Technology for Learning a Sign Language in an Educational Environment: A Machine Learning-Based Approach
Technology for Learning a Sign Language in an Educational Environment: A Machine Learning-Based Approach

Summary/Abstract: The integration of emerging technologies, particularly machine learning, into educational environments has opened new opportunities for inclusive learning. This study presents the design, implementation, and evaluation of a web-based sign language learning system powered by machine learning. Waterfall methodology was used to describe the development of web-based systems. The system is structured around three core dimensions: Expression, communication, and comprehension. A pre-experimental quantitative approach was employed with a sample of 145 primary school students in Lima, Peru. Data were collected using a validated Likert-scale questionnaire applied before and after the intervention. Statistical analysis using the Wilcoxon signed-rank test showed significant improvements across all dimensions. Specifically, the percentage of students who felt fully capable of expressing themselves in sign language rose from 2% to 49%, while similar gains were observed in communication and comprehension. These findings demonstrate the potential of machine learning-based tools to support foundational sign language learning in inclusive educational settings. The study contributes to the growing field of AI-enhanced education and highlights the importance of accessible technology for students with and without disabilities.

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