Artificial intelligence chatbot for gold-based Islamic financial education in Indonesia

Authors

  • Muhammad Muchson Attoyibi Department of Information System, Faculty of Technology and Engineering, Universitas Sunan Gresik, Gresik, East Java 61153, Indonesia

DOI:

https://doi.org/10.61511/ajcsee.v4i1.2026.3347

Keywords:

AI chatbot, educational technology, digital financial literacy., gold invesment, social sustainability

Abstract

Background: Islamic financial literacy related to gold-based products in Indonesia remains relatively low, particularly in understanding sharia-compliant gold pawn (rahn emas), investment mechanisms, and trust in digital financial services. This gap in literacy limits community financial resilience and poses a challenge to sustainable social development. While digital platforms such as Pegadaian Digital have expanded access to Islamic financial products, limited public understanding and skepticism toward digital services continue to hinder optimal utilization within the broader social context. This study aims to evaluate the effectiveness of an AI-based chatbot, designed with a specific instructional persona to enhance user engagement, as an educational tool for gold-based Islamic finance. Methods: A quantitative pretest–posttest design was employed involving 30 participants who interacted with the chatbot. Learning outcomes were measured using 12 multiple-choice questions covering four aspects: understanding of gold pawn mechanisms, sharia principles, interest in gold investment, and trust in digital financial services. Findings: The results indicate a substantial improvement in participants’ knowledge after using the chatbot. The average score increased from 52.8% in the pretest to 85.6% in the post-test. Conclusion: These findings suggest that AI chatbots are effective, scalable, and accessible tools for enhancing Islamic financial literacy, but also serve as a novel intersection between technological innovation and social empowerment. By bridging the literacy gap, this technology contributes to a more stable and resilient social-financial environment in Indonesia. Novelty/Originality of this article: The novelty of this study lies in the integration of an Agentic RAG architecture with a supportive Sharia-consultant persona, which not only ensures factual accuracy based on DSN-MUI fatwas but also addresses the socio-cognitive barriers of rural learners. The greatest improvement was observed in understanding sharia principles and digital service security, which are critical components for fostering digital trust within the community.

References

Adriani, F. R., & Riadi, I. (2025). Forensic analysis of website in cyberbullying cases on Instagram using National Institute of Justice method. International Journal of Computer Applications, 187(38), 64–71. https://doi.org/10.5120/ijca2025925678

Alsharo, M., & Aloulou, W. J. (2025). Smart banking chatbots and consumer engagement: The role of trust and privacy in AI-driven banking. Journal of Strategic Marketing. https://doi.org/10.1080/0965254X.2025.2481140

Antara. (2025). Islamic economy seen as key driver of Indonesia's future growth. Antara News. https://en.antaranews.com/news/379645/islamic-economy-seen-as-key-driver-of-indonesias-future-growth

Arner, D. W., Barberis, J. N., & Buckley, R. P. (2017). The evolution of FinTech: A new post-crisis paradigm? Georgetown Journal of International Law, 47(4), 1271–1319. https://doi.org/10.2139/ssrn.2676553

Ascarya. (2011). Akad dan produk bank syariah. PT RajaGrafindo Persada.

Atos. (2025). Sovereign generative AI for manufacturing. Atos. https://atos.net/wp-content/uploads/2025/07/atos-whitepaper-sovereign-genai-for-manufacturing-co-authored-by-atos-and-flender-2025.pdf

Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., & Abdelrazek, M. (2024). Seven failure points when engineering a retrieval augmented generation system. In Proceedings of the 3rd ACM/IEEE International Conference on AI Engineering: Software Engineering for AI (pp. 7–16). Association for Computing Machinery. https://doi.org/10.1145/3644815.3644945

Bas, M. B., Rhamadhani, R. F., Anggraeni, R. N., Darmawan, I. P. E., & Djuri, P. A. (2025). Artificial intelligence and financial regulation in Indonesia’s Islamic banking: A systematic literature review. Paradoks: Jurnal Ilmu Ekonomi, 8(3), 1174–1187. https://doi.org/10.57178/paradoks.v8i3.1519

Burns, B., Beda, J., Hightower, K., & Evenson, L. (2022). Kubernetes: Up and running (3rd ed.). O'Reilly Media.

Dewan Syariah Nasional MUI. (2002). Fatwa DSN-MUI No. 25/DSN-MUI/III/2002 tentang rahn. DSN-MUI.

Frantar, E., Ashkboos, S., Hoefler, T., & Alistarh, D. (2023). GPTQ: Accurate post-training quantization for generative pre-trained transformers. arXiv. https://doi.org/10.48550/arXiv.2210.17323

Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Wang, M., & Wang, H. (2024). Retrieval-augmented generation for large language models: A survey. arXiv. https://doi.org/10.48550/arXiv.2312.10997

Hagendorff, T. (2020). The ethics of AI ethics: An evaluation of guidelines. Minds and Machines, 30(1), 99–120. https://doi.org/10.1007/s11023-020-09517-8

Hakim, A. F., & Ly, M. K. (2024). Determinants of Islamic fintech adoption: A systematic literature review. Journal of Islamic Marketing, 15(8), 1869–1893. https://doi.org/10.1108/JIMA-11-2023-0373

Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J. E., Zhang, H., & Stoica, I. (2023). Efficient memory management for large language model serving with PagedAttention. In Proceedings of the 29th Symposium on Operating Systems Principles (pp. 611–626). Association for Computing Machinery. https://doi.org/10.1145/3600006.3613165

Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474. https://arxiv.org/abs/2005.11401

Nugroho, I. R., Wilujeng, I., & Prasetyo, Z. K. (2023). Mobile learning research trends in Indonesia: A systematic literature review. Jurnal Penelitian Pendidikan IPA, 9(8), 356–365. https://doi.org/10.29303/jppipa.v9i8.3073

Octaviyani, P. P., Sutianingsih, & Irawan, N. (2024). Systematic literature review: The impact of chatbot use and service quality on customer loyalty. Jurnal Riset Manajemen dan Bisnis, 8(2), 234–251. https://doi.org/10.36600/rma.v15i2.453

Otoritas Jasa Keuangan. (2022). Survei nasional literasi dan inklusi keuangan (SNLIK) 2022. Otoritas Jasa Keuangan. https://www.ojk.go.id/id/berita-dan-kegiatan/siaran-pers/Pages/Survei-Nasional-Literasi-dan-Inklusi-Keuangan-Tahun-2022.aspx

Otoritas Jasa Keuangan. (2024). Survei Nasional Literasi dan Inklusi Keuangan (SNLIK) 2024. Jakarta: OJK. Otoritas Jasa Keuangan. (2024). Survei Nasional Literasi dan Inklusi Keuangan (SNLIK) 2024. Jakarta: OJK. https://ojk.go.id/id/berita-dan-kegiatan/siaran-pers/Pages/OJK-dan-BPS-Umumka-n-Hasil-Survei-Nasional-Literasi-dan-Inklusi-Keuangan-Tahun-2024.aspx

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P. F., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730–27744. https://doi.org/10.48550/arXiv.2203.02155

Pavlo, A. (2024). Databases in 2023: A year in review. Carnegie Mellon University Database Group Blog. https://www.cs.cmu.edu/~pavlo/blog/2024/01/2023-databases-retrospective.html

Qu, R., Tu, R., & Bao, F. S. (2025). Is semantic chunking worth the computational cost? Findings of the Association for Computational Linguistics: NAACL 2025, 2155–2177. https://doi.org/10.18653/v1/2025.findings-naacl.114

Rahayu, R., Ali, S., Hidayah, R., & Nabila, S. (2024). Determinant factors of Islamic financial technology acceptance: Evidence from Indonesia. Journal of Telecommunications and the Digital Economy, 12(1), 405–426. https://doi.org/10.18080/jtde.v12n1.790

Redis Ltd. (2025). Redis as the engine behind real-time, intelligent chatbots. Redis Blog. https://redis.io/blog/redis-as-the-engine-behind-real-time-intelligent-chatbots

Scassa, T. (2024). Sovereignty and the governance of artificial intelligence. UCLA Law Review, 71(2), 312–358. https://www.uclalawreview.org/wp-content/uploads/securepdfs/2024/05/06-Scassa-No-Bleed.pdf

Smith, J., & Lee, K. (2025). Unveiling customer expectations of chatbot interactions: A systematic literature review and research agenda. International Journal of Human-Computer Interaction, 41(22), 10767–10794. https://doi.org/10.1080/10447318.2025.2588815

Sumidartiny, A. N. (2025). The influence of Islamic financial literacy, religiosity, and mental accounting on sharia financing decisions: Evidence from Jakarta. Review of Islamic Economics and Finance, 8(2), 351–368. https://ejournal.upi.edu/index.php/rief/article/download/90358/34872

Sung, Y. T., Chang, K. E., & Liu, T. C. (2016). The effects of integrating mobile devices with teaching and learning on students' learning performance: A meta-analysis and research synthesis. Computers & Education, 94, 252–275. https://doi.org/10.1016/j.compedu.2015.11.008

Supabase Inc. (2023). pgvector vs. Pinecone: Cost and performance comparison. Supabase Blog. https://supabase.com/blog/pgvector-vs-pinecone

Wollny, S., Schneider, J., Di Mitri, D., Weidlich, J., Rittberger, M., & Drachsler, H. (2021). Are we there yet? A systematic literature review on chatbots in education. Frontiers in Artificial Intelligence, 4, Article 654924. https://doi.org/10.3389/frai.2021.654924

Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T., & He, L. (2021). A survey of human-in-the-loop for machine learning. arXiv. https://doi.org/10.48550/arXiv.2108.00941

Zhong, K., et al. (2025). Towards transparent RAG: Fostering evidence traceability in LLM generation. arXiv. https://doi.org/10.48550/arXiv.2505.13258

Zhong, K., Suleiman, B., Erradi, A., & Chen, S. (2025). SemRAG: Semantic knowledge-augmented RAG for improved question-answering. arXiv. https://doi.org/10.48550/arXiv.2507.21110

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Published

2026-07-28

How to Cite

Attoyibi, M. M. (2026). Artificial intelligence chatbot for gold-based Islamic financial education in Indonesia. Asian Journal Collaboration of Social Environmental and Education, 4(1), 66–83. https://doi.org/10.61511/ajcsee.v4i1.2026.3347

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