MiniXplorer Technical Performance Evaluation and SDG Alignment Assessment - Eliza B. Ayo; Josan D. Tamayo; Teresita S. Mijares; Rosemarivic A. Bustamante; Raymond Peralta (International Journal of Multidisciplinary: Applied Business and Education Research, 2025)
Artificial intelligence (AI) is increasingly being integrated into education, offering new ways to address global learning challenges. This study examines the development and effectiveness of MiniXplorer, a mobile application powered by Google’s Machine Learning Kit (ML Kit) for image recognition and Text-to-Speech (TTS) technology. The project also considers how the app contributes to the United Nations Sustainable Development Goal 4 (Quality Education). The study followed a descriptive-developmental design using a mixed-methods approach. MiniXplorer was tested in different image conditions (e.g., resolution, format, lighting, and noise), underwent automated compatibility checks, and was assessed for security risks. User experiences were evaluated following the ISO/IEC 25010 quality standards, with data collected from surveys, interviews, and observations. MiniXplorer showed strong performance, working best with natural lighting, .jpg formats, and front or side object views (average ratings between 2.50–2.83 on a 3-point scale). It was also capable of handling partial obstructions and more complex image scenarios. Automated testing confirmed smooth compatibility with modern Android operating systems. Users gave positive feedback across all ISO 25010 criteria, with particularly high scores in functional suitability (1.42–2.00), usability (1.49), and reliability (1.75). Two minor security issues were detected but were promptly resolved. MiniXplorer has proven to be an engaging, accessible, and effective educational tool for young learners. Its design and performance support the goals of SDG 4, SDG 9 and SDG 10 by promoting inclusive, equitable, and high-quality education through the use of affordable AI technologies.