Enhancing Cultural Heritage Preservation: The Role of Artificial Intelligence in Documenting the Pattennung Dance through 360° Video Technology
https://doi.org/10.35877/454RI.daengku4515
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Abstract
The advancement of Artificial Intelligence (AI) technologies offers novel prospects for the preservation of cultural heritage, particularly in traditional performing arts like the Pattennung dance. Representing a significant aspect of South Sulawesi's cultural identity, Pattennung encapsulates historical, social, and philosophical dimensions inherent in its choreography. However, existing challenges such as inadequate documentation, a decline in participation among younger generations, and the pervasive forces of globalization render this traditional art form vulnerable to cultural dilution and potential extinction. This article examines the multifaceted role of AI in the digitalization and preservation of Pattennung, employing techniques such as motion reconstruction, cultural databases, and immersive Virtual Reality (VR) environments to create engaging and authentic cultural experiences.
While these technological innovations provide pathways for broader accessibility and educational opportunities, they also present significant challenges regarding cultural authenticity, including the risks of commodification, the reduction of rich philosophical meanings, and potential distortions of the dance's historical context. This study highlights the importance of integrating community involvement and ethical considerations in the development of AI applications to ensure that the true essence of Pattennung is preserved. By fostering collaboration among cultural practitioners, technologists, and local communities, this research argues that AI can serve as a strategic asset in the enduring effort to uphold and celebrate traditional art forms. The findings of this study aim to inform policymakers and cultural organizations on the best practices for AI implementation in cultural preservation, ultimately ensuring that innovations in technology reinforce rather than undermine the authenticity of cultural expressions.
The integration of AI brings both opportunities and challenges, warranting a balanced consideration of techniques like Digital Twin Technology and immersive digital experiences that not only document but also enhance community engagement and intergenerational transmission of cultural knowledge. This research asserts that AI possesses the substantial potential to catalyze cultural preservation efforts while keeping the philosophical and contextual narratives intact, thereby enriching the cultural fabric of society.
References
Avnoon, N. and Oliver, A. L. (2023). Nothing new under the sun: medical professional maintenance in the face of artificial intelligence's disruption. Big Data & Society, 10(2). https://doi.org/10.1177/20539517231210269
Chen, J., Lai, J., Luo, S., Zhang, Y., Li, H., & Luo, Y. (2024). Sustainable ai design for canton embroidery based on design thinking an example of lotus and butterfly themes. Proceedings of the 3rd International Conference on Innovation Design and Digital Technology, ICIDDT 2023, November 3–5, 2023, Z. https://doi.org/10.4108/eai.3-11-2023.2342152
Ghaith, K. and Hutson, J. (2024). A qualitative study on the integration of artificial intelligence in cultural heritage conservation. Metaverse, 5(2), 2654. https://doi.org/10.54517/m.v5i2.2654
Gibbs, B., Quennerstedt, M., & Larsson, H. (2016). Teaching dance in physical education using exergames. European Physical Education Review, 23(2), 237-256. https://doi.org/10.1177/1356336x16645611
Harisanty, D., Obille, K. L. B., Anna, N. E. V., Purwanti, E., & Retrialisca, F. (2024). Cultural heritage preservation in the digital age, harnessing artificial intelligence for the future: a bibliometric analysis. Digital Library Perspectives, 40(4), 609-630. https://doi.org/10.1108/dlp-01-2024-0018
Harisanty, D., Obille, K. L. B., Anna, N. E. V., Purwanti, E., & Retrialisca, F. (2024). Cultural heritage preservation in the digital age, harnessing artificial intelligence for the future: a bibliometric analysis. Digital Library Perspectives, 40(4), 609-630. https://doi.org/10.1108/dlp-01-2024-0018
Hiswara, A., Aziz, A. M., & Pujowati, Y. (2023). Cultural preservation in a globalized world: strategies for sustaining heritage. West Science Social and Humanities Studies, 1(03), 98-106. https://doi.org/10.58812/wsshs.v1i03.250
Khadijah, U. L. S., Winoto, Y., Shuhidan, S. M., Anwar, R. K., & Lusiana, E. (2024). Community participation in preserving the history of heritage tourism sites. Journal of Law and Sustainable Development, 12(1), e2504. https://doi.org/10.55908/sdgs.v12i1.2504
Kiyasseh, D., Ma, R., Haque, T. F., Miles, B. J., Wagner, C. v., Donoho, D. A., … & Hung, A. J. (2023). A vision transformer for decoding surgeon activity from surgical videos. Nature Biomedical Engineering, 7(6), 780-796. https://doi.org/10.1038/s41551-023-01010-8
Lawin, F. J., Byström, A., Roepstorff, C., Rhodin, M., Almlöf, M., Silva, M., … & Hernlund, E. (2023). Is markerless more or less? comparing a smartphone computer vision method for equine lameness assessment to multi-camera motion capture. Animals, 13(3), 390. https://doi.org/10.3390/ani13030390
Li, M., Miao, Z., & Ma, C. (2019). Dance movement learning for labanotation generation based on motion-captured data. IEEE Access, 7, 161561-161572. https://doi.org/10.1109/access.2019.2951588
Ma, B. (2025). Application of deep learning in the restoration and design of traditional dance movements. International Journal of High Speed Electronics and Systems. https://doi.org/10.1142/s0129156425407211
Maier‐Hein, L., Eisenmann, M., Sarıkaya, D., März, K., Collins, T., Malpani, A., … & Speidel, S. (2022). Surgical data science – from concepts toward clinical translation. Medical Image Analysis, 76, 102306. https://doi.org/10.1016/j.media.2021.102306
Mulyanto, E., Yuniarno, E. M., Hafidz, I., Budiyanta, N. E., Priyadi, A., & Purnomo, M. H. (2023). Modified deep pattern classifier on indonesian traditional dance spatio-temporal data. EMITTER International Journal of Engineering Technology, 11(2), 214-233. https://doi.org/10.24003/emitter.v11i2.832
Pisoni, G., Díaz-Rodríguez, N., Gijlers, H., & Tonolli, L. (2021). Human-centered artificial intelligence for designing accessible cultural heritage. Applied Sciences, 11(2), 870. https://doi.org/10.3390/app11020870
Rajaraman, V., Nallaswamy, D., & Shenoy, A. (2025). Artificial intelligence and machine learning in diagnosing and managing temporomandibular disorders: a systematic review and meta-analysis. Journal of Oral Biology and Craniofacial Research, 15(6), 1591-1600. https://doi.org/10.1016/j.jobcr.2025.09.013
Speaker, T., O’Donnell, S., Wittemyer, G., Bruyere, B. L., Loucks, C., Dancer, A., … & Solomon, J. (2022). A global community‐sourced assessment of the state of conservation technology. Conservation Biology, 36(3). https://doi.org/10.1111/cobi.13871
Talawar, A., Suresh, S., & Alathur, S. (2024). Assessing the moderating effects of involvement on tourist attitudes and intentions through virtual reality applications. Journal of Hospitality and Tourism Insights, 7(2), 1050-1069. https://doi.org/10.1108/jhti-10-2023-0676
Wu, X., Wang, L., Chen, R., Liu, B., Yang, X., Feng, Y., … & Shi, D. (2025). Generation of fundus fluorescein angiography videos for health care data sharing. JAMA Ophthalmology, 143(8), 623. https://doi.org/10.1001/jamaophthalmol.2025.1419
Zhu, Q. and Liu, X. (2025). The application of artificial intelligence in the revitalization of intangible cultural heritage helps the cultural industry succeed. Journal of Computational Methods in Sciences and Engineering, 25(5), 4566-4580. https://doi.org/10.1177/14727978251337999
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