Understanding Anak Dalam Tribe students’ experiences with deep learning-based AI-generated voice cloning of Tumenggung
DOI:
https://doi.org/10.24071/icre.v1i1.47Keywords:
AI-EdTech, AI-generated voice cloning, Anak Dalam Tribe, cultural-historical activity theory, deep learningAbstract
Education for Suku Anak Dalam has always represented an encounter between two worlds of knowledge about the modern logic of schooling and the oral wisdom sustained by the spiritual authority of the Tumenggung. This study explores that question by integrating AI-generated voice cloning of the Tumenggung’s voice into a deep learning-based approach. Drawing on a qualitative approach framed by third-generation Cultural-Historical Activity Theory, we examine how this digital artifact mediates the relationships among teachers, students, and indigenous values. The findings reveal that the Tumenggung’s digital voice is not merely a replica, but a cultural dialogue space that rekindles a sense of belonging, fosters affective engagement, and builds epistemic bridges between culture and technology. Ultimately, the study suggests that the essence of educational innovation does not lie in machines that imitate humans, but in humans who rediscover their humanity through technology.
Downloads
References
Abulibdeh, A., Zaidan, E., & Abulibdeh, R. (2024). Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions. Journal of Cleaner Production, 437, Article 140527. https://doi.org/10.1016/j.jclepro.2023.140527
Agrawal, S. (2023). William Gibson’s Sprawl trilogy: Connection between humans and artificial intelligence. Rupkatha Journal on Interdisciplinary Studies in Humanities, 15(4), 1–9. https://doi.org/10.21659/rupkatha.v15n4.19
Ali, I., Warraich, N. F., & Butt, K. (2025). Acceptance and use of artificial intelligence and AI-based applications in education: A meta-analysis and future direction. Information Development, 41(3), 859–874. https://doi.org/10.1177/02666669241257206
Allam, H., Dempere, J., Akre, V., & Flores, P. (2023). Artificial intelligence in education (AIED): Implications and challenges. Proceedings of the HCT International General Education Conference, 13, 126–140. https://doi.org/10.2991/978-94-6463-286-6_10
Andayanie, L. M., Adhantoro, M. S., Purnomo, E., & Kurniaji, G. T. (2025). Implementation of deep learning in education: Towards mindful, meaningful, and joyful learning experiences. Journal of Deep Learning. 1(1), 47–56. https://doi.org/10.23917/jdl.v1i1.11157
Aridan, M., Hijriyah, U., Khabibjonovna, K. N., Geng, H., Azad, I., & Elyas, T. (2025). Pre-service language teachers’ readiness for deep learning approaches: Insights from a cross-regional study in Asia. LLT Journal: A Journal on Language and Language Teaching, 28(2), 527–551. https://doi.org/10.24071/llt.v28i2.12274
Banh, L., & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33, Article 63. https://doi.org/10.1007/s12525-023-00680-1
Baris, A. (2024). AI covers: Legal notes on audio mining and voice cloning. Journal of Intellectual Property Law and Practice, 19(7), 571–576. https://doi.org/10.1093/jiplp/jpae029
Batiibwe, M. S. K. (2019). Using Cultural Historical Activity Theory to understand how emerging technologies can mediate teaching and learning in a mathematics classroom: a review of literature. Research and Practice in Technology Enhanced Learning, 14, Article 12. https://doi.org/10.1186/s41039-019-0110-7
Bone, T. (2023). Key Components of indigenous pedagogies. Indspire.
Bulathwela, S., Pérez-Ortiz, M., Holloway, C., Cukurova, M., & Shawe-Taylor, J. (2024). Artificial Intelligence alone will not democratise education: On educational inequality, techno-solutionism and inclusive tools. Sustainability (Switzerland), 16(2), Article 781. https://doi.org/10.3390/su16020781
Clarà, M. (2015). Representation and emotion causation: A cultural psychology approach. Culture and Psychology, 21(1), 37–58. https://doi.org/10.1177/1354067X14568687
Cong-Lem, N. (2022). Vygotsky’s, Leontiev’s and Engeström’s Cultural-Historical (Activity) Theories: Overview, clarifications and implications. Integrative Psychological and Behavioral Science, 56, 1091–1112. https://doi.org/10.1007/s12124-022-09703-6
da Silva, C., Pereira, F., & Amorim, J. P. (2023). The integration of indigenous knowledge in school: a systematic review. Compare: A Journal of Comparative and International Education, 54(7), 1210–1228. https://doi.org/10.1080/03057925.2023.2184200
Ejjami, R. (2024). The future of learning: AI-based curriculum development. International Journal for Multidisciplinary Research, 6(4). https://doi.org/10.36948/ijfmr.2024.v06i04.24441
Gidiotis, I., & Hrastinski, S. (2024). Imagining the future of artificial intelligence in education: a review of social science fiction. Learning, Media and Technology, 51(1), 35–47. https://doi.org/10.1080/17439884.2024.2365829
Gong, C. (2023). AI voices reduce cognitive activity? A psychophysiological study of the media effect of AI and human newscasts in Chinese journalism. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1243078
González-Docasal, A., & Álvarez, A. (2023). Enhancing voice cloning quality through data selection and alignment-based metrics †. Applied Sciences (Switzerland), 13(14), Article 8049. https://doi.org/10.3390/app13148049
Grimalt-Álvaro, C., & Ametller, J. (2021). A cultural-historical activity theory approach for the design of a qualitative methodology in science educational research. International Journal of Qualitative Methods, 20. https://doi.org/10.1177/16094069211060664
Gu, F. (2021). On deep learning-based synthesis of language training and humanistic education in college English teaching. OALib, 8, Article e7493. https://doi.org/10.4236/oalib.1107493
Guberman, A., & Smith, K. (2021). Editorial: Expansive learning in teacher education. Frontiers in Education, 6, Article 696965. https://doi.org/10.3389/feduc.2021.696965
Guberman, A., Avidov-ungar, O., Dahan, O., & Serlin, R. (2021). Expansive Learning in Inter-Institutional Communities of Practice for Teacher Educators and Policymakers. Frontiers in Education. 6, Article 533941. https://doi.org/10.3389/feduc.2021.533941
Hamal, O., El Faddouli, N. E., Alaoui Harouni, M. H., & Lu, J. (2022). Artificial intelligent in education. Sustainability (Switzerland), 14(5), Article 2862. https://doi.org/10.3390/su14052862
Harris, J., Caldwell, B., & Longmuir, M. F. (2013). Literature review: A culture of trust enhances performance. In Australian Institute for Teaching and School Leadership (Issue June). Australian Institute for Teaching and School Leadership Limited (AITSL).
Hidayat, M. S., Rahardjo, T., & Suprihatini, T. (2013). Penerimaan Suku Anak Dalam terhadap pendidikan (Undergraduate thesis summary). Interaksi Online, 1(4). https://ejournal3.undip.ac.id/index.php/interaksi-online/article/view/3620
Hite, R. L., Childers, G. M., & Hoffman, J. (2025). Cultural–Historical Activity Theory as an integrative model of socioscientific issue based learning in museums using extended reality technologies. International Journal of Science Education, Part B: Communication and Public Engagement, 15(1), 79–94. https://doi.org/10.1080/21548455.2024.2324854
Ho, C. W. (2025). A study on integrating artificial intelligence into teaching activities in rural communities for elementary school students. International Journal of Information and Education Technology, 15(6), 1144–1149. https://doi.org/10.18178/ijiet.2025.15.6.2317
Hutapea, N. S., Putra, Z., Manullang, J., & Hartati, R. (2024). Enhancing student engagement and academic performance through digital literacy : A transformative approach in Canva application. Fonologi: Jurnal Ilmuan Bahasa Dan Sastra Inggris, 2(4). https://doi.org/10.61132/fonologi.v2i4.1227
Jurcys, P., Fenwick, M., & Liaudanskas, A. (2024). Voice cloning in an age of generative AI: Mapping the limits of the law & principles for a new social contract with technology. Social Science Research Network (SSRN). https://ssrn.com/abstract=4850866
Kalantzis, M., & Cope, B. (2024). Literacy in the time of artificial intelligence. Reading Research Quarterly, 60(1). Article e591. https://doi.org/10.1002/rrq.591
Kamalov, F., Calonge, D. S., & Gurrib, I. (2025). New Era of artificial intelligence in education: Towards a sustainable multifaceted revolution. Sustainability, 15(16), Article 12451. https://doi.org/10.3390/su151612451
Korteling, J. E. (Hans., van de Boer-Visschedijk, G. C., Blankendaal, R. A. M., Boonekamp, R. C., & Eikelboom, A. R. (2021). Human- versus artificial intelligence. Frontiers in Artificial Intelligence, 4, Article 622364. https://doi.org/10.3389/frai.2021.622364
Kovach, M. (2013). Treaties, truths and transgressive pedagogies: Re-imagining indigenous presence in the classroom. Socialist Studies/Études Socialistes, 9(1), 109–127. https://doi.org/10.18740/s4ks36
Kritandani, W., Aryani, R., & Rakasiwi, T. (2024). A report review: Artificial intelligence and the future of teaching and learning. International Research-Based Education Journal, 6(2), 245-253. https://doi.org/10.17977/um043v6i2p245-253
Mangione, G. R. J., Pieri, M., & De Santis, F. (2024). Revitalizing education in rural and small schools: The role of AI in teachers’ professional development. Italian Journal of Educational Technology, 32(1), 21–35. https://doi.org/10.17471/2499-4324/1328
Manik, S. M., Ritonga, M. U., & Hadi, W. (2025). Integrating deep learning into school curriculum: Challenges, strategies, and future directions. Jurnal Pendidikan Indonesia, 3(1), 29–44. https://doi.org/10.62007/joupi.v3i1.415
Michels, S. (2024). Teaching (with) artificial intelligence: The next twenty years. Journal of Political Science Education, 20(4), 510–521. https://doi.org/10.1080/15512169.2023.2266848
Murtaqiatusholihat, Ali, M., Hernawan, A. H., & Dewi, L. (2023). The Effectiveness of a curriculum designed based on an authentic learning approach in improving study success, attitudes, and independent learning abilities of prospective teachers. International Journal of Learning, Teaching and Educational Research, 22(9), 365–381. https://doi.org/10.26803/ijlter.22.9.20
Naseer, F., Khan, M. N., Tahir, M., Addas, A., & Aejaz, S. M. H. (2024). Integrating deep learning techniques for personalized learning pathways in higher education. Heliyon, 10(11), Article e32628. https://doi.org/10.1016/j.heliyon.2024.e32628
Perkins, M. (2023). Academic integrity considerations of AI large language models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching and Learning Practice, 20(2), Article 07. https://doi.org/10.53761/1.20.02.07
Qin, L., Yao, L., & Jin, Y. (2022). Unpacking the interaction between foreign language learners’ emotion, cognition, and activity in the flipped classroom in higher education: A perezhivanie perspective. Frontiers in Psychology, 13, Article 1005237. https://doi.org/10.3389/fpsyg.2022.1005237
Qomaruddin, & Sa’diyah, H. (2024). Kajian teoritis tentang teknik analisis data dalam penelitian kualitatif: Perspektif Spradley, Miles dan Huberman. Journal of Management, Accounting and Administration, 1(2), 77–84. https://doi.org/10.52620/jomaa.v1i2.93
Riomalen, A., Rissi, Y., & Sinaga, D. (2025). AI dan pembelajaran mendalam (deep learning): Meningkatkan kualitas pendidikan di era digital. Jayapangus Press Cetta: Jurnal Ilmu Pendidikan, 8(4), 10–23. https://doi.org/10.37329/cetta.v8i4.4386
Rizky, M., & Nazri Abdul Rahman, M. (2023). Education modernization and social status change of Suku Anak Dalam (SAD) in Jambi province. Jurnal Widyagogik, 11(1), 152–163. https://doi.org/10.21107/Widyagogik/v11i1.24153
Ross, J. N., Eastman, A., Laliberte, N., & Rawle, F. (2022). The power behind the screen : Educating competent technology users in the age of digitized inequality. International Journal of Educational Research, 115, Article 102014. https://doi.org/10.1016/j.ijer.2022.102014
Rulyansah, A., Ghufron, S., Rihlah, J., & Hardiningrum, A. (2025). Bridging the digital divide : Empowering teachers with AI tools in rural Indonesian schools. Indonesia Berdaya, 6(2), 459–470. https://ukinstitute.org/journals/ib/article/view/1073
Samsu, Rusmini, Kustati, M., Ritonga, M., Maulana, A. N., & Zulmuqim. (2022). Tumenggung leadership and educational model in leading indigenous people: Suku Anak Dalam portrait. Cogent Social Sciences, 8(1), Article 2123634. https://doi.org/10.1080/23311886.2022.2123634
Schneider, S., Beege, M., Nebel, S., Schnaubert, L., & Rey, G. D. (2022). The Cognitive-Affective-Social Theory of Learning in digital environments (CASTLE). Educational Psychology Review, 34(1), 1-38. Educational Psychology Review. https://doi.org/10.1007/s10648-021-09626-5
Selasih, N. N., & Sudarsana, I. K. (2018). Education based on ethnopedagogy in maintaining and conserving the local wisdom: A literature study. Jurnal Ilmiah Peuradeun, 6(2), 293-306. https://doi.org/10.26811/peuradeun.v6i2.219
Shaikh, H., & Hossen, M. I. (2025). Implementation of new education policy: Perception and professional adaptation of secondary school teachers in Khulna. International Journal of Indonesian Education and Teaching, 9(1), 55–74. https://doi.org/10.24071/ijiet.v9i1.10451
Somekh, B., & Nissen, M. (2011). Cultural-historical activity theory and action research. Mind, Culture, and Activity, 18(2), 93–97. https://doi.org/10.1080/10749039.2010.523102
Sumbera, B. (2021). Cultural-Historical Activity Theory (CHAT): a structure for examining justice-centered leadership outcomes. The Journal of the California Association of Professors of Educational Administration, 1, 19–30. https://eric.ed.gov/?id=EJ1318411
Tripathi, A., & Yadav, V. (2025). Leveraging artificial intelligence for rural education: A systematic review of transforming learning opportunities and bridging the urban-rural divide. Journal of Advances in Artificial Intelligence, 3(3), 215–223. https://doi.org/10.18178/jaai.2025.3.3.215-223
Wallin, D., & Tunison, S. (2022). Following their voices: Supporting indigenous students’ learning by fostering culturally sustaining relational pedagogies to reshape the school and classroom environment. Australian and International Journal of Rural Education, 32(2), 75–90. https://doi.org/10.47381/aijre.v32i2.317
Yuan, Z., & Chunrong, W. (2022). Deep learning-based listening teaching strategy in junior middle school. International Journal of Humanities and Social Science, 9(2), 65–70. https://doi.org/10.14445/23942703/ijhss-v9i2p110
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Diah Octavia Kusuma Wardani, Yundi Fitrah, Hadiyanto, Sophia Rahmawati, Umil Muhsinin (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.

