The International Institute of Information Technology Bangalore (IIIT-B) is working on a low-cost eye-tracking tool that uses a simple webcam to detect when people answering long surveys lose focus or feel tired. This tool aims to help improve the quality of public health and social survey data. Funded by Machine Intelligence and Robotics CoE (MINRO), the project focuses on large-scale health and behavior surveys, which are mostly done through door-to-door visits. During the COVID-19 pandemic, in-person surveys became difficult, and many agencies switched to online surveys. But online surveys often have high dropout rates and incomplete answers because respondents feel overwhelmed or distracted. Led by Professor Jaya Sreevalsan Nair and PhD candidate Beryl Gnanaraj at IIIT-B, the team developed this eye-tracking idea to spot signs of mental fatigue, like loss of focus or hesitation. "By flagging such moments, survey organisers can better assess the quality of the data or redesign surveys to reduce respondent fatigue," said Prof. Nair. Unlike expensive professional eye-tracking devices which cost nearly ₹50 lakh, the IIIT-B tool works with a regular webcam. It uses webcam video to create visual maps showing where a respondent looks on the screen. These maps help identify gaze points, which machine learning models analyze to estimate attention and mental effort. Though less precise than professional devices, the system combines raw webcam footage with processed visual clues to improve reliability. It is designed for post-survey analysis, with real-time use planned for the future. The team is discussing testing the tool with the National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, to use it first in research before field trials. Making a large dataset for training the system is challenging due to varying real-world conditions like poor lighting and head movement. The researchers also focus on protecting privacy; the project is under ethical review, and data is used only for research with strict guidelines. This innovative tool could also extend to monitoring attention in online learning, assessing reading skills in children with learning disabilities, and detecting cheating in online exams, making it a versatile new technology from IIIT-B.