What Makes Text Look ‘AI-Generated’ to Detection Algorithms?

Introduction

A grammar checker can improve clarity, but many researchers and students worry their submissions might be flagged as AI-generated even when written legitimately. Detection systems do not read meaning the way humans do. They analyze statistical and stylistic signals in text. This article explains those signals, why they matter for academic and technical writing, and what you can do to ensure your writing reads as human, honest, and publication ready. You will learn what AI content detectors look for, common failure modes, concrete before/after examples, and practical checks you can apply now.

What detectors actually measure (what)

Detection algorithms use one or more of these signal types:

• Token-level predictability (perplexity and probability). Detectors often measure how predictable tokens are under a language model. Text that consistently contains highly probable tokens (low perplexity) or follows narrow probability peaks can look machine-generated to some detectors. (Reference: Wikipedia, Perplexity)

• Probability curvature and model-specific signatures. Some methods examine how a model’s log-probability landscape behaves around a passage. For example, DetectGPT uses curvature measures, showing that sampled model text tends to sit in regions with certain probability properties. These are technical, model-aware signals detectors exploit. (Reference: arXiv, DetectGPT, 2301.11305)

• Stylometric and linguistic features. Algorithms use cues such as function-word frequencies, sentence length distributions, punctuation patterns, repeated n-grams, syntactic dependency patterns, and lexical richness. Combining multiple linguistic features often improves detection accuracy. (Reference: Computational Linguistics / MIT Press survey)

• Surface repetition and overuse of common phrases. Generated text can repeat high-probability phrases, templates, or close paraphrases. Detectors may flag unusually repetitive phrasing or identical high-frequency n-grams.

• Watermarks and provenance markers. Some models can embed detectable watermarks in tokens. Detectors that know the watermark scheme can find those traces. Watermarking is a different approach but is increasingly discussed in research and policy. (Reference: arXiv, 2301.11305)

Why these signals matter for academic writing (why)

Detectors were built to distinguish statistical regularities of model outputs from human variability. For academic writers, legitimate practices such as concise, formulaic language, repeated methodological phrases, or highly standardized reporting can mirror the regularity detectors expect from models and raise false positives. Conversely, aggressive paraphrasing or editing pipelines can remove human traits and produce false negatives. Research shows detectors are useful but imperfect and vulnerable to simple transformations and domain shifts. (Reference: arXiv, 2303.11156)

How detectors fail (common mistakes and limits)

• False positives when human text is regular. Highly formal or formulaic texts (legal contracts, checklists, or classical documents) can score as machine-like. Reported examples include historic documents incorrectly labeled. (Reference: University of Maryland CS article)

• False negatives from paraphrase or post-editing. Recursive paraphrasing or human revision can reduce detector accuracy. Adversarial edits and paraphrasers can change statistical patterns without changing meaning. (Reference: arXiv, 2303.11156)

• Domain shift and model evolution. Detectors trained on older model outputs or specific genres may not generalize to new model releases or technical subfields. (Reference: MIT Press survey)

• Overreliance on single metrics. Tools that report only one score can mislead. Robust assessments combine multiple features and contextual review. (Reference: International Journal for Educational Integrity, 2023)

When to be concerned (when)

Be cautious when:

• A high-stakes decision depends on a detector result (university misconduct hearings, manuscript rejection, or legal disputes). Detector output should not be the sole evidence. (Reference: University of Maryland CS article)

• Your text is highly formulaic, heavily templated, or compressed into short, uniform sentences (common in some methods sections).

• You used AI tools to draft or heavily rephrase parts of a manuscript and lack provenance or revision logs.

Practical ways to make your writing authentically human and robust for reviewers (how + tips)

These strategies are framed as writing improvement, helpful for clarity and publication readiness.

1. Vary sentence rhythm and length
   Mix short declarative sentences with longer sentences that show logical connections. Sentence length variance is a cue of human cadence, and overly uniform sentence lengths can look suspicious.

Example:
Before (AI-like): “We evaluated the system. The system performed well. The results were significant.”
After (human): “We evaluated the system using a cross-validation protocol and found it performed well; overall, the results were statistically significant and consistent across folds.”

2. Show process and uncertainty
   Include methodological detail, conditional statements, and appropriate hedging (for example, may suggest, in this dataset) to reflect scientific reasoning rather than overconfident summaries.

3. Cite and contextualize
   Embed citations, dataset names, and concrete numbers. Referencing a discipline-specific study, method, or dataset adds grounding that generic phrasing lacks.

4. Use discipline-specific terminology and structure
   Field-specific phrasing, defined acronyms, and conventional organization (Introduction to Methods to Results to Discussion) increase authenticity for reviewers.

5. Edit for voice and nuance
   Remove formulaic boilerplate and insert authorial nuance such as why you made choices, limitations you observed, and next steps.

6. Use tools for informed revision
   Grammar and style checkers help refine clarity and reduce accidental regularities. Tools like Trinka’s Grammar Checker assist with academic grammar and consistency, while Trinka’s AI Content Detector can give a quick signal about machine-like patterns. Use these as diagnostic supports, not definitive adjudicators.

Before/after example (academic paragraph)

Before (more likely to be flagged):
“In this study, we developed a model to improve performance. The model achieved higher accuracy than baselines. These results demonstrate the utility of our approach and suggest broader applicability across datasets.”

After (revised for human clarity and nuance):
“We developed a convolutional model that incorporates temporal attention to improve classification accuracy on biomedical time series. Compared with three baselines, our model increased mean accuracy by 4.2 percentage points (95% CI: 2.1–6.3) on the MIMIC-III subset. These results indicate potential gains in settings with irregular sampling, although further validation on prospective clinical data is necessary.”

Why this helps: the revised paragraph adds method detail, a numerical effect size and interval, a named dataset, and an honest limitation.

Best practices for academics (checklist)

• Keep a revision log: record when you used AI drafting tools and what you changed.

• Use multi-signal checks: combine grammar and style checks with human review and, if needed, an AI content detector to spot suspicious uniformity.

• Prefer discipline conventions: follow journal style and include method specifics, datasets, and citation context.

• When in doubt in high-stakes contexts, request human review from a supervisor, mentor, or an institutional writing center.

Mistakes to avoid

• Relying solely on a single detector score to make high-stakes decisions. Detectors are informative but not definitive. (Reference: arXiv, 2303.11156)

• Overediting to sound human by adding irrelevant filler. This can harm clarity and reviewer trust.

• Omitting provenance when you did use AI drafting. Transparent disclosure to coauthors, supervisors, or journals is best practice.

Conclusion

Detectors look for statistical regularities such as predictability, probability patterns, repeated templates, and stylistic fingerprints rather than intent. For academic writers, the solution is not to chase a detector score but to write with clarity, disciplinary grounding, methodological detail, and honest nuance. Use diagnostic tools (for example, an academic grammar checker for language and consistency and an AI content detector for an additional signal) to improve readability and identify potential issues before submission, and pair automated checks with human review.