Understanding students' readiness for artificial intelligence-based project learning in strengthening local wisdom values

Authors

  • Sutrisno Universitas Muhammadiyah Ponorogo, Indonesia https://orcid.org/0000-0003-4858-3615
  • Abdul Azis Universitas Muhammadiyah Makassar, Indonesia
  • Sigit Dwi Laksana Universitas Muhammadiyah Ponorogo, Indonesia https://orcid.org/0000-0002-3481-5692
  • Eli Karliani Universitas Palangka Raya, Indonesia
  • Yayuk Hidayah Universitas Negeri Yogyakarta, Indonesia
  • Prini Desima Evawani Ambarita Universitas HKBP Nommensen Pematangsiantar, Indonesia
  • I Putu Windu Mertha Sujana Universitas Pendidikan Ganesha, Indonesia
  • Ernawati Simatupang Universitas Muhammadiyah Sorong, Indonesia

DOI:

https://doi.org/10.21831/jc.v23i1.90763

Keywords:

artificial intelligence, Indonesia local wisdom, learning project

Abstract

This study aimed to measure students’ readiness to implement artificial intelligence-based project learning by strengthening local wisdom values in Indonesia. Using a quantitative survey method, the participants in this study were 285 program students from universities in Indonesia, namely Universitas Muhammadiyah Ponorogo, Universitas Muhammadiyah Makassar, Universitas Pendidikan Indonesia, and Universitas Negeri Yogyakarta, who were randomly assigned to ensure balanced representation across academic years. The study was an online, objective test on an artificial intelligence-based project aimed at strengthening local wisdom values. Data analysis was used with descriptive quantitative methods. The results of the analysis showed that students’ readiness in artificial intelligence-based project learning for strengthening local wisdom values was at the “good” level of awareness, while other indicators were at the “fair” and “poor” levels, indicating the need for further improvement on artificial intelligence-based project learning for strengthening local wisdom values in Indonesia. These findings underscore the importance of integrating artificial intelligence-based project learning to strengthen local wisdom values. Thus, this study recommended the development of specialised training on artificial intelligence-based project learning to reinforce local wisdom values and to incorporate local wisdom materials into the learning curriculum to equip students with the necessary skills for the development of artificial intelligence.

References

As I, Pal S, Basu P. (2018).Artificial intelligence in architecture: Generating conceptual design via deep learning. International Journal of Architectural Computing. 2018;16(4):306-327. doi:10.1177/1478077118800982

Barros, A., Prasad, A., & Śliwa, M. (2023). Generative artificial intelligence and academia: Implication for research, teaching and service. Management Learning, 54(5), 597-604. https://doi.org/10.1177/13505076231201445

Caspari-Sadeghi, S. (2023). Artificial Intelligence in Technology-Enhanced Assessment: A Survey of Machine Learning. Journal of Educational Technology Systems, 51(3), 372-386. https://doi.org/10.1177/00472395221138791

Eliza, F., et al (2024). Assessing student readiness for mobile learning from a cybersecurity perspective. Online Journal of Communication and Media Technologies, 14(4), e2024xx. https://doi.org/10.30935/ojcmt/xxxx

Gao, D., & Yu, D. (2024). Challenges and Cracks: Ethical Issues in the Development of Artificial Intelligence. Science, Technology and Society, 29(3), 415-434. https://doi.org/10.1177/09717218241246372

Ghasemi S, Dashti M.(2024).Artificial Intelligence and Deep Learning in Preservation Rhinoplasty: A Review. The American Journal of Cosmetic Surgery. 2024;0(0). doi:10.1177/07488068231224133

Halimah, L., Hidayah, Y., Heryani, H., Trihastuti, M., & Arpannudin, I. (2022). The meaning of maintaining a life philosophy of simplicity for life pleasure: A study in Kampung Naga, Tasikmalaya. Journal of Human Behavior in the Social Environment, 33(8), 1149–1159. https://doi.org/10.1080/10911359.2022.2128489

Idowu SO, Fatokun AA. (2021).Artificial Intelligence (AI) to the Rescue: Deploying Machine Learning to Bridge the Biorelevance Gap in Antioxidant Assays. SLAS TECHNOLOGY: Translating Life Sciences Innovation. 2021;26(1):16-25. doi:10.1177/2472630320962716

Kaka H, Zhang E, Khan N. (2021).Artificial Intelligence and Deep Learning in Neuroradiology: Exploring the New Frontier. Canadian Association of Radiologists Journal. 2021;72(1):35-44. doi:10.1177/0846537120954293

Kavitha, K., & Joshith, V. P. (2024). The Transformative Trajectory of Artificial Intelligence in Education: The Two Decades of Bibliometric Retrospect. Journal of Educational Technology Systems, 52(3), 376-405. https://doi.org/10.1177/00472395241231815

Koć-Januchta, M. M., Schönborn, K. J., Tibell, L. A. E., Chaudhri, V. K., & Heller, H. C. (2020). Engaging With Biology by Asking Questions: Investigating Student’s Interaction and Learning With an Artificial Intelligence-Enriched Textbook. Journal of Educational Computing Research, 58(6), 1190-1224. https://doi.org/10.1177/0735633120921581

Kulkarni C, Liu D, Fardeen T, et al. (2024).Artificial intelligence and machine learning technologies in ulcerative colitis. Therapeutic Advances in Gastroenterology. 2024;17. doi:10.1177/17562848241272001

Liu, J., Li, S., & Dong, Q. (2024). Collaboration with Generative Artificial Intelligence: An Exploratory Study Based on Learning Analytics. Journal of Educational Computing Research, 62(5), 1234-1266. https://doi.org/10.1177/07356331241242441

Lo, A. W., & Zhang, R. (2022). The wisdom of crowds versus the madness of mobs: An evolutionary model of bias, polarization, and other challenges to collective intelligence. Collective Intelligence, 1(1). https://doi.org/10.1177/26339137221104785

Lu Y, Pareek A, Yang L, et al. (2023).Deep Learning Artificial Intelligence Tool for Automated Radiographic Determination of Posterior Tibial Slope in Patients With ACL Injury. Orthopaedic Journal of Sports Medicine. 2023;11(12). doi:10.1177/23259671231215820

Ma T, Wu Q, Jiang L, et al. (2023).Artificial Intelligence and Machine (Deep) Learning in Otorhinolaryngology: A Bibliometric Analysis Based on VOSviewer and CiteSpace. Ear, Nose & Throat Journal. 2023;0(0). doi:10.1177/01455613231185074

Madakam, S., Uchiya, T., Mark, S., & Lurie, Y. (2022). Artificial Intelligence, Machine Learning and Deep Learning (Literature: Review and Metrics). Asia-Pacific Journal of Management Research and Innovation, 18(1-2), 7-23. https://doi.org/10.1177/2319510X221136682

Mathur P, Srivastava S, Xu X, Mehta JL. Artificial Intelligence, Machine Learning, and Cardiovascular Disease.(2020). Clinical Medicine Insights: Cardiology. 2020;14. doi:10.1177/1179546820927404

McCarthy, K. S., & Yan, E. F. (2024). Reading Comprehension and Constructive Learning: Policy Considerations in the Age of Artificial Intelligence. Policy Insights from the Behavioral and Brain Sciences, 11(1), 19-26. https://doi.org/10.1177/23727322231218891

Mohamed Ali A-M, El-Alali E, Weltz AS, Rehrig ST. (2021).Thoracic Point-of-Care Ultrasound: A SARS-CoV-2 Data Repository for Future Artificial Intelligence and Machine Learning. Surgical Innovation. 2021;28(2):214-219. doi:10.1177/15533506211018671

Mühlhoff, R. (2020). Human-aided artificial intelligence: Or, how to run large computations in human brains? Toward a media sociology of machine learning. New Media & Society, 22(10), 1868-1884. https://doi.org/10.1177/1461444819885334

Pazhayattil AB, Konyu-Fogel G. (2023).An empirical study to accelerate machine learning and artificial intelligence adoption in pharmaceutical manufacturing organizations. Journal of Generic Medicines. 2023;19(2):81-91. doi:10.1177/17411343221151109

Rae, D., Cartwright, E., Gongora, M., Hobson, C., & Shah, H. (2024). ‘Insight unlocked’: Applying a collective intelligence approach to engage employers in informing local skills improvement planning. Industry and Higher Education, 38(2), 164-176. https://doi.org/10.1177/09504222231186376

Romoli M, Caliandro P. Artificial intelligence, (2019). machine learning, and reproducibility in stroke research. European Stroke Journal. 2024;9(3):518-520. doi:10.1177/23969873241275863

Samala, Agariadne Dwinggo; Howard, Natalie-Jane; Criollo-C., Santiago; Budiman, Ridho Dedy Arief; Hakiki, Muhammad; Hidayah, Yayuk.(2024). What Does an IMoART Application Look Like? IMoART--An Interactive Mobile Augmented Reality Application for Support Learning Experiences in Computer Hardware. International Journal of Interactive Mobile Technologies, 2024, Vol 18, Issue 13, p148. DOI 10.3991/ijim.v18i13.47565

Sharma, R. K. (2020). Artificial Intelligence, Machine Learning and the Reconstruction of Employee Psychology. NHRD Network Journal, 13(4), 472-479. https://doi.org/10.1177/2631454120971912

Sterne, J., & Razlogova, E. (2019). Machine Learning in Context, or Learning from LANDR: Artificial Intelligence and the Platformization of Music Mastering. Social Media + Society, 5(2). https://doi.org/10.1177/2056305119847525

Verma, A., Lamsal, K., & Verma, P. (2022). An investigation of skill requirements in artificial intelligence and machine learning job advertisements. Industry and Higher Education, 36(1), 63-73. https://doi.org/10.1177/0950422221990990

Verma, A., Lamsal, K., & Verma, P. (2022). An investigation of skill requirements in artificial intelligence and machine learning job advertisements. Industry and Higher Education, 36(1), 63-73. https://doi.org/10.1177/0950422221990990

Wang, X., et al (2024). The Efficacy of Artificial Intelligence-Enabled Adaptive Learning Systems From 2010 to 2022 on Learner Outcomes: A Meta-Analysis. Journal of Educational Computing Research, 0(0). https://doi.org/10.1177/07356331241240459

Yun, G., Lee, K. M., & Choi, H. H. (2024). Empowering Student Learning Through Artificial Intelligence: A Bibliometric Analysis. Journal of Educational Computing Research, 0(0). https://doi.org/10.1177/07356331241278636

Zhu A, Tailor P, Verma R, et al. (2023).Implementation of deep learning artificial intelligence in vision-threatening disease screenings for an underserved community during COVID-19. Journal of Telemedicine and Telecare. 2023;0(0). doi:10.1177/1357633X231158832

Published

2026-04-02

How to Cite

Sutrisno, Azis, A., Laksana, S. D., Karliani, E., Hidayah, Y., Ambarita, P. D. E., … Simatupang, E. (2026). Understanding students’ readiness for artificial intelligence-based project learning in strengthening local wisdom values . Jurnal Civics: Media Kajian Kewarganegaraan, 23(1), 172–183. https://doi.org/10.21831/jc.v23i1.90763

Issue

Section

Original Research Article

Citation Check