Proven methods for teaching the readers who struggle most have been known for decades. Why do we often fail to use them? "There's a window of opportunity to intervene," Mark Seidenberg, a cognitive neuroscientist, said. "You don't want to let that go." In 2024, my niece Caroline received a Ph.D. in gravitational-wave physics. Her research interests include "the impact of model inaccuracies on biases in parameters recovered from gravitational wave data" and "Petrov type, principal null directions, and Killing tensors of slowly rotating black holes in quadratic gravity." I watched a little of her dissertation defense, on Zoom, and was lost as soon as she'd finished introducing herself. She and her husband now live in Italy, where she has a postdoctoral appointment. Caroline's academic achievements seem especially impressive if you know that until third grade she could barely read: to her, words on a page looked like a pulsing mass. She attended a private school in Connecticut, and there was a set time every day when students selected books to read on their own. "I can't remember how long that lasted, but it felt endless," she told me. She hid her disability by turning pages when her classmates did, and by volunteering to draw illustrations during group story-writing projects. One day, she told her grandmother that she could sound out individual letters but when she got to "the end of a row" she couldn't remember what had come before. A psychologist eventually identified her condition as dyslexia. Fluent readers sometimes think of dyslexia as a tendency to put letters in the wrong order or facing the wrong direction, but it's more complicated than that.

Division of Psychology, Communication & Human Neuroscience, The University of Manchester Author Note Hugo Rydel - Johnston https://orcid.org/0009 - 0006 - 1103 - 1015 Alex Ka fkas https://orcid.org/0000 - 0001 - 5133 - 8827 We have no conflict s of interest to disclose. Correspondence concerning this article should be addressed to Hugo Rydel - Johnston, Division of Psychology, Communication & Human Neuroscience, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK . DYSLEXIC READING TAKES LONGER 2 Abstract We ask where, and under what conditions, dyslexic reading costs arise in a large - scale naturalistic reading dataset. Using eye - tracking aligned to word - level properties -- word length, frequency, and predictability -- we model the influence of each of these feat ures on dyslexic time costs. We find that all three properties robustly change reading times in both typical and dyslexic readers, but dyslexic readers show stronger sensitivities to each of the three features, especially predictability. Counterfactual man ipulations of these features substantially narrow the dyslexic - control gap -- by about one - third -- with predictability showing the strongest effect, followed by length, and frequency. These patterns align with existing dyslexia theories suggesting heightened de mands on linguistic working memory and phonological encoding in dyslexic reading and directly motivate further research into lexical complexity and preview benefits to further explain the quantified gap. In effect, these findings break down when extra dysl exic costs arise, how large they are, and provide actionable guidance for the development of interventions and computational models for dyslexic readers. Keywords: e ye movements, r eading time, w ord length, l exical f requency, p redictability, s kipping, t otal reading time DYSLEXIC READING TAKES LONGER 3 Why Dyslexic Reading Takes Longer - And When Dyslexia is characterized by persistent difficulty in accurate and/or fluent word recognition and decoding (Lyon et al., 2003) and affects between 4 - 8% of individuals (Yang et al., 2022; Doust et al., 2022).

Things to Do in L.A. Tap to enable a layout that focuses on the article. Vanessa Silver, who tutors young children with dyslexia, works with Liina Yerro, 9, in Granada Hills. This is read by an automated voice. Please report any issues or inconsistencies here . California to begin universal screening of kindergarten through second-grade students for reading difficulties, including dyslexia.

Dyslexia in adults remains an under-researched and under-served area, particularly in non-English-speaking contexts, despite its significant impact on personal and professional lives. This work addresses that gap by focusing on Sinhala, a low-resource language with limited tools for linguistic accessibility. We present an assistive system explicitly designed for Sinhala-speaking adults with dyslexia. The system integrates Whisper for speech-to-text conversion, SinBERT, an open-sourced fine-tuned BERT model trained for Sinhala to identify common dyslexic errors, and a combined mT5 and Mistral-based model to generate corrected text. Finally, the output is converted back to speech using gTTS, creating a complete multimodal feedback loop. Despite the challenges posed by limited Sinhala-language datasets, the system achieves 0.66 transcription accuracy and 0.7 correction accuracy with 0.65 overall system accuracy. These results demonstrate both the feasibility and effectiveness of the approach. Ultimately, this work highlights the importance of inclusive Natural Language Processing (NLP) technologies in underrepresented languages and showcases a practical

Dyslexia, a neurodevelopmental disorder characterized by persistent reading difficulties, is often linked to reduced activity of the visual word form area in the ventral occipito-temporal cortex. Traditional approaches to studying dyslexia, such as behavioral and neuroimaging methods, have provided valuable insights but remain limited in their ability to test causal hypotheses about the underlying mechanisms of reading impairments. In this study, we use large-scale vision-language models (VLMs) to simulate dyslexia by functionally identifying and perturbing artificial analogues of word processing. Using stimuli from cognitive neuroscience, we identify visual-word-form-selective units within VLMs and demonstrate that targeted ablation of these units, unlike ablation of random units, leads to selective impairments in reading tasks while general visual and language comprehension abilities remain intact. In particular, the resulting model matches dyslexic humans' phonological deficits without a significant change in orthographic processing. Taken together, our modeling results replicate key characteristics of dyslexia and establish a computational framework for investigating reading disorders.

This study explores the use of virtual reality (VR) and artificial intelligence (AI) to predict the presence of dyslexia in Italian and Spanish university students. In particular, the research investigates whether VR-derived data from Silent Reading (SR) tests and self-esteem assessments can differentiate between students that are affected by dyslexia and students that are not, employing machine learning (ML) algorithms. Participants completed VR-based tasks measuring reading performance and self-esteem. A preliminary statistical analysis (t tests and Mann Whitney tests) on these data was performed, to compare the obtained scores between individuals with and without dyslexia, revealing significant differences in completion time for the SR test, but not in accuracy, nor in self esteem. Then, supervised ML models were trained and tested, demonstrating an ability to classify the presence/absence of dyslexia with an accuracy of 87.5 per cent for Italian, 66.6 per cent for Spanish, and 75.0 per cent for the pooled group. These findings suggest that VR and ML can effectively be used as supporting tools for assessing dyslexia, particularly by capturing differences in task completion speed, but language-specific factors may influence classification accuracy.

Dyslexia, affecting an estimated 10% to 20% of the global population, significantly impairs learning capabilities, highlighting the need for innovative and accessible diagnostic methods. This paper investigates the effectiveness of eye-tracking technology combined with machine learning algorithms as a cost-effective alternative for early dyslexia detection. By analyzing general eye movement patterns, including prolonged fixation durations and erratic saccades, we proposed an enhanced solution for determining eye-tracking-based dyslexia features. A Random Forest Classifier was then employed to detect dyslexia, achieving an accuracy of 88.58\%. Additionally, hierarchical clustering methods were applied to identify varying severity levels of dyslexia. The analysis incorporates diverse methodologies across various populations and settings, demonstrating the potential of this technology to identify individuals with dyslexia, including those with borderline traits, through non-invasive means. Integrating eye-tracking with machine learning represents a significant advancement in the diagnostic process, offering a highly accurate and accessible method in clinical research.

Artificial intelligence should be deployed to "level up" opportunities for dyslexic children, according to the UK science and technology secretary, Peter Kyle, who warned there was currently not enough human capacity to help people with the learning difficulty. Kyle, who is dyslexic and uses AI to support his work, said the government should carefully look at "how AI can transform education and help us assess and understand a young person's abilities into the future". He spoke as the TV chef Jamie Oliver, who is also dyslexic, launched a campaign calling for improved teacher training on dyslexia and earlier screening of children to detect the condition sooner. About 6 million people in the UK are estimated to have dyslexia, which primarily affects reading and writing skills. Kyle told the Guardian he had felt "quite emotional" when seeing AI technology used to help young people learn with "incredible empathy, encouragement and knowledge".

Two high school graduates who say they can't read or write are suing their respective public school systems, arguing they were not given the free public education to which they are entitled. Cornell Law School Professor William A. Jacobson, director of the Securities Law Clinic, told Fox News Digital the lawsuits signify a "much deeper problem" with the American public school system. "I think these cases reflect a deeper problem in education. For each of these cases, there are probably tens of thousands of students who never got a proper education -- they get pushed along the system," Jacobson said. "Unfortunately … we've created incentives, particularly for public school systems, to just push students along and not to hold them accountable."

As information technology becomes a necessity, rather than just a convenience, for most people, more attention is now focused on supporting the widest range of users; thus, making technology more inclusive. At the same time as awareness of inclusive technology is increasing, radically new ways to support users are emerging from advances in generative AI. Can generative AI provide value for developers of inclusive technology? What further work will be needed to make the most of the potential? These are the questions addressed in the accompanying paper, with a specific emphasis on supporting people with dyslexia.