# Language Proficiency Assessment from Eye Movements in Naturalistic Passage Reading

arXiv: https://arxiv.org/abs/2608.30583 · HTML: https://mlsanity.com/paper/2608.30583/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 58 references that are in the map.
- First posted on arXiv 31 Aug 2026.

## Builds on (most cited first, 8 of 8)

- [Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye](https://mlsanity.com/paper/1906.11889/) (2019)
- [URIEL+: Enhancing Linguistic Inclusion and Usability in a Typological and Multilingual Knowledge Base](https://mlsanity.com/paper/2409.18472/) (2024)
- [Assessing Language Proficiency from Eye Movements in Reading](https://mlsanity.com/paper/1804.07329/) (2018)
- [Eye-tracking based classification of Mandarin Chinese readers with and without dyslexia using neural sequence models](https://mlsanity.com/paper/2210.09819/) (2022)
- [Predicting Native Language from Gaze](https://mlsanity.com/paper/1704.07398/) (2017)
- [Fine-Grained Prediction of Reading Comprehension from Eye Movements](https://mlsanity.com/paper/2410.04484/) (2024)
- [Decoding Reading Goals from Eye Movements](https://mlsanity.com/paper/2410.20779/) (2024)
- [D'ej`a Vu? Decoding Repeated Reading from Eye Movements](https://mlsanity.com/paper/2502.11061/) (2025)

## Abstract (arXiv)

Standard language proficiency tests rely on linguistic tasks such as vocabulary, grammar and reading comprehension quizzes. An alternative, cognitively motivated approach, introduced in Berzak et al. (2018), proposed instead to predict language proficiency from behavioral traces of eye movements in reading. In this work, we validate and extend this approach from single sentences to more naturalistic reading of contextualized passages in English as a second language, new proficiency measures, prediction models, and reading in an information seeking regime. We find that the approach is effective in all these evaluations. We further address two key open questions on eye movement based proficiency testing: (1) potential scoring biases that reflect the proximity of the reader's native language to English, which may undermine validity, and (2) its reliability. We find that eye movement based proficiency scores are indeed biased towards L1s that are linguistically closer to English. We propose a score debiasing method which effectively remedies this issue. The reliability analyses suggest that eye movement proficiency scores are more reliable than standard language proficiency scores. Overall, our results strengthen and broaden the empirical foundations for future eye movement based language assessment technologies.