Delayed Wh-Question Development in Children with Hearing Loss: Evidence for Morphosyntactic Vulnerability from Corpus-Based NLP and LLM Analyses

AbstractThis study provides corpus-based evidence that English-speaking children with hearing loss (CHL) show both quantitative and qualitative delays in wh-question development compared to typically developing (TD) peers. Using Natural Language Processing (NLP)/Large Language Model (LLM) based methods and two clinical subcorpora from CHILDES, we analyzed child utterances across several syntactic dimensions: frequency, lexical diversity, structural completeness, clausal embedding, wh-fronting, and utterance length. CHL produced significantly fewer wh-questions, used a narrower range of wh-types, showed lower rates of embedding, and more structural incompleteness. These differences were most evident in syntactically complex forms, such as embedded and canonical fronted wh-questions. The results support input-sensitive and usage-based accounts of syntactic development and highlight the need for enriched linguistic input in supporting CHL’s grammatical growth. Importantly, these group differences persisted when controlling for overalllanguage development as indexed by mean length of utterance (MLU) in words, indicatingthat CHL’s difficulties with wh-questions are not reducible to generalgrammatical delay.Methodologically, the study combines dependency-parsing-based analyses with exploratory LLM evaluation to assess the feasibility and limits of automated approaches to spontaneous child language. NLP-based analyses were more stable for formally defined syntactic features, while GPT-based analysis showed mixed performance, performing better on global structural judgments than on fine-grained syntactic diagnostics.