Phraseological Patterns Within Human–Artificial Intelligence Togetherness (HAIT): A Corpus-Based Comparison of ESL Development and AI Simulation

The widespread integration of generative AI into academic writing has created a new communicative context in which human authorship coexists with algorithmic fluency, yet their phraseological relationship remains insufficiently understood. To address this gap, this study examines the phraseological differences between human-authored and AI-generated academic writing within the emerging framework of Human–Artificial Intelligence Togetherness (HAIT). Using a comparative corpus-based approach, it analyzes three datasets: first-year English as a Second Language (ESL) student texts (F1C), fourth-year ESL student texts (F4C), and AI-generated texts (AIC) produced by five large language models. The analysis focuses on five key features—type–token ratio (TTR), collocations, colligations, lexical bundles, and formulaic sequences—to explore how phraseological competence develops in human learners and how it contrasts with the fluency produced by algorithmic simulation. Results indicate a clear developmental trajectory between F1C and F4C: lexical variety, structural complexity, and phraseological density increase incrementally, yet both human corpora remain thematically narrow, often restricting the prompt “social” to social media contexts. In contrast, the AI corpus exhibits immediate lexical breadth, thematic diversity, and syntactic precision, with frequent use of advanced academic bundles and stable colligational forms. However, AI texts lack the hallmarks of developmental authenticity, such as revision, contextual adaptation, and evidence of cognitive effort. This asymmetry highlights the ontological divide between experiential human authorship and statistically generated fluency. The findings highlight that using AI writing as a benchmark risks obscuring L2 development, urging pedagogy centered on reflection, phraseological visibility, scaffolded authorship, and epistemic integrity.