This research presents the design and development of KamAi, a mobile application that leverages deep learning to recognize basic Filipino Sign Language (FSL). Developed for Android platforms, KamAi aims to address communication challenges faced by the Deaf community by enabling real-time recognition of FSL letters, numbers, and commonly used words. The application integrates Convolutional Neural Networks (CNNs) with Google’s MediaPipe for gesture detection, offering an accessible, user-friendly tool for both Deaf and hearing individuals engaged in FSL learning. Employing a developmental research design guided by the Design Thinking framework, the study engaged FSL educators, deaf and mute, hearing individuals and IT experts to ensure functionality, usability, and relevance. A dataset of over 29,000 images, combining captured and publicly available sources, was utilized to train the deep learning models. Evaluation results indicated strong model performance: the letter recognition model achieved an accuracy of 88.38%, precision of 97%, recall of 87%, and F1-score of 90%. The number model yielded 91.41% accuracy and 89% precision, though with a lower recall of 21% and F1-score of 28%, highlighting challenges in distinguishing visually similar signs. The word recognition model attained 83.08% accuracy, 96% precision, 88% recall, and 91% F1-score, demonstrating effective classification of everyday gestures. User Acceptance Testing (UAT) was conducted based on ISO 25010 software quality standards, with all six evaluated attributes, Functionality (4.73), Reliability (4.51), Compatibility (4.71), Usability (4.50), Efficiency (4.59), and Portability (4.77), receiving “Highly Acceptable” ratings. These results confirm KamAi’s practical viability as a mobile-based assistive learning tool and its potential contribution to inclusive education and digital accessibility in the Philippine context.

KamAI is an Android-based mobile application designed to support the learning and recognition of basic Filipino Sign Language (FSL), the official sign language of the Deaf community in the Philippines. The system integrates Convolutional Neural Networks (CNNs) and Google MediaPipe to enable real-time gesture recognition for FSL letters, numbers, and common words. To evaluate the application’s overall quality and practical usability, User Acceptance Testing (UAT) was conducted using the ISO/IEC 25010 software quality framework. Six quality attributes, Functionality, Reliability, Compatibility, Usability, Efficiency, and Portability, were assessed using a five-point Likert scale. Results showed that all evaluated attributes received “Highly Acceptable” ratings, with mean scores ranging from 4.50 to 4.77. Functionality (4.73) and Portability (4.77) received the highest ratings, indicating accurate recognition performance and effective operation across various Android devices. These findings demonstrate strong user approval and confirm KamAI’s readiness for real-world deployment as a mobile assistive learning tool that promotes inclusive education and digital accessibility in the Philippine context.

This study presents the design, development, and evaluation of iSign, an Android-based educational mobile application developed to support sign language learning among deaf and hard-of-hearing individuals in rural communities. The project was conducted using a research and development (R&D) approach guided by the ADDIE instructional design framework. A preliminary needs assessment, based on municipal records and field validation, revealed limited access to structured sign language education and assistive learning resources in selected barangays of Odiongan, Romblon. The iSign application was designed as a user-centered and accessible mobile learning tool incorporating alphabet learning (A–Z), number recognition (0–9), a dictionary containing 160 commonly used vocabulary words with definitions and corresponding sign language video demonstrations, and multimedia content such as nursery rhyme songs interpreted in sign language. The system was developed using Android Studio and structured to support offline accessibility to accommodate low-connectivity environments. The application was evaluated in terms of functional suitability, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability. Twenty-one community participants and four information technology experts assessed the system using a five-point Likert scale. The overall weighted mean rating of 4.0 (“Agree”) indicates that the developed application met acceptable software quality standards and user satisfaction levels. The findings demonstrate that iSign is a functional and accessible mobile learning application that can serve as a supplementary tool for sign language education in underserved communities.