Long COVID, where symptoms persist after recovering from COVID-19, can affect cognitive functions like language. However, little is known about its impact on children’s language skills, especially across different school levels. This study investigated the impact of long COVID on language proficiency among 1244 children (Asian; 53.5% boys) from kindergartens (N = 408, Mage = 4.42 ± 1.26 years), primary schools (N = 547, Mage = 9.69 ± 1.96 years), and secondary schools (N = 289, Mage = 14.97 ± 1.85 years) in Hong Kong. Language proficiency was assessed using the Language Experience and Proficiency Questionnaire (LEAP-Q), which measured speaking, listening, reading, and writing in both Chinese and English. Participants were categorized into three groups: long COVID, recovered from COVID-19, and no history of COVID-19. One-way and two-way ANOVAs were used to analyze the differences in language proficiency across these groups and school levels. Children with long COVID symptoms exhibited significantly lower overall language proficiency, particularly in speaking and listening, compared to those in the recovered and no-COVID groups. The effect was more pronounced among primary and secondary students, with secondary school students showing the most substantial deficits. No significant differences were found between the recovered and no-COVID groups. The results suggest that long COVID might have detrimental effects on children’s linguistic proficiency. The language development of older students who suffered from long COVID could benefit from receiving targeted educational and therapeutic interventions. Copyright © 2025 by the authors.

Introduction: Readability formulas are crucial for identifying suitable texts for children’s reading development. Traditional formulas, however, are linear models designed for alphabetic languages and struggle with numerous predictors. Purpose: To develop advanced readability formulas for Chinese texts using machine-learning algorithms that can handle hundreds of predictors. It is also the first readability formula developed in Hong Kong. Method: The corpus comprised 723 texts from 72 Chinese language arts textbooks used in public primary schools. The study considered 274 linguistic features at the character, word, syntax, and discourse levels as predictor variables. The outcome variables were the publisher-assigned semester scale and the teacher-rated readability level. Fifteen combinations of linguistic features were trained using Support Vector Machine (SVM) and Random Forest (RF) algorithms. Model performance was evaluated by prediction accuracy and the mean absolute error between predicted and actual readability. For both publisher-assigned and teacher-rated readability, the all-level-feature-RF and character-level-feature-RF models performed the best. The top 10 predictive features of the two optimal models were analyzed. Results: Among the publisher-assigned and subjective readability measures, the all-RF and character-RF models performed the best. The feature importance analyses of these two optimal models highlight the significance of character learning sequences, character frequency, and word frequency in estimating text readability in the Chinese context of Hong Kong. In addition, the findings suggest that publishers might rely on diverse information sources to assign semesters, whereas teachers likely prefer to utilize indices that can be directly derived from the texts themselves to gauge readability levels. Conclusion: The findings highlight the importance of character-level features, particularly the timing of a character’s introduction in the textbook