Project Info

Lexia

Inspiration

As college students, the four of us have worked with elementary and middle school students who struggle with reading comprehension and confidence while studying. One student in particular, an 11-year-old named Noah, left a lasting impression on us. While reading, Noah would often reread the same sentence several times, becoming increasingly frustrated each time. We watched his confidence fade as he second-guessed himself and hesitated to continue, even when he understood more than he thought. Through experiences like Noah's, we realized that many students show signs of dyslexia, reading fatigue, or other learning challenges that often go unnoticed. Current reading tools are largely one size fits all and depend on students recognizing their own struggles and asking for help, a step many young learners are reluctant or unable to take. This inspired us to ask a simple question: what if technology could recognize when a student was struggling and adapt in real time to their needs? We built Lexia to make reading more personalized, supportive, and accessible. By identifying signs of difficulty and providing tailored assistance as students read, we hope to bridge literacy gaps before frustration turns into discouragement and help every learner build the confidence to succeed.

What it does

Lexia is an adaptive reading comprehension platform that combines webcam-based eye tracking, AI-powered comprehension support, and voice assistance to create a more autonomous reading experience for dyslexic students. The platform detects signals such as regressions, fixation spikes, and line-skipping to identify when a reader may be struggling. When difficulty is detected, Lexia defines words, reads out text, and adds problem words to a sidebar to be later visited. Once a reading session is over, the student can see a progress report displaying variance in words and a word count of problem words. The goal is to support readers without forcing them to stop and search for help themselves.

How we built it

We built Lexia using React and FastAPI, integrating webcam-based eye tracking, AI-powered comprehension support, text-to-speech functionality, and dynamic content ingestion. We leveraged browser-based eye tracking to analyze reading behavior in real time and connected those signals to adaptive reading interventions. The platform combines multiple technologies to create a seamless experience: eye tracking for behavioral signals, AI-generated reading support and quizzes, voice assistance for pronunciation and comprehension, and automated extraction of reading content from websites and uploaded documents.

Challenges we ran into

The biggest challenge was eye tracking. Building a system that could reliably estimate where a user was looking using only a webcam required extensive calibration, testing, and iteration. Small changes in lighting, head position, webcam quality, and user movement could significantly affect tracking accuracy. We spent a large portion of the hackathon experimenting with calibration methods, improving stability, and reducing noise in gaze predictions. Finding the right balance between responsiveness and accuracy was one of the most technically demanding parts of the project. Another challenge was deciding when the system should intervene. We wanted Lexia to feel supportive rather than distracted, so we carefully designed the platform to provide assistance only when consistent struggle patterns were detected.

Accomplishments we're proud of

We're proud that we successfully built a functioning adaptive reading platform that responds to reader behavior in real time. Integrating eye tracking, AI-powered reading support, voice assistance, and content ingestion into a cohesive user experience within a hackathon timeframe was a major accomplishment. Most importantly, we're proud that Lexia tackles a meaningful educational problem that we've personally witnessed through tutoring and mentoring younger students.

What we learned

The biggest thing we learned was how complex eye tracking is in practice. We gained hands-on experience with gaze estimation, calibration techniques, behavioral signal detection, and the challenges of making webcam-based tracking usable in real-world environments. We also learned the importance of user-centered design in educational technology. Throughout development, we found that even small interface and calibration changes dramatically affected the reading experience. Building Lexia reinforced the idea that effective accessibility tools need to adapt to users rather than forcing users to adapt to the technology.

What's next

We're excited to continue improving Lexia beyond this prototype. Our next steps include enhancing eye-tracking accuracy, exploring signals like pupil dilation to better detect frustration and reading fatigue, and supporting dedicated IR eye-tracking hardware. We also plan to build native mobile apps, introduce multi-user accounts, and create real-time teacher alerts so educators can identify struggling students earlier. Long term, we hope to deploy Lexia as a browser extension that can support students across the web and conduct formal studies to validate its impact on reading outcomes. Our goal is to make personalized reading support available wherever and whenever students need it.

Analysis

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abirami kumar
1

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Technology

Found in codeClaimed only

CSSIn code
FastAPIIn code
HTMLIn code
JavaScriptIn code
PythonIn code
ReactIn code
TypeScriptIn code
AnthropicClaimed

7 of 8 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

2.6 MB

Source files

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.