Resumen
This study introduces the Eye-tracking Translation Software (ETS), a system that leverages eye-tracking data and real-time translation to enhance reading flow for non-native language users in complex, technical texts. By measuring the fixation duration, we can detect moments of cognitive load, ETS selectively provides translations, maintaining reading flow and engagement without undermining language learning. The key technological components include a desktop eye-tracker integrated with a custom Python-based application. Through a user-centered design, ETS dynamically adapts to individual reading needs, reducing cognitive strain by offering word-level translations when needed. A study involving 53 participants assessed ETS?s impact on reading speed, fixation duration, and user experience, with findings indicating improved comprehension and reading efficiency. Results demonstrated that gaze-based adaptations significantly improved their reading experience and reduced cognitive load. Participants positively rated ETS?s usability and were noted through preferences for customization, such as pop-up placement and sentence-level translations. Future work will integrate AI-driven adaptations, allowing the system to adjust based on user proficiency and reading behavior. The study contributes to the growing evidence of eye-tracking?s potential in educational and professional applications, offering a flexible, personalized approach to reading assistance that balances language exposure with real-time support.