In 2024, AI adoption became a defining trend among HR professionals. A staggering 87% of recruiters used AI throughout the recruiting process—especially for skill tests and assessments.
But today, a new concern is emerging: 24% of HR leaders are now seeking AI-resistant candidate assessment methods.
Why the shift?
This growing demand reflects HR professionals’ increasing awareness of AI’s broader implications
Most HR leaders we talk to are seeing a significant increase in application volume but a decrease in average candidate quality.
Tools such as ChatGPT make it easier for job seekers to polish, tailor, or even fully generate resumes — making it difficult to assess genuine candidate fit and raising the risk of a bad hire.
If you're serious about mitigating this risk, you're in the right place. This guide will show you:
- How candidates are using AI to build resumes
- The 11 tell-tale signs of AI-enhanced applications
- Why AI resume detectors aren’t as reliable as they seem
- How to implement AI-resistant candidate screening techniques
Let’s begin.
Want to detect candidates using AI assistance during an interview? Try Real Talk in Willo
How Applicants Use AI To Generate Resumes
Job seekers are increasingly using AI tools to enhance their resumes in several key ways:
- Overcoming personal writing challenges
- Generating initial drafts and outlines (in many cases, even full resumes)
- Optimizing for ATS keywords
- Evading AI detection tools
Before we get into the details of how each application manifests, you should understand that applicants use AI in resumes for two primary reasons:
- To scale their job search without burnout: Many candidates must apply to dozens—sometimes hundreds—of roles before landing a single interview. Writing a customized resume for each application is time-consuming and emotionally draining.
- To “level the playing field”: With over 80% of employers using AI to screen resumes, candidates see AI tools as a way to “level the playing field.” Their reasoning is simple: if employers use AI to filter candidates, it’s only fair for applicants to use AI in response.
With this mindset, the modern job seeker uses AI to write resumes in various ways:
To overcome personal writing struggles
Writing a strong resume requires a level of skill that many applicants simply haven’t developed. This challenge is often compounded by factors like limited work experience or non-native language proficiency.
Take, for example, an early-career applicant who isn’t familiar with industry-specific terminology and is also not a native English speaker. Framing their experience effectively for a hiring manager becomes significantly more difficult.
To bridge these gaps, many candidates turn to AI tools to help them:
- Improve grammar and sentence structure
- Clarify wording and make content more concise
- Adjust tone to sound polished, confident, and aligned with the role
But there’s a trade-off. Because applicants with limited proficiency may not recognize what reads as “off,” their AI-assisted resumes often end up over-polished and keyword-stuffed—raising red flags for recruiters and potentially masking genuine skill gaps.
To generate initial drafts and outlines
For many applicants, the hardest part of resume writing is getting started. Faced with a blank page, they turn to AI to generate an initial draft or basic outline to build on. They provide AI with input — such as their experience, skills, or a job description — and let the tool produce a rough draft they can expand and refine.
For keyword optimization
To improve their odds of passing Applicant Tracking Systems (ATS) filters, candidates often feed job descriptions into AI tools. The AI then rewrites their resume or cover letter to include the specific keywords, phrases, and “buzzwords” found in the posting.
The goal here is to match the language the ATS is trained to recognize so their resumes pass the screening process.
To pass AI detection
Some applicants have learned to identify the subtle signs of AI writing, like overuse of em dashes, awkward phrasing, or placeholder text (e.g., “CompanyName”). To avoid detection, they go back and edit these markers with AI.
In other cases, instead of revising AI-generated resumes, candidates design prompts that provide AI tools with as much information as possible to ensure the most accurate results. Some examples:
- “Act as a recruiter. Score my resume against this job description. Then, make it stronger.”
- “Here are my accomplishments at work. Use them to create a resume for X role based on this job description.”
This produces high-quality outputs that can almost pass for human-written content.
Another strategy applicants use to bypass AI detectors is invisible keyword stuffing. This involves hiding extra role-relevant keywords in a resume by using white text on a white background or shrinking the font size to a near-unreadable size.
Because this hidden text is human-written, it can dilute the patterns AI detectors use to flag machine-generated content, making the resume appear more human.
At the same time, the inflated keyword count helps the resume rank higher in Applicant Tracking Systems (ATS), even if the candidate lacks the requirements for the role.
Now that you understand how candidates use AI to create resumes, the clues will become easier to spot.
11 Tips for spotting AI-generated resumes
The following 11 tips are based on how candidates are using AI to write and refine their resumes.
1. Excessive length and redundancy
AI-generated resumes often sound impressive but say very little of real value.
Instead of getting straight to the point, they tend to over-explain simple ideas, repeat the same information in different words, or use filler words that add little value.
This happens because AI tools lack the nuance and real-world context to prioritize the information to include in outcome-based bullets. Rather, they provide the most comprehensive answer based on what’s in their training data.
As a result, a bullet point that could be a clear, one-line summary often gets stretched into three lines of vague, bloated text.
For example, instead of writing:
- Managed social media accounts and increased engagement by 30% across Instagram, LinkedIn, and TikTok.
An AI-generated version might say:
- Successfully leveraged cross-functional digital platforms to manage and oversee dynamic social media initiatives, resulting in enhanced audience engagement metrics and positive brand awareness growth.
Both describe the same task, but only the first gets to the point.
2. Overuse of buzzwords and corporate jargon
Human writers tend to naturally incorporate buzzwords and jargon within accomplishment-focused statements. Conversely, AI tools often overuse these terms, packing them into resumes in a way that feels forced and unnatural.
Why?
AI tools are trained on professional content from across the web—job descriptions, company websites, press releases, and industry blogs. So, they use such content as a reference point, adding buzzwords and jargon indiscriminately, even when simpler phrasing would suffice.
For example, instead of something direct like:
- Trained new hires on internal tools and workflows,
You might see:
- Facilitated knowledge transfer by executing comprehensive onboarding initiatives for cross-functional team members.
Here’s a short list of popular buzzwords and corporate jargon overused in AI-generated resumes:
3. Detailed descriptions that sound vague
AI tools are good at generating content that mimics the structure of a strong resume.
However, because they lack access to real-world context — such as the actual scope of the project or industry benchmarks — they often generate paragraphs that appear detailed but fail to explain what the candidate specifically did or achieved.
Take, for example, this resume for an operations lead role:
There are a lot of words. However, it doesn’t specify what the candidate did, whether it worked, or the results achieved. There’s no mention of measurable outcomes — no metrics, KPIs, or performance improvements tied to the initiative.
Now, compare that with this human-written version:
- Led a cross-team project that cut internal reporting time by 30% by automating data collection using Google Sheets and Zapier.
You see, the difference is clear as light and day. The human-written version is specific and clearly demonstrates the candidate’s impact.
4. Rambling and lack of clarity
You read a resume. The grammar is flawless, the sentences flow well, and the formatting looks polished. But by the time you reach the end of the paragraph — even after a second read — the meaning remains unclear.
That’s a common hallmark of AI-generated content.
Take, for example, this snippet from an AI-generated resume section:
- Successfully contributed to the overall objectives of the team by engaging in collaborative efforts aimed at delivering high-quality outcomes through strategic alignment of internal resources and consistent communication practices.
On the surface, everything appears to be in order; there are no grammatical errors, the content is logically connected, and it sounds coherent.
But you learn nothing specific. What was delivered? What role did the candidate play? What was the outcome?
If a human were to write the same section, it would read as:
- Coordinated weekly check-ins to improve team communication, helping complete projects 15% faster.
This version gets to the point. Just by scanning through, you can understand what the person did, how they did it, and what impact it had.
5. Inconsistent Voice & Tone
When candidates write some parts of their resume themselves and use AI to generate or heavily edit others, you might notice abrupt shifts in tone and writing style.
One section might sound natural and authentic, as if the candidate is genuinely sharing their experience, while another feels detached and overly formal.
Here’s a great example:
6. Generic accomplishments
Since AI lacks a direct connection with the real world, it produces vague, generic accomplishment statements that sound impressive but lack unique identifiers — such as metrics and concrete outcomes — tied to the candidate's experience.
These statements are so generic that they could apply to almost any resume—regardless of their role or company—and they would still make sense.
Here’s a side-by-side comparison of three generic and specific accomplishments:
Generally, if a resume bullet point could describe almost anyone in a similar role and still be accurate, it’s probably AI-generated.
7. Unusual phrasing or word choice
AI tools struggle to mimic the way people naturally write. As a result, AI-generated resumes often include awkward phrasing, unusual word choices, or sentences that just don’t sound quite right to a human reader.
Here’s a perfect example of a section of an AI-generated resume with unusual phrasing and word choice:
- Instrumentalized cross-functional synergies to holistically expedite operational cadence and optimize team output trajectories.
Some signs that this is AI-generated:
- There’s no clear subject, verb, or object.
- No one says 'instrumentalized' when they mean 'helped' or 'led'.
- You don't typically expedite cadence. Instead, cadence is maintained, set, or established. So the verbs and nouns don’t naturally belong together.
If a human were to write the same sentence, it would read:
- "Led cross-team efforts to improve workflow speed, helping the department complete projects 20% faster over six months."
8. A mismatch between experience and the level of detail
The level of detail in a resume should match the candidate’s actual experience. When there’s a mismatch—like an entry-level candidate describing projects in overly complex or inflated terms—it can be a sign the resume was generated or heavily edited by AI.
That’s because AI tools don’t know candidates’ experience level. They rely on patterns from training data and aim to produce content that sounds polished and professional, even if it exaggerates the scope or depth of the candidate’s real experience.
Let’s say you’re hiring for a marketing associate position with two years of experience in marketing. An AI-generated bullet point will say:
- Spearheaded the development and execution of a cross-functional go-to-market strategy to drive multi-channel brand growth and enhance customer acquisition across diverse digital touchpoints.
The language in this bullet point is unusually advanced for someone with just two years of experience.
Terms like “spearheaded,” “go-to-market strategy,” and “multi-channel brand growth” are typically used by senior professionals, not entry-level employees or junior team members.
9. Absence of personal anecdotes or unique stories
Human-written resumes often include subtle, real-life touches — like a specific challenge the candidate solved, a unique client project they led, or a key lesson they learned in the role. These details show lived experience.
AI, on the other hand, can’t draw from real life. So, it fills resumes with broad, generic statements that sound polished but reveal nothing personal or unique about the candidate, especially in the experience section.
For example, a human-written resume might read:
- After our team was downsized, I took the initiative to reorganize our workflows, helping us hit our quarterly targets with 30% fewer resources.
While the experience section of an AI-generated resume might read:
- Demonstrated adaptability by contributing to successful project outcomes in dynamic environments.
10. Repetition across multiple applications (Subtle)
When multiple resumes use similar phrases, sentence structures, or buzzwords, it’s often a sign that the applicants used the same AI tool, likely with the same job description as input.
For instance, if several candidates copy and paste your job ad into ChatGPT, the AI will produce similar-sounding resumes for each of them. That’s because the model draws from the same training data and adheres to the same writing patterns.
The result is resumes that contain similar phrases like “collaborated cross-functionally,” “drove strategic initiatives,” or “leveraged data to inform decisions,” regardless of each candidate’s actual background.
To spot this, focus on how candidates describe their responsibilities and achievements. Look for identical wording, repeated jargon, or accomplishments that seem oddly alike across different applications, especially if they mirror your description verbatim.
11. Metadata or hidden text
Though uncommon, applicants sometimes accidentally leave behind AI prompts or placeholder text when submitting their applications in a hurry. Some examples include:
- Dummy text like “[Company Name]”
- Direct conversational prompts and responses. For example: “You said: xyz” or “ChatGPT: said.”
- AI-generated closing lines. For example, “Do you want me to ___ or ___ with this?”
- Summaries that begin with phrases like “In summary” or “To conclude.”
Additionally, some AI-generated resumes contain hidden metadata and text. This includes invisible characters or watermarks, as well as keywords in invisible text, such as white font on a white background.
To spot hidden text and watermarks, copy and paste the resume text into a plain text editor (e.g., Notepad). Hidden formatting, invisible characters, or white-on-white text will become visible.
Should You Trust AI Resume Detectors for Candidate Screening?
AI resume detectors are the easiest and fastest way to detect AI usage. However, they may generate false positives because:
- They rely on surface-level patterns (e.g., repetitive phrasing) that even skilled human writers may use. As a result, well-written, genuine resumes can be incorrectly flagged as AI-generated.
- They primarily focus on stylistic patterns rather than the degree of AI involvement in the writing process. As such, they can’t distinguish between AI-assisted writing (where candidates use AI to help polish or draft parts) and fully AI-generated content.
Candidates have also learned how to outsmart AI detectors. By tweaking the phrasing, restructuring sentences, and adding small personal details — like anecdotes or role-specific language — they can make AI-generated resumes appear human and evade detection.
Now, most AI detection tools claim high accuracy. Turnitin reports 98% accuracy, Originality.AI says 98.2%, and GPTZero claims 99%.
Those numbers sound impressive — and they are. But, the fact that they are not 100% accurate should be concerning.
Hiring is a high-stakes process. Even a tiny margin of error can have serious consequences:
- It compromises the integrity and fairness of the screening process.
- A single false positive could result in rejecting a perfectly qualified candidate, causing you to lose top talent to competitors.
- Disqualified applicants may file lawsuits claiming they were unfairly filtered out based on flawed AI detection.
These limitations are why recruiters are increasingly skeptical about the use of AI, according to our 2025 hiring trends report.
We observed a 15-point drop in the adoption of AI by hiring professionals between 2024 and 2025. During the same period, the percentage of people opposed to using AI in recruitment doubled from 4% to 9%.
What’s the solution, then?
One proven way to improve your hiring process is to adopt an AI-resistant hiring approach. This approach involves embracing skills-based assessment & human-first rating system.
That means:
Instead of relying solely on resumes or credentials, assess candidates based on what they can actually do. Give them real-world tasks or simulations that mirror the job—whether that’s writing a blog post, designing a quick wireframe, or analyzing a data set.
Equally, a human-first hiring principle puts you in control of assessing candidates. Not AI. Not automation tools. You. Here’s how Willo helps:
Adopt AI-resistant Hiring With Willo
Willo allows you to screen candidates through a mix of answer types that reveal their true potential and skillset. For example, if you’re hiring a social media manager, you can:
- Evaluate confidence and communication skills via video responses.
- Use multiple-choice questions to assess specific knowledge, such as social media platforms and marketing strategies.
- Assess clarity and writing ability with written answers.
- Test experience by having candidates create content calendars or strategy documents and upload them once they’re done.
- Verify their qualifications and experience using document uploads, such as portfolios and project samples.
Throughout the screening process, Real Talk, Willo’s built-in anti-cheat mechanism, actively monitors for signs of AI use or external assistance. This includes detecting overly scripted answers, responses that lack natural speech patterns, or signs of copy-pasting.
Even when applicants try to outsmart the system—by mixing AI-suggested phrasing with real-world experience or maintaining constant eye contact to mask reading from a second screen—Real Talk can detect inconsistencies in tone, timing, and flow.
Instead of replacing human judgment, Willo Intelligence automates only repetitive tasks, such as:
- Writing follow-up emails
- Manually browsing transcripts for keywords
- Writing a candidate performance summary
- Generating a shortlist for the hiring manager
- Brainstorming follow-up questions for interviews
That way, you can hire fast while still applying human discernment, so genuinely qualified candidates aren’t overlooked.
Ready to hire with confidence? Book a demo.
Want to see how other companies are improving the recruitment process with skills-based hiring? We’ve covered four case studies for you.
