Detect AI-Generated Text: Wikipedia’s Practical Guide

The rise of large language models has made it increasingly difficult to tell human-written prose from machine-generated content. Community editors and researchers have identified recurring patterns and habits that help distinguish AI-written text. This article synthesizes those practical signals into an evidence-based checklist you can apply to articles, bios, and web copy.

Why detecting AI-generated text matters

Identifying synthetic text is important for trust, credibility, and content quality across journalism, encyclopedias, corporate sites, and platforms that depend on accurate attribution. When machine-generated copy is indistinguishable from human writing, readers and editors lose a key signal about sourcing, perspective, and accountability. Detection helps:

• Preserve editorial standards and factual accuracy.
• Prevent inadvertent amplification of biased or fabricated claims.
• Protect reputations by flagging unauthoritative bios or promotional copy.

Community guidelines developed by experienced editors offer practical, scalable heuristics that go beyond unreliable automated detectors.

What patterns point to AI-written prose?

Successful detection relies on stylistic and structural signals rather than single “tell” words. Below are patterns commonly flagged by editors and analysts that reliably surface in AI-generated text.

1. Overemphasis on importance in generic terms

AI-written passages often spend disproportionate space explaining why a subject is important using vague phrases: “a pivotal moment,” “a broader movement,” or “continues to underscore the significance.” These constructions sound persuasive but lack precise sourcing or concrete metrics.

2. Present-participial constructions with hazy claims

Look for trailing clauses built around present participles — phrases like “emphasizing the significance,” “highlighting the continued relevance,” or “reflecting ongoing trends.” These structures can inflate perceived importance without adding verifiable detail.

3. Promotional or marketing language

Generic promotional adjectives are another common fingerprint. Phrases such as “scenic landscape,” “cutting-edge,” or “state-of-the-art” often appear where neutral, sourced descriptions would be expected. Editors describe this tone as “like a TV commercial.”

4. Overly broad event lists and minor media citations

AI outputs can attempt to build notability by enumerating minor appearances or low-signal media mentions as if they were independent validation. These lists may include trivial appearances that do not establish authority.

5. Flattened sourcing and paraphrase-like references

Machine-written copy frequently paraphrases secondary sources without clear attribution, producing passages that seem well-phrased yet lack original reporting or unique evidence.

How can you tell if text was written by AI?

(Featured-snippet style answer)

Quick test: scan for vague importance claims, present-participial framing, promotional adjectives, long lists of minor mentions, and a lack of primary-source citations. If multiple signs appear together, the piece is likely AI-generated.

1. Check tone: Is it promotional or neutral?
2. Scan for present-participial hedges: “highlighting,” “emphasizing,” “reflecting.”
3. Look for lists of low-evidence mentions or citations without primary sources.
4. Verify factual claims against reliable sources.

Step-by-step checklist to evaluate suspicious prose

Use this actionable checklist when reviewing an article or submission:

• Tone audit: Is language generic, promotional, or emotionally amplified?
• Grammar patterns: Are present-participial constructions abundant?
• Sourcing check: Are claims linked to primary, independent references?
• Evidence density: Are specific dates, places, and direct quotes present?
• Uniqueness test: Does the text recycle common internet phrasing or unusual collocations found on many sites?

Apply each step and score the piece. A cumulative score across categories gives a practical likelihood that the prose is synthetic.

Examples of flagged language (and how to rewrite it)

Below are anonymized examples, the identified problem, and a more robust rewrite.

• AI-style: “This development marks a pivotal moment, reflecting the continued relevance of modern innovation.”

  Issue: Vagueness and inflated importance without evidence.

  Rewrite: “The change coincided with a 24% increase in adoption among mid-sized firms between 2022 and 2024, according to a sector survey.”

• AI-style: “The company boasts a scenic campus and state-of-the-art facilities.”

  Issue: Promotional adjectives without relevance to notability.

  Rewrite: “The firm reports a 10% year-over-year productivity gain after adopting the new systems, per its 2024 operational report.”

Why automated detectors fall short

Automated detection tools can produce false positives and false negatives because they often rely on statistical patterns tied to training data distribution. Skilled editors recommend human review focused on context, sourcing, and editorial intent. The combination of human judgment plus heuristic signals is far more reliable than any single algorithmic classifier.

Integrating manual review with lightweight tooling

Practical workflows combine:

• Automated triage to surface high-risk edits.
• Human pattern evaluation for tone, grammar, and sourcing.
• Cross-checks against authoritative references and archives.

This approach scales because it keeps humans in the loop where nuance matters most.

What this means for publishers and editors

Editors should prioritize clear sourcing, require primary references for claims of importance, and train reviewers to spot the stylistic fingerprints listed above. For organizations that accept contributions, establishing transparent policies and providing examples of acceptable vs. unacceptable phrasing reduces ambiguity and improves moderation speed.

For readers, media literacy that emphasizes source verification and skepticism toward promotional phrasing helps maintain high information quality online.

Related reading and internal resources

For deeper context on community approaches and content governance, see our coverage of editorial frameworks and platform responses:

• Wikipedia AI Guidelines: How Wikimedia Protects Content — an overview of policy and practical steps platforms use to manage synthetic edits.
• The Future of Wikipedia: Navigating Challenges — broader analysis of editorial trust and platform governance.
• Is the LLM Bubble Bursting? What Comes Next for AI — strategic context on the rapid development of language models and deployment risks.

Practical recommendations for content teams

Teams should update editorial checklists and onboarding materials to include AI-detection heuristics. Recommended operational changes:

1. Require citation of primary or independent secondary sources for claims of significance.
2. Introduce a short style guide section on promotional vs. neutral tone.
3. Train moderators to flag present-participial hedges and long lists of minor mentions.
4. Maintain a transparent log of suspected synthetic submissions and outcomes.

These shifts reduce the long-term editorial burden while protecting against misinformation and reputation harm.

Limitations and future directions

As models evolve, they’ll mimic human idiosyncrasies more successfully. That makes ongoing community-driven research and shared evidence bases essential. Editors and researchers should keep publishing annotated examples and maintaining living guides that adapt as stylistic footprints change.

Conclusion

Detecting AI-generated text is less about spotting single words and more about recognizing repeated stylistic habits: vague claims of importance, present-participial hedges, promotional adjectives, and lists that substitute for evidence. Combining a clear checklist with human judgment and selective tooling offers the best path forward for editors, publishers, and platforms aiming to preserve trust and accuracy.

Ready to evaluate your content?

Use the checklist above on your next article or submission. If you manage editorial workflows, update your contributor guidelines and moderation training to include these patterns. Need help auditing your content strategy or training teams to spot AI-written prose? Contact our editorial consulting team to schedule a review and practical workshop.