37th International Conference on Neural Information Processing Systems (NIPS ′23); 10-16 December 2023; New Orleans, LA, USA. Red Hook, NY, USA: Curran Associates Inc.; 2023. Article 2550, pp. 58508-58519.
Y. Wang, Y. Li, X. Zhang, X. Liu, A. Dai, A. B. Chan, et al., “Edit Temporal-Consistent Videos with Image Diffusion Model,” ACM Transactions on Multimedia Computing, Communications and Applications, vol. 20, no. 12, pp. 1-16, 2024, doi: 10.1145/3691344. X. Zhao