This study investigates whether human–LLM interaction in academic writing exhibits cross-cultural variation. Using NLP-informed corpus methods, we analyze nine semi-structured student interviews from three national contexts (Romania, Bulgaria, Switzerland) to examine how AI use is linguistically constructed across three dimensions of epistemic positioning: agency strength, authority dynamics, and discourse-level stance. Results show a strong predominance of distancing and hedging strategies, with AI consistently framed as a functional writing support tool rather than an epistemic authority. At the same time, modest but systematic cross-country differences indicate culturally embedded variation in how students discursively negotiate epistemic responsibility and evaluation in AI-assisted writing practices.

Translation can systematically alter text difficulty, particularly when moving into morphologically rich languages. This study examines whether readability-constrained Large Language Models (LLMs) can mitigate difficulty shifts observed in English–Romanian translation of children’s literature. We construct a paired four-condition corpus comprising English originals, published Romanian translations, readability-constrained LLM translations, and human readability adaptations (12 aligned passages; approx. 23,000 words). Readability is assessed using a Romanian grade-level index (LEMI) designed to be educationally comparable to Flesch–Kincaid Grade Level (FKGL), the cross-linguistic LIX metric, and morphologically informed measures derived from spaCy. Published Romanian translations are significantly more difficult than their English originals, showing higher LIX scores, grade-level estimates, and increased morphological variation. Readability-constrained LLM translation substantially reduces difficulty relative to the published versions (median delta approx. −1.46 grade levels), with significant decreases in LIX, morphological feature density, and lexical diversity (MTLD). Human adaptation yields a smaller reduction (median delta approx. −0.26). Although the direct comparison between LLM and human adaptation is marginal (p = .055, r = 0.64), LLM outputs generally produce larger reductions. These findings demonstrate that translation-induced difficulty shifts are measurable and that controllable LLM translation can modulate readability across structural, lexical, and morphological dimensions in multilingual educational contexts.

Access to age-appropriate texts is critical for young readers’ literacy acquisition. For limited-resourced languages, such as Romanian, this area remains under-researched. As such, we present ongoing work on improving readability for old Romanian texts by applying Large Language Models (LLMs). First, we compiled and cleaned a comprehensive list of archaic and regional terms from lexicographic sources, including DEX online and printed dictionaries. The cleaning process involved duplicate removal, orthographic normalization, context-based filtering, and manual review. Key challenges included distinguishing archaic forms from rare or poetic ones, resolving polysemous entries, and managing inconsistent labeling across sources. Second, LLMs were utilized to validate the archaic and regional nature of identified terms and replace them with modern equivalents, while also determining the appropriate reading level for both original and modified versions. Results show that through the replacement of archaic and regional terms, the appropriate age for the modified texts decreases by approximately 0.5 years for texts extracted from textbooks and canonical writings.

This study examines the lexical and syntactic interventions of human and LLM proofreading aimed at improving overall intelligibility in identical second language writings, and evaluates the consistency of outcomes across three LLMs (ChatGPT-4o, Llama3.1-8b, Deepseek-r1-8b). Findings show that both human and LLM proofreading enhance bigram lexical features, which may contribute to better coherence and contextual connectedness between adjacent words. However, LLM proofreading exhibits a more generative approach, extensively reworking vocabulary and sentence structures, such as employing more diverse and sophisticated vocabulary and incorporating a greater number of adjective modifiers in noun phrases. The proofreading outcomes are highly consistent in major lexical and syntactic features across the three models.

Readability is a crucial characteristic of texts, greatly influencing comprehension and reading efficacy. Unfortunately, limited research is available for less-resourced languages, especially for young populations where its impact is even higher. This paper introduces a new readability tool for children’s literature in the Romanian language, explicitly targeting primary school students aged 7-11. The tool consists of a digital repository of school reading texts (self-compiled corpus) and a text analysis interface that generates automatic readability reports for uploaded short texts. The methodology involves extracting, testing, and calibrating a readability formula for Romanian using the children’s literature corpus. Related work on readability and readability tools is discussed, followed by a description of the children’s literature corpus and the platform functionalities. The first steps are presented towards validating the readability formula for children’s literature in Romanian using the ReaderBench framework, while calibration variables relevant to the Romanian language and children’s literature are examined. Currently, no existing platform integrates a research-based readability formula for the Romanian language, making this tool unique. Overall, this research contributes to applied corpus linguistics and Digital Humanities studies and offers a valuable resource for educators, parents, and children in accessing age-appropriate and readable texts.

Romania ranks almost last in Europe when it comes to gender equality in political representation, with about 10% fewer women in politics than the E.U. average. We proceed from the assumption that this underrepresentation is also influenced by the sexism and verbal abuse female politicians face in the public sphere, especially in online media. We collect a novel dataset with sexist comments in Romanian language from newspaper articles about Romanian female politicians and propose baseline models using classical machine learning models and fine-tuned pretrained transformer models for the classification of sexist language in the online medium.