The demand for such an authority is enormous. Correctness is not a marginal preference; it is an ideology deeply rooted in the school system, in educational evaluation, and in professional life, where linguistic accuracy is often rewarded and deviation penalized. Digital grammar checkers have been meeting this demand for decades, but large language models have expanded the possibilities dramatically. With a single prompt, users can obtain corrections of grammar, spelling, and style in seconds, free of charge, and without access to a human proofreader. In classrooms where a teacher may not have time to correct thirty essays, outsourcing the task to an AI system is becoming an everyday reality. The researchers illustrate this with a real case: a pupil was asked to write a short text and have it corrected by an AI because the teacher lacked time. That single classroom decision, they note, already signals to students that AI corrections deserve the same trust as a teacher’s red pen.

Yet what exactly is corrected, how, and with reference to which resources remains largely unexplored. This matters because less linguistically informed users are the most likely to accept AI-generated corrections uncritically. Previous studies have produced worrying hints: AI systems may give more corrections than human editors, sometimes over-correcting perfectly acceptable language, sometimes producing fluent but misleading fixes, and occasionally introducing fresh grammatical errors into edited text. One recent classroom study found that German learners more readily adopted colloquial forms when they believed an AI had generated them. The apparent simplicity of AI-based correction, Dovalil and Hanulíková argue, conceals a far more complex sociolinguistic question.

To answer it, the authors first rethink what standard language actually is. Against the purely descriptivist view, in which a variant counts as standard when it appears frequently in corpora and model texts, they draw on Ulrich Ammon’s model of the social forces that determine what is standard in a language. In this framework, standard varieties are not fixed properties of a language but sociolinguistic concepts negotiated among four groups of actors: codifiers such as the authors of dictionaries and grammars, linguists, model speakers and writers, and norm authorities, defined as persons whom other language users expect to correct their language production. Crucially, this expectation must be legitimate. When a user addresses a grammatical doubt to another person, they expect trustworthy and reliable information in return. The question is whether an LLM can fulfill that role, and the answer, the authors insist, cannot be read off from statistics alone.

Herein lies a subtle technical point about norms. Frequencies, distributions, and regularities are often called norms in the broad sense, but normative validity and the factuality of language use must be strictly separated. The assumption that actual usage automatically confers normative force does not stand the test: regularities may arise from language norms but also from many other sources, so the inference from pattern to prescription is not compelling. LLMs, trained on vast collections of human-written text and tuned through preference optimization and post-training alignment, ultimately generate their linguistic behavior from statistical learning rather than explicit grammatical codification. Their transformer architectures encode words as vectors, operating on the premise that words appearing in similar contexts have similar meanings. When such a model corrects a text, it is pattern matching, not metalinguistic reasoning in the informed sense, and recent cognitive science suggests the two can dissociate. Worse, the researchers note a feedback loop: AI-generated metalinguistic responses become part of the future linguistic environment on which models are trained, a self-referential production of language use that the authors compare to autopoiesis, in which the boundary between language use and standard norm grows increasingly blurred.

To move from theory to evidence, the team designed an empirical test in German, a language rich in well-known cases of grammatical doubt. They constructed a short fiction story containing thirteen targeted variants across eight grammatical variables, seven inflectional and one syntactic. The variables included the case government of the prepositions wegen and gegenüber, where codification recommends genitive and dative respectively; the declension of demonstrative pronouns in genitive constructions such as das Leben deren versus derer and Ende diesen versus dieses Monats; parallel declension of consecutive attributive adjectives in noun phrases without articles, as in mit großem finanziellen Aufwand; declension of adjectives following the pronoun alles; the uninflected language name in im modernen Irisch; the weak masculine noun Präsident after a preceding genitive, as in mit Irlands Präsidenten, where a trend toward reduced declension collides with the absence of a declension trigger; and word order in um…zu constructions with verba dicendi, the so-called Echo-Satz pattern. Corpus frequencies were examined for every variant pair, mostly aligning with the codified form, sometimes strongly, as in wegen dem versus des at 93 to 3779, though one variant, mit großem finanziellen Aufwand versus finanziellem, diverged at roughly 3 to 1 in favor of the non-codified form.

The same text was then submitted to six proofreading and LLM tools in eight model configurations: ChatGPT 5.5, Copilot M365, Gemini Flash and Flash-Lite, DeepL Write in corrections-only mode, Claude Sonnet 5 in medium and Max reasoning effort settings, and Perplexity. Free, non-subscription versions were used, memory functions were disabled, and each tool corrected the identical text at three time points spaced five days apart in July 2026. The prompt framed the text as a school assignment and asked for grammar corrections only, treating the models exactly as users treat a norm authority.

The results were striking. Correction behavior varied considerably both between models and within individual models over time, and no model corrected all targeted phenomena at any time point. Correction rates ranged from 8 percent for DeepL to 77 percent for Gemini Flash at the first time point, or 85 percent once reformulations were added. No model showed improvement over time. Some configurations fluctuated wildly, with Gemini Flash sliding from 77 to 62 to 38 percent across the three sessions, while others remained stable, such as Claude Max reasoning at 69 percent. Except for Claude and DeepL, all models also produced a third type of behavior, reformulating rather than directly correcting or leaving a targeted construction untouched. Only one variant, wegen dem corrected to wegen des, was managed consistently in all instances, whereas one, mit Irlands Präsidenten, was never corrected by any model. Duplicate occurrences of the same variant were always handled identically, but lexical realizations of the same underlying rule diverged: while mit Irlands Präsidenten went uncorrected, mit Deutschlands Präsidenten was corrected in 12 percent of cases. Most tellingly, the syntactic um…zu variant was corrected only twice despite corpus data showing a strong preference for the corrected word order at 1 to 283. Correction behavior, the authors conclude, is not solely a function of model capability or reasoning effort and cannot be reliably predicted across repeated queries, undermining the intuitive assumption that repeated use of the same tool yields increasingly consistent corrections.

Approximately half of the corrections complied with codification and model texts, a level of limited consistency that falls short of what norm authorities are expected to deliver. The authors are careful not to overstate the indictment: human correction practices among teachers and linguistic experts are themselves heterogeneous, so LLMs simply add another set of inconsistent instances to an already pluralistic landscape. Interviews conducted by the researchers with laypeople and linguists reveal sharply different usage patterns. Laypeople trust AI opinions somewhat more, treating the models as norm authorities and citing the sheer amount of training data as a guarantee of quality, whereas linguists remain more careful before implementing AI-designed adjustments. This shows, the authors write, that LLMs may be turned into a norm authority provided a user does so. Whether that transformation sticks may depend on a phenomenon known as algorithm appreciation, the finding that people sometimes adhere more to advice they believe comes from an algorithm than from a human.

The crucial issue for future research, the authors conclude, is therefore not whether LLMs can manage standard language, but whether statistical text generators should acquire the social legitimacy traditionally granted to human norm authorities. They sketch conditions under which that legitimacy might be earned: if models can be shown to draw transparently on knowledge codified by reliable sources, dictionaries, grammars, expert linguistic literature, or even correction practices completed by human authorities, they could be treated as an autonomous norm authority. Until then, the researchers recommend AI as a powerful proofreading supplement to, not yet a replacement for, human judgment on standard-related issues. Answering the legitimacy question, they stress, cannot be done through benchmark evaluations alone; it requires integrating sociolinguistic theory, language ideology, and studies of human-AI interaction. The red pen, in other words, has not been retired. It has merely acquired a new, statistical rival whose authority rests on trust that has not yet been justified.

Subject of Research: Language management in the age of LLMs: AI tools in proofreading as emerging norm authorities?

Article Title: Language management in the age of LLMs: AI tools in proofreading as emerging norm authorities?

Article References: Dovalil, V., & Hanulíková, A. (2026). Language management in the age of LLMs: AI tools in proofreading as emerging norm authorities?. Frontiers in Language Sciences, 5, Article 1957524. https://doi.org/10.3389/flang.2026.1957524

Image Credits: AI Generated

DOI: 10.3389/flang.2026.1957524

Keywords: large language models, LLMs, proofreading, standard language, norm authority, language management, grammar correction, sociolinguistics, AI trust, German grammar, Language, management