AI-generated and AI-assisted CVs are now part of everyday recruitment. For recruiters and hiring teams, the challenge is no longer simply spotting whether AI was used but understanding what that use means for candidate screening. As more applications look polished, generic, or strikingly similar, recruiters face a new challenge: distinguishing low-effort submissions from strong candidates who used AI to present their experience more clearly.
In this article, we look at how recruiters can assess AI-influenced applications and make decisions with greater confidence and fairness.
The dilemma of recognising AI-assisted CVs
For hiring teams, the first thing to remember is that writing style alone is not reliable evidence of AI use. A polished CV or cover letter may have been generated with AI, but it may also have been written by a candidate and then refined with AI for clarity and formatting. That distinction matters.
Broadly, we can group AI-influenced applications into two categories:
- Generic, low-effort submissions;
- Candidate-written submissions edited with AI assistance.
The first category is easier to spot: a cover letter may contain placeholder text, repeat the job ad, or make broad claims without showing any experience.
The second category is much harder to assess. A candidate may have written the contents themselves and used AI to improve structure, wording, or formatting. The result can be almost identical to an application written by someone with strong writing skills.
That is why an overly polished application is not an automatic red flag. What matters is whether the candidate can support the claims they make in their application.
What AI-assisted applications have in common
AI-written applications often lack lived experience and individual nuance. Instead, they highlight experience through buzzwords that align with the job description.
AI can combine a CV and a job description to create the impression of relevant experience. What it cannot provide is the candidate’s personal experience. Personal examples and clear explanations of how the candidate handled situations are stronger signs that they actively contributed to the application.
Inconsistency is another indicator of AI involvement. If the candidate does not provide enough information to the AI creating their application, mismatches can appear between the experiences listed in the CV and the cover letter. Placeholder text can also appear in the final application if it is sent out without proofreading.
These details can help recruiters distinguish thoughtful, AI-assisted applications from quick, low-effort ones.
Look at the writing style, but don’t rely on it
AI tools can have recognisable formatting and writing habits. They may overuse certain punctuation, such as:
- Long em dashes ( — compared to normal dashes - );
- Excessive use of semicolons;
- Excessive use of colons in titles and to break up text;
- Typographic quotation marks (“” as opposed to "").
Some of these, such as frequent use of em dashes, may be more noticeable than others. However, none of these signs can prove that AI was used. Recruiters should treat stylistic choices as clues, not evidence, and focus on whether the candidate can support their claims.
Language choices can also reveal a lot. AI tools often favour strong action verbs to describe job tasks, making minor tasks look like major achievements. Strong verbs have their place, but not every task merits a "spearheaded", "drove" or "achieved".
If every bullet point sounds equally impressive, look for evidence behind those claims.
How to screen applicants on the truthfulness of their application
When it is difficult to assess a candidate from their CV alone, a brief initial screening can be highly effective.
Targeted questions about a listed skill or experience quickly reveal whether the candidate can explain the work behind the claim. Someone who has genuinely done the work can describe the context, the choices they made, and the tools they used. Someone with inflated claims will struggle to stray far from the words on the page.
Tests, work samples and practical exercises can also help recruiters assess capability directly. A set of structured questions at the start of the application process can help filter out generic applications early on.
A genuine claim on a CV should be supported by a situation, an action, and a clear outcome. Asking what the candidate learned from the experience can provide another layer of insight.
In a noisier, increasingly AI-enabled job market, referrals and references provide another source of information. A candidate who comes through a trusted recommendation already has someone willing to vouch for their skills and professional experience. That doesn’t remove the need for proper assessment, but it can give recruiters another way to verify applications that look strong on paper.
Ultimately, the goal is not to detect whether AI was used, but to assess whether the person behind the application has the experience and skills they claim. Look beyond the polished wording: ask questions, verify claims and give candidates the opportunity to demonstrate what they can actually do.
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