Development of Alakdan: An A.I. virtual assistant for Centro Escolar University processes

This study addresses improvements in student administrative processes at Centro Escolar University (CEU)-Manila by developing and evaluating Alakdan, a mobile virtual assistant powered by Natural Language Processing (NLP). The chatbot guides students through key procedures such as admission, enrollment, scholarship applications, and diploma requests. Evaluated against the ISO/IEC 25010 software quality standards, Alakdan demonstrated high performance across seven attributes: functional suitability, performance efficiency, usability, reliability, security, maintainability, and portability. Testing revealed a low error rate (1.50%) and fallback rate (2.79%), with response times averaging 5–8 seconds. Positive user feedback underscores its effectiveness in reducing administrative burdens. This research contributes to AI applications in higher education and offers insights for future chatbot development in academic settings.

Authors

Publication Details

Journal
World Journal of Advanced Research and Reviews
Published
2025-02-22
DOI
https://doi.org/10.30574/wjarr.2025.25.2.0389
Primary Topic
Online and Blended Learning
Type
article
Field-Weighted Citation Impact
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article

Development of Alakdan: An A.I. virtual assistant for Centro Escolar University processes

Eliza B. Ayo, Francis Steven Z. Dungca, Reymark M. Garduque, Ellesia Catherine P. Reyes et al.
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Development of Alakdan: An A.I. virtual assistant for Centro Escolar University processes

Eliza B. Ayo, Francis Steven Z. Dungca, Reymark M. Garduque, Ellesia Catherine P. Reyes, Grace Marjorie B. Berber
article en

Abstract

This study addresses improvements in student administrative processes at Centro Escolar University (CEU)-Manila by developing and evaluating Alakdan, a mobile virtual assistant powered by Natural Language Processing (NLP). The chatbot guides students through key procedures such as admission, enrollment, scholarship applications, and diploma requests. Evaluated against the ISO/IEC 25010 software quality standards, Alakdan demonstrated high performance across seven attributes: functional suitability, performance efficiency, usability, reliability, security, maintainability, and portability. Testing revealed a low error rate (1.50%) and fallback rate (2.79%), with response times averaging 5–8 seconds. Positive user feedback underscores its effectiveness in reducing administrative burdens. This research contributes to AI applications in higher education and offers insights for future chatbot development in academic settings.

World Journal of Advanced Research and ReviewsVol. 25(2)
Openalex Percentile: Top 97%
Online and Blended Learning
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