Applying Artificial Intelligence Technology to Web-Based School Management System Administrative Task Automation

Authors

  • Fery Arung Tongka Amikom University Yogyakarta
  • Chriscel Novian Amikom University Yogyakarta
  • Harisna Indriya Putra Amikom University Yogyakarta
  • Ema Utami Amikom University Yogyakarta
  • Hanif Al Fattah Amikom University Yogyakarta

DOI:

https://doi.org/10.59890/ijarss.v3i5.37

Keywords:

Artificial Intelligence, Administrative Automation, School Management System

Abstract

This research systematically reviews the use of Artificial Intelligence (AI) to automate administrative tasks in web-based school management systems, analyzing 15 articles. It identifies key AI modalities such as machine learning for scheduling, natural language processing for documentation, and generative AI for content creation. Despite significant potential for improving efficiency and reducing manual errors, challenges include limited AI literacy among educators and administrators. The study emphasizes the importance of algorithmic interpretability and a multi-level implementation approach. To optimize AI’s benefits, the research recommends developing AI literacy programs, establishing ethical and regulatory frameworks, and building robust technological infrastructure. These steps are essential for successful AI integration, enabling resource reallocation from administrative duties to core educational activities.

References

Al-jaf, K., Öz, C., Mahmud, H., Rashid, T. A., Al-jaf, K., Öz, C., Mahmud, H., & Rashid, T. A. (2024). Leveraging Chatbots for Effective Educational Administration : A Systematic Review Leveraging Chatbots for Effective Educational Administration : A Systematic Review. https://doi.org/10.20944/preprints202410.0238.v1

Aldiab, A., Chowdhury, H., Kootsookos, A., Alam, F., & Allhibi, H. (2019). Utilization of Learning Management Systems (LMSs) in higher education system: A case review for Saudi Arabia. Energy Procedia, 160(2018), 731–737. https://doi.org/10.1016/j.egypro.2019.02.186

Banh, L., & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33(1), 1–17. https://doi.org/10.1007/s12525-023-00680-1

Chanchlani, N., Buchanan, F., & Gill, P. J. (2020). Addressing the indirect effects of COVID-19 on the health of children and young people. Cmaj, 192(32), E921–E927. https://doi.org/10.1503/cmaj.201008

Chukwudi, U. M., Aniekan, U. R., & Imoh, S. G. (2024). Examining the Credibility of Artificial Intelligence ( AI ) in Educational Management : Implications for Administration and Planning. 1(1), 14–30.

Garg, L., Agarwal, D., Gupta, D., Goel, P., & Jain, P. (2024). An Intelligent Approach to Admissions using Blockchain and Artificial Intelligence. 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET), 1–5.

Hossain, R., Sohag, H. J., Hasan, F., Ahmed, S., Al- Amin, & Islam, M. M. (2024). Prospective Artificial Intelligence (AI) Applications in the University Education Level: Enhancing Learning, Teaching and Administration through a PRISMA Base Review Systematic Review. Pakistan Journal of Life and Social Sciences, 22(2), 9173–9191. https://doi.org/10.57239/PJLSS-2024-22.2.00694

Huynh-The, T., Pham, Q. V., Pham, X. Q., Nguyen, T. T., Han, Z., & Kim, D. S. (2023). Artificial intelligence for the metaverse: A survey. Engineering Applications of Artificial Intelligence, 117, 1–24. https://doi.org/10.1016/j.engappai.2022.105581

Jiang, Y., Li, X., Luo, H., Yin, S., & Kaynak, O. (2022). Quo vadis artificial intelligence? Discover Artificial Intelligence, 2(1). https://doi.org/10.1007/s44163-022-00022-8

Kimmons, R., Rosenberg, J., & Allman, B. (2021). Trends in Educational Technology: What Facebook, Twitter, and Scopus Can Tell us about Current Research and Practice. TechTrends, 65(2), 125–136. https://doi.org/10.1007/s11528-021-00589-6

Mambu, J. G. Z., Pitra, D. H., Rizki, A., Ilmi, M., Nugroho, W., & Natasya, V. (2023). Pemanfaatan Teknologi Artificial Intelligence ( AI ) Dalam Menghadapi Tantangan Mengajar Guru di Era Digital. 06(01), 2689–2698.

McLoughlin, G. M., McCarthy, J. A., McGuirt, J. T., Singleton, C. R., Dunn, C. G., & Gadhoke, P. (2020). Addressing Food Insecurity through a Health Equity Lens: a Case Study of Large Urban School Districts during the COVID-19 Pandemic. Journal of Urban Health, 97(6), 759–775. https://doi.org/10.1007/s11524-020-00476-0

Peng, X., Dong, K., Ye, C., Jiang, Y., Zhai, S., Cheng, R., Liu, D., Gao, X., Wang, J., & Wang, Z. L. (2020). A breathable, biodegradable, antibacterial, and self-powered electronic skin based on all-nanofiber triboelectric nanogenerators. Science Advances, 6(26). https://doi.org/10.1126/sciadv.aba9624

Sørensen, N. L., Bemman, B., Jensen, M. B., Moeslund, T. B., & Thomsen, J. L. (2023). Machine learning in general practice: scoping review of administrative task support and automation. BMC Primary Care, 24(1), 1–14. https://doi.org/10.1186/s12875-023-01969-y

Su, J., Ng, D. T. K., & Chu, S. K. W. (2023). Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities. Computers and Education: Artificial Intelligence, 4(October 2022), 100124. https://doi.org/10.1016/j.caeai.2023.100124

Su, Y., Chen, G., Li, M., Shi, T., & Fang, D. (2021). Design and Implementation of Web Multimedia Teaching Evaluation System Based on Artificial Intelligence and jQuery. Mobile Information Systems, 2021. https://doi.org/10.1155/2021/7318891

Tangkudung, R. R. S., Wartabone, I. N., Meisy, C., Rivaly, F., Komalig, M., Radjak, A. A., & Injili, L. K. (2024). Online Management System for Universities Based on A Website. 5(11), 5197–5204.

Tjoa, E., & Guan, C. (2021). A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI. IEEE Transactions on Neural Networks and Learning Systems, 32(11), 4793–4813. https://doi.org/10.1109/TNNLS.2020.3027314

Trondal, J. (2025). The Multilevel Administrative State and the Future of Public Administration Research The Multilevel Administrative State and the Future of Public Administration ABSTRACT. International Journal of Public Administration, 48(5–6), 321–333. https://doi.org/10.1080/01900692.2024.2412273

Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., & Xu, B. (2009). Kobashigawa, J., Youn, H.S., Iskander, M. and Yun, Z., 2009, June. Comparative study of genetic programming vs. neural networks for the classification of buried objects. In 2009 IEEE Antennas and Propagation Society International Symposium (pp. 1-4). IEEE. Antennas and Propagation Society International Symposium, 1–4. https://doi.org/10.1007/s00330-021-07715-1

Wong, L. S. Y., Loo, E. X. L., Kang, A. Y. H., Lau, H. X., Tambyah, P. A., & Tham, E. H. (2020). Age-Related Differences in Immunological Responses to SARS-CoV-2. Journal of Allergy and Clinical Immunology: In Practice, 8(10), 3251–3258. https://doi.org/10.1016/j.jaip.2020.08.026

Downloads

Published

2025-05-27

Issue

Section

Articles