MiniXplorer Technical Performance Evaluation and SDG Alignment Assessment

Josan  D. Tamayo,; Eliza  B. Ayo,; Raymond  L. Peralta,; Teresita  S. Mijares,; Rosemarivic  A. Bustamante,

MiniXplorer Technical Performance Evaluation and SDG Alignment Assessment

Eliza B. Ayo | Josan D. Tamayo | Teresita S. Mijares | Rosemarivic A. Bustamante | Raymond L. Peralta

Discipline: Education

Abstract:

Artificial intelligence (AI) is increasingly being integrated into edu-cation, 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 fol-lowed 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 com-patibility checks, and was assessed for security risks. User experi-ences were evaluated following the ISO/IEC 25010 quality stand-ards, 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 han-dling partial obstructions and more complex image scenarios. Auto-mated 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 is-sues 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. This study demonstrates how AI-driven tools can be developed and im-plemented to provide meaningful educational support, especially in resource-limited settings.

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