Textual Features Alone Are Not Reliable Indicators of AI or Human Authorship

1.1. The Problem of Inferring Authorship from Writing Characteristics

The increasing use of Large Language Models (LLMs) in academic and professional writing has generated considerable interest in determining whether a document was written by a human, generated by AI, or produced through some form of human–AI collaboration. Such judgments are often made from observable features of the completed text. Highly organized structures, consistent terminology, systematic argumentation, balanced explanations, regular paragraph patterns, explicit qualifications, and clearly formulated conclusions, for example, are sometimes interpreted as indications of LLM generation.

Such interpretations require caution. The occurrence of characteristics commonly observed in LLM-generated text does not establish that those characteristics originated with LLMs, nor does their presence necessarily constitute strong evidence of AI authorship. Many features now associated with AI-generated writing were established conventions of academic, scientific, technical, and professional communication long before generative AI became widely available. A fundamental distinction must therefore be maintained between characteristics of writing and evidence of authorship. A characteristic can describe how a document is written without reliably identifying the process by which it was produced.

1.2. Writing Structure and Authorship Are Different Dimensions

Writing structure concerns the organization and presentation of content; authorship concerns the process through which that content is intellectually developed and textually produced. The two dimensions may be related, but the former does not directly determine the latter.

A theoretical article, for example, may introduce a problem, define important concepts, establish relationships among them, develop propositions, identify qualifications, examine implications, and conclude by integrating the argument. Such organization may be highly systematic precisely because systematic organization is appropriate for communicating a theoretical framework. Human researchers have long relied on explicit organization, logical progression, stable conceptual definitions, recurring terminology, and synthesis to make complex arguments intelligible.

The ability of LLMs to produce similarly organized articles therefore does not make structural organization an inherently AI-specific characteristic. In particular, the proposition LLMs frequently produce highly structured writing must be distinguished from the substantially stronger proposition highly structured writing is likely to have been produced by an LLM. The first proposition may be valid without providing sufficient grounds for accepting the second.

1.3. The Human Origins of Many LLM Writing Patterns

LLMs learn statistical, linguistic, rhetorical, and structural regularities from large collections of textual material, much of which ultimately derives from human-produced writing. Books, academic articles, reports, educational materials, technical documents, professional communications, and other forms of written discourse contain recurring practices for defining concepts, organizing arguments, explaining relationships, qualifying claims, and integrating conclusions. LLMs can learn and generate text consistent with these patterns, but their capacity to do so does not make the underlying patterns intrinsically characteristic of AI.

At a general level, this relationship can be represented as Human Writing Practices → Written Corpora → LLM Learning → Generated Writing. Similarities between high-quality human writing and LLM-generated writing are consequently unsurprising: both may exhibit conventions that are extensively represented in established written discourse. This produces a fundamental attribution problem. If skilled human writers and LLMs are both capable of systematic exposition, the observation of systematic exposition in a completed article cannot clearly distinguish between them. What the similarity demonstrates most directly may be an LLM’s capacity to generate text consistent with established writing practices, rather than evidence that the practice itself is indicative of AI authorship.

1.4. Good Writing Characteristics Are Not Necessarily AI Characteristics

Many characteristics sometimes identified as signs of AI-generated academic writing are also conventional objectives of competent academic writing. Logical organization, clear definitions, consistent terminology, coherent transitions, explicit conceptual relationships, appropriate qualification of claims, consideration of exceptions, and conclusions supported by preceding arguments are examples.

A progression such as Define → Explain → Qualify → Integrate, for instance, provides a straightforward means of presenting a theoretical concept. The concept is first defined; its meaning and significance are then explained; relevant conditions or limitations are identified; and the concept is subsequently connected to the broader argument. LLMs frequently generate explanations that follow this progression, but the progression itself reflects an established method of exposition. The same reasoning applies to structures such as General Concept → Component Concepts → Relationships → Implications. Their occurrence in AI-generated text does not establish an AI origin because their usefulness as organizational structures predates contemporary LLMs.

Such characteristics are therefore more directly informative about the organization, clarity, coherence, and rhetorical construction of a document than about its authorship. Their relevance to authorship attribution must be established separately.

1.5. The Problem of Inverted Causal Reasoning

Authorship judgments become methodologically weak when the relationship between established writing conventions and LLM-generated writing is implicitly reversed. A simplified inference might proceed as follows: LLMs often generate highly structured articles → this article is highly structured → this article was probably generated by an LLM. The difficulty is that this reasoning does not adequately explain why structured writing appears in LLM output in the first place.

A more complete representation is Established Human Writing Conventions → LLM Learning of Those Conventions → LLM Reproduction of Structured Writing. When a characteristic is well established in human writing and can also be generated by LLMs, its presence alone has limited capacity to distinguish between the two production processes.

This does not render structural characteristics irrelevant to authorship assessment. Their evidential value instead depends on whether, and to what extent, they differentiate among competing explanations of textual production. The observation that a characteristic frequently occurs in AI-generated writing is not sufficient if the same characteristic also occurs frequently in comparable competent human writing.

1.6. Shared Characteristics and Discriminative Value

An authorship indicator is useful to the extent that it distinguishes among competing explanations of how a text was produced. A characteristic that occurs frequently in AI-generated writing but rarely in comparable human writing may provide meaningful evidence. By contrast, a characteristic common to both has substantially weaker discriminative value.

Authorship assessment can therefore be expressed conceptually as Authorship Evidence ≠ Mere Presence of a Characteristic. The more relevant question is how strongly an observed characteristic distinguishes among plausible authorship processes. If experienced researchers and advanced LLMs both commonly produce clear definitions, systematic arguments, explicit qualifications, and logically organized sections, these characteristics may indicate competent exposition while providing insufficient information to identify whether the text was produced by a researcher, an LLM, or some combination of the two.

As LLMs become increasingly capable of generating text that conforms to sophisticated human writing conventions, the observable overlap between human and AI textual characteristics may expand. Where that overlap increases, characteristics shared extensively by both become correspondingly less useful as independent indicators of authorship.

1.7. Theoretical Writing and Structural Regularity

The genre, purpose, and conceptual requirements of a document must also be considered when structural regularity is interpreted. Theoretical writing ordinarily requires consistency among concepts, definitions, propositions, and relationships. If researchers establish a conceptual hierarchy such as Occupation → Task → Subtask → Skill, subsequent sections should preserve those relationships. Similarly, a framework organized around a process such as Requirements → Skill Selection → Execution → Evaluation → Reallocation may require repeated reference to the same concepts as different conditions and applications are examined.

Structural regularity may consequently arise from the theory being communicated rather than from the mechanism used to produce the prose. Repeated terminology, parallel explanations, progressive development, and systematic synthesis can all result from the need to preserve relationships established within a framework. Attributing such regularity primarily to AI generation therefore risks explaining in terms of the writing mechanism what may be more directly attributable to the conceptual structure of the subject itself.

1.8. Terminological Consistency and Formulaic Generation

Terminological consistency should likewise be distinguished from formulaic textual generation. General writing advice may encourage lexical variation, but theoretical and technical writing often requires stable terminology when particular expressions designate specifically defined constructs. If task, subtask, skill, human skill, agentic skill, and skill allocation refer to distinct concepts within a theoretical framework, their repeated use can preserve conceptual precision. Substituting alternative expressions merely to create lexical variety could introduce ambiguity or imply distinctions that the framework does not intend.

Conceptually necessary terminological repetition is therefore different from unnecessary repetition of sentence structures, transition phrases, paragraph templates, or rhetorical constructions. The former may be a requirement of rigorous theoretical writing; the latter may provide contextual information about how a text was produced. Even substantial formulaic regularity, however, cannot independently establish AI authorship. Standardized human writing procedures, institutional templates, editorial guidelines, collaborative practices, and deliberate stylistic choices can produce comparable patterns.

1.9. Structural Regularity as Contextual Evidence

Structural characteristics may nevertheless contribute to authorship assessment when treated as contextual and probabilistic evidence rather than decisive indicators. Unusually uniform paragraph lengths, nearly identical rhetorical sequences across numerous sections, recurring transition constructions, unnecessary restatement of conclusions, or equivalent levels of elaboration for concepts of substantially different importance may increase the plausibility of templated or AI-assisted production.

Such characteristics become more informative when the degree of regularity exceeds what can reasonably be explained by the subject matter, theoretical framework, publication format, or disciplinary convention. Even then, alternative explanations need to be considered. Researchers may deliberately employ standardized article structures, work from institutional templates, follow controlled writing procedures, collaborate through systematic editorial processes, or impose explicit requirements for consistency across sections. Structural regularity should therefore receive evidential weight only after plausible alternative explanations have been considered. No individual structural characteristic is, by itself, sufficient to establish a particular authorship process.

1.10. Intellectual Origin and Textual Production

Human–AI collaborative writing introduces a further distinction between intellectual construction and textual construction. Intellectual construction concerns the origin and development of concepts, propositions, theoretical relationships, arguments, analytical decisions, and substantive conclusions. Textual construction concerns the formulation and refinement of sentences, paragraphs, sections, transitions, and other forms of linguistic expression.

These activities can be distributed differently between humans and AI systems. Researchers may formulate an entire theoretical framework, establish its concepts and relationships, determine its propositions, and then use an LLM to produce an initial textual explanation that they subsequently review, correct, reorganize, and approve. In another workflow, researchers may write the manuscript themselves and use an LLM for linguistic refinement, restructuring, condensation, expansion, or stylistic editing.

These production processes differ substantially even though the completed documents may display similar observable characteristics. An article can contain considerable AI-generated prose while remaining strongly human-directed in its intellectual construction. Conversely, the fact that sentences were written directly by a human does not by itself establish that every underlying conceptual contribution originated with that writer. Authorship assessment therefore becomes more precise when intellectual and textual contributions are examined separately rather than treated as a single undifferentiated category.

1.11. Human–AI Writing as a Continuum

Contemporary writing processes are more accurately represented as a continuum than as a binary distinction between human-written and AI-written text. Possible configurations include fully human drafting, human drafting followed by AI editing, iterative human–AI co-writing, AI drafting from a detailed human-developed framework followed by substantial human revision, and predominantly AI generation with comparatively limited human intervention.

This continuum can be represented as Human Writing ← Human-Led AI Assistance ← Iterative Human–AI Construction → AI Drafting with Human Refinement → Predominantly AI Generation. Observable characteristics of the finished document may not reveal its exact position on this continuum. A heavily revised AI draft can acquire characteristics associated with human academic writing, whereas a highly standardized human manuscript may display characteristics commonly attributed to LLMs. Likewise, an LLM working from detailed researcher-developed instructions may reproduce a predetermined theoretical structure with considerable consistency.

The completed document consequently provides only partial evidence about the sequence of human and AI contributions, their relative importance, and the processes through which the text was ultimately produced.

1.12. Production Provenance and Stylistic Inference

When the objective is to determine how an academic article was actually produced, direct evidence of the production process is generally more informative than stylistic interpretation of the completed document. Manuscript versions, tracked revisions, drafting histories, research notes, prompts, AI interaction records, document metadata, author correspondence, version-control records, and explicit descriptions of writing procedures can provide evidence directly related to textual provenance.

Stylistic analysis operates differently: it attempts to infer production history from properties of the resulting text. As skilled human writers and advanced LLMs increasingly produce overlapping linguistic and structural characteristics, such inference necessarily involves substantial uncertainty. The evidential distinction can be summarized as Production Evidence → Stronger Basis for Authorship Attribution, whereas Textual Characteristics → Probabilistic and Context-Dependent Basis for Authorship Attribution.

Stylistic assessment can therefore contribute to an evaluation of possible authorship, but the strength of its conclusions should remain proportional to the discriminative value of the characteristics on which those conclusions depend.

1.13. A General Principle for Assessing AI Authorship

A general framework for evaluating possible AI authorship can be represented as Observed Characteristic → Alternative Explanations → Discriminative Value. First, an observable feature is identified, such as structural regularity, repeated transitions, conceptual consistency, uniform paragraph organization, or another linguistic pattern. Second, plausible explanations for that feature are considered. These may include AI generation, skilled human writing, disciplinary conventions, theoretical requirements, institutional templates, collaborative editing, or deliberate standardization. Third, the analysis considers whether the characteristic meaningfully distinguishes among those explanations.

Under this framework, a characteristic should not receive substantial evidential weight simply because it commonly appears in LLM-generated text. What matters is whether its occurrence differs sufficiently across plausible human and AI production processes to support attribution. This principle is especially important when evaluating logical organization, conceptual consistency, clear definitions, systematic argumentation, explicit qualification, and similar characteristics that are both common outputs of advanced LLMs and established objectives of high-quality human academic writing.

1.14. Structural Quality as Evidence of Writing Practice Rather Than Authorship

The increasing ability of LLMs to generate text consistent with sophisticated writing conventions makes it important to distinguish between the origin of a writing characteristic and its reproduction by AI. Characteristics historically developed through human academic and professional writing can now appear in texts produced by either humans or computational systems. Their reproduction by LLMs does not, in itself, transform them into AI-specific characteristics.

The distinction can be summarized as follows: A characteristic reproduced by AI is not necessarily a characteristic originating from AI. Highly structured exposition, logical progression, terminological consistency, conceptual qualification, systematic synthesis, and explicit relationships among concepts may therefore be interpreted more appropriately, in the first instance, as characteristics of particular writing practices or standards of exposition rather than as indicators of a particular author type. They become relevant to authorship attribution only to the extent that additional evidence shows the observed pattern to be more consistent with one production process than with plausible alternatives.

1.15. Conclusion

Attributing AI authorship from academic writing requires a clear distinction between what a document looks like and how it was produced. LLMs frequently generate highly structured, logically consistent, systematic, and carefully qualified prose. These characteristics, however, are not unique to LLMs, and many reflect established conventions of human academic and professional writing represented extensively in the textual materials from which LLMs learn. It is therefore insufficient to infer AI authorship primarily from the observation that an article is highly organized, consistently follows a theoretical logic, repeatedly uses defined terminology, or systematically develops its propositions. In theoretical research, the same characteristics may arise from disciplined conceptual construction, academic writing conventions, terminological precision, or requirements imposed by the framework itself.

Structural and linguistic characteristics can contribute to probabilistic authorship assessment, particularly when multiple unusual patterns occur together and cannot be adequately explained by genre, subject matter, editorial practice, or deliberate standardization. Their evidential significance nevertheless depends on their capacity to distinguish AI production from plausible human alternatives. A more appropriate assessment therefore separates writing characteristics, intellectual construction, textual construction, and production provenance, while recognizing that human–AI writing encompasses numerous intermediate configurations rather than a simple human-versus-AI distinction.

The central theoretical principle can consequently be stated as follows: the reproduction of established human writing conventions by Large Language Models does not convert those conventions into reliable indicators of AI authorship. The more extensively a writing characteristic is shared by competent human writers and LLMs, the less that characteristic alone can distinguish between human and AI textual production.