Proficiency Without Interaction: Comparing Stance and Engagement in LLM‐Generated and Human CEFR Writing
This study compared the linguistic resources writers use to express attitudes, signal certainty, and interact with readers, that is, stance and engagement patterns. We examined these patterns in large language model (LLM)‐generated essays and human learner essays across Common European Framework of Reference for Languages (CEFR) levels A2 through C1. We analyzed 592 human essays alongside 4724 LLM responses to identical writing tasks produced by six models. Task‐controlled mixed‐effects models revealed systematic divergences that exceed what raw frequencies suggest, indicating that structural proficiency can mask functional misalignments. At A2, LLMs overused attitude markers (71% higher than humans) while underusing engagement markers (22% lower). At upper levels, gaps widened further: self‐mention collapsed to 41% of human rates at B2 and 27% at C1, engagement markers fell to 27% at both B2 and C1, and hedges and boosters dropped to approximately 61% of human rates at C1. These patterns held across all six models, indicating that CEFR‐conditioned generation produces a functional profile that is consistent across models but mismatched to human writing: it associates lower proficiency with evaluative excess and higher proficiency with suppressed interactional marking. When LLM texts serve as models or feedback sources in language teaching, practitioners can expect stance and engagement profiles to diverge from human learner writing in level‐dependent ways.