AI-Driven Animation untuk Pemuda Non-Ahli:Pelatihan Menuju Dunia Kreatif
Keywords:
AI, AI-Driven Animation, Technology Acceptance Model, pemuda non-ahli, Industri kreatifAbstract
Kegiatan Pengabdian kepada Masyarakat ini mengevaluasi efektivitas pelatihan AI-Driven Animation bagi pemuda non-ahli dalam meningkatkan persepsi kemudahan penggunaan (Perceived Ease of Use/PEU), persepsi manfaat (Perceived Usefulness/PU), dan niat penggunaan berkelanjutan (Behavioral Intention/BI) berdasarkan kerangka Technology Acceptance Model (TAM). Menggundesain pre-test dan post-test, data dikumpulkan melalui kuesioner berbasis skala Likert. Hasil menunjukkan peningkatan signifikan pada rata-rata PEU, PU, dan BI. Uji-t berpasangan menghasilkan nilai t tinggi dengan p-value <0,001 untuk semua konstruk, menandakan perubahan yang signifikan secara statistik. Analisis korelasi Pearson mengindikasikan hubungan sangat kuat antara PEU dan PU, serta hubungan kuat antara PU dan BI, dan PEU dan BI. Temuan ini konsisten dengan asumsi TAM, bahwa persepsi kemudahan penggunaan berkontribusi positif terhadap persepsi manfaat, yang pada gilirannya meningkatkan niat penggunaan berkelanjutan. Secara keseluruhan, pelatihan AI-Driven Animation terbukti efektif dalam memberdaypemuda non-ahli, mempercepat adopsi teknologi kreatif, dan mendorong keberlanjutan penggunaan AI dalam industri kreatif digital. Hasil ini memperkuat relevansi TAM sebagai kerangka konseptual dalam memahami faktor-faktor yang memengaruhi penerimaan dan adopsi teknologi baru di kalangan generasi muda.
References
Abootorabi, M. M., Ghahroodi, O., Zahraei, P. S., Behzadasl, H., Mirrokni, A., Salimipanah, M., ... & Asgari,E. (2025). Generative AI for Character Animation: A Comprehensive Survey of Techniques, Applications, and Future Directions. arXiv:2504.19056v1 [cs.CV]. https://doi.org/10.48550/arXiv.2504.19056.
Cavazza, M., Charles, F., & Mead, S. J. (2001). AI-based animation for interactive storytelling," Proceedings Computer Animation 2001. Fourteenth Conference on Computer Animation (Cat. No.01TH8596),Seoul, Korea (South), 2001, pp. 113-120. https://doi.org/10.1109/CA.2001.982384.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Epstein, Z., Hertzmann, A., Investigators of Human Creativity, Akten, M., Farid, H., Fjeld, J., ... & Smith, A.(2023). Art and the science of generative AI. Science, 380(6650), 1110-1111. https://doi.org/10.1126/science.adh4451.
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative ai. Business & Information Systems Engineering, 66(1), 111-126. https://doi.org/10.1007/s12599-023-00834-7.
Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of information technology case and application research, 25(3), 277-304. https://doi.org/10.1080/15228053.2023.2233814
Ghimire, A., & Edwards, J. (2024). Generative AI adoption in classroom in context of technology acceptance model (TAM) and the innovation diffusion theory (IDT). arXiv:2406.15360v1 [cs.CY]. https://doi.org/10.48550/arXiv.2406.15360
Gupta, V., & Yang, H. (2024). Generative artificial intelligence (AI) technology adoption model for entrepreneurs: case of ChatGPT. Internet Reference Services Quarterly, 28(2), 223-242.https://doi.org/10.1080/10875301.2023.2300114.
Kolb, D. A. (1984). Experiential Learning: Experience as the Source of Learning and Development. Prentice Hall.
Mogaji, E., Viglia, G., Srivastava, P., & Dwivedi, Y. K. (2024). Is it the end of the technology acceptance model in the era of generative artificial intelligence?. International Journal of Contemporary Hospitality Management, 36(10), 3324-3339. https://doi.org/10.1108/IJCHM-08-2023-1271.
Shahzad, M. F., Xu, S., & Asif, M. (2025). Factors affecting generative artificial intelligence, such as ChatGPT, use in higher education: An application of technology acceptance model. British Educational Research Journal, 51(2), 489-513. https://doi.org/10.1002/berj.4084.
Sharma, H., & Juyal, A. (2023). Future of Animation with Artificial Intelligence. ShodhKosh: Journal of Visual and Performing Arts, 4(2SE), pp. 180–187. https://doi.org/10.29121/shodhkosh.v4.i2SE.2023.559.
Singh, A., Future of Animated Narrative and the Effects of Ai on Conventional Animation Techniques.(2023). 7th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), Bangalore, India, pp. 1-4.https://doi.org/10.1109/CSITSS60515.2023.10334104.
UNESCO. (2021). Reimagining our futures together: A new social contract for education. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000379707.locale=en.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186-204.
Yixuan, L., Harun A., & Dongyu, R. (2024). AI-Driven Animation Creation: Application Exploration and Potential Risks. 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS), Bangkok, Thailand, 1-6. https://doi.org/10.1109/AiDAS63860.2024.10730166.
