AI-Driven Innovations in System Reliability, Government Automation, and Personalized Learning

Vivien A. Agustin, Jonilo Mababa, Vilma A. Dela Cruz, Edwin C. Agustin, Vanessa A. Diaz, Verona A. Guzman and Criselle J. Centeno
Additional contact information
Vivien A. Agustin: Graduate School Department La Consolacion University, Bulihan, City of Malolos, Bulacan, Philippines
Jonilo Mababa: Graduate School Department La Consolacion University, Bulihan, City of Malolos, Bulacan, Philippines
Vilma A. Dela Cruz: Graduate School Department Pamantasan ng Lungod ng Maynila, Intramuros Manila, Philippines
Edwin C. Agustin: Graduate School Department Pamantasan ng Lungod ng Maynila, Intramuros Manila, Philippines
Vanessa A. Diaz: Civil-Military Operations Regiment CMOR Compound Lawton Avenue Fort Bonifacio Taguig City, Philippines
Verona A. Guzman: J. Villegas Vocational High School Jacinto St.
Criselle J. Centeno: Graduate School Department Pamantasan ng Lungod ng Maynila, Intramuros Manila, Philippines Tondo, Manila, Philippines

International Journal of Research and Innovation in Applied Science, 2025, vol. 10, issue 3, 762-769

Abstract: This study demonstrates how Generative AI (GenAI) may improve productivity, accuracy, and workflow optimization in a variety of applications, including autonomous snowcat navigation, government report automation, and AI-powered personalized e-learning. AI-powered data visualization and extraction expedites government reporting while lowering human error and intervention. AI-powered path optimization, obstacle recognition, and sensor fusion enhance autonomous snowcat navigation’s adaptability and safety in challenging environments. Machine learning algorithms make predictive analytics, adaptive content, and recommendation systems possible in personalized e-learning, which improves learning results and student engagement. The findings demonstrate how AI can revolutionize complex process automation, enhance decision-making, and boost operational effectiveness. The necessity for ongoing improvements in transparency, equity, and security in AI applications is highlighted by obstacles including algorithmic bias, data privacy issues, and scale constraints.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.rsisinternational.org/journals/ijrias/ ... -issue-3/762-769.pdf (application/pdf)
https://rsisinternational.org/journals/ijrias/arti ... rsonalized-learning/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bjf:journl:v:10:y:2025:i:3:p:762-769

Access Statistics for this article

International Journal of Research and Innovation in Applied Science is currently edited by Dr. Renu Malsaria

More articles in International Journal of Research and Innovation in Applied Science from International Journal of Research and Innovation in Applied Science (IJRIAS)
Bibliographic data for series maintained by Dr. Renu Malsaria ().