MiniXplorer Technical Performance Evaluation and SDG Alignment Assessment

Authors

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

DOI:

https://doi.org/10.11594/ijmaber.06.12.09

Keywords:

Educational Technology, Image Recognition, Text-to-Speech, UN Sustainable Development Goals, SDG 4, Mobile Learning, AI in Education, ISO 25010

Abstract

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. 

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References

Alamil, B. A., Nazareno, K., Santos, Z. A., Chua, J., & Ayo, E. (2025). Development of MiniXplorer: An image recognition mo-bile application using Google machine learning kit and text-to-speech integra-tion. World Journal of Advanced Re-search and Reviews, 25(2), 899–911. https://doi.org/10.30574/wjarr.2025.25.2.0421

Berendt, B., Littlejohn, A., & Blakemore, M. (2020). AI in education: Learner choice and fundamental rights. Learning, Media and Technology, 45(3), 312-324.

Bhardwaj, H., Tomar, P., Sakalle, A., & Sharma, U. (2021). Principles and Foundations of Artificial Intelligence and Internet of Things Technology. In Artificial Intelli-gence to Solve Pervasive Internet of Things Issues (pp. 377-392). Academic Press.

Carruthers, P. (2020). The roots of scientific reasoning: Infancy, modularity, and the art of tracking. In The Cognitive Basis of Science (pp. 73-95). Cambridge University Press.

Fitria, T. N. (2023). Using NaturalReader: A free text-to-speech online with AI-powered voices in teaching listening TOEFL. ELTALL: English Language Teaching, Applied Linguistic and Litera-ture, 4(02).

Gutiérrez, J. A. T. (2022). Stimulation of nu-merical skills in children with visual im-pairments using image recognition. Pro-cedia Computer Science, 198, 179-184.

Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., ... & Koedinger, K. R. (2021). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelli-gence in Education.

Liquin, E. G., & Lombrozo, T. (2020). Explana-tion-seeking curiosity in childhood. Cur-rent Opinion in Behavioral Sciences, 35, 110-114.

Niklas, F., Annac, E., & Wirth, A. (2020). App-based learning for kindergarten children at home (Learning4Kids): Study protocol. BMC Pediatrics, 20, 554.

Nuraini Herawati, D. R., Widajati, W., & Sar-tinah, E. P. (2022). The role of text to speech assistive technology to improve reading ability in e-learning for ADHD students. Journal of ICSAR.

Siby, A., et al. (2020). Text to speech conver-sion for visually impaired people. Inter-national Journal of Engineering Research & Technology.

Tian, Y. (2020). Artificial intelligence image recognition method based on convolu-tional neural network algorithm. IEEE Access, 8, 125731-125744.

United Nations. (2015). Transforming our world: The 2030 Agenda for Sustainable Development. United Nations.

Zhai, X., Chu, X., Chai, C. S., et al. (2021). A re-view of artificial intelligence (AI) in edu-cation from 2010 to 2020. Complexity, 2021, 1-18.

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Published

2025-12-23

Data Availability Statement

Available upon Request

How to Cite

Ayo, E. B., Tamayo, J. D., Mijares, T. S., Bustamante, R. A., & Peralta, R. (2025). MiniXplorer Technical Performance Evaluation and SDG Alignment Assessment . International Journal of Multidisciplinary: Applied Business and Education Research, 6(12), 5960-5968. https://doi.org/10.11594/ijmaber.06.12.09