AI, Cheating, and Assessments: What Hiring Teams Should Know

If you’ve worked in hiring long enough, you’ve probably had the thought at some point: Is this candidate being completely honest with me?

It’s a fair question, and a familiar one. Recruitment has always involved a degree of uncertainty, whether you’re reviewing resumes, conducting interviews, or checking references.

Today, that question has taken on new urgency. With generative AI tools now widely available, many HR and talent acquisition leaders are asking a modern version of the same concern: Can candidates use AI to cheat on pre-employment assessments? And if so, does that make assessments less reliable?

For many organizations, this has become the elephant in the room. It’s a valid concern and one worth addressing directly. But at the same time, it’s important to separate what feels new from what has always existed in hiring. The real question for employers shouldn’t be whether cheating is possible, but how to design hiring processes that remain fair, predictive, and job-relevant in a world where AI is simply part of the landscape.

Cheating in Hiring Isn’t New, AI Just Changed the Tool

The reality is that cheating in hiring didn’t begin with AI. While new tools may be changing how candidates prepare or present themselves, the underlying behavior isn’t new. People have always looked for ways to gain a competitive edge, whether that means stretching the truth on resumes, rehearsing interview answers, or framing their experience in the most favorable light possible. In fact, a FlexJobs survey found that one in three workers admit to lying on their resumes. What’s different now is that technology has become faster, more sophisticated, and more accessible.

Hiring has never been a perfectly honest or objective process. Long before AI entered the conversation, employers were already navigating incomplete, biased, or sometimes misleading information. Candidates have always elaborated on resumes, selectively chosen references, and sought advice on how to stand out during the hiring process. In that sense, AI hasn’t created dishonesty. It has simply made certain forms of assistance quicker, easier to access, and in some cases, harder to detect.

What feels disruptive now isn’t that cheating suddenly exists, but that technology has evolved quickly enough to force employers to rethink what meaningful evaluation looks like in modern hiring. And that’s not necessarily a bad thing.

The goal of hiring has never been to remove uncertainty entirely. It’s to reduce risk by making better decisions with better information. AI raises new questions, but it also reinforces something hiring professionals have known for years: relying solely on surface-level signals, like resumes or interviews, was never enough. What matters most is having reliable, job-relevant insight that helps predict success in the role.

Are Pre-Employment Assessments Still Reliable in the Age of AI?

A common misconception is that assessments are only useful if they are completely impossible to cheat on. But that has never been the standard.

Pre-employment assessments were introduced to bring more structure, consistency, and scientifically validated insight into a hiring process that is often subjective. They aren’t designed to replace human judgment or deliver perfect certainty. Instead, they help employers evaluate candidates more fairly by focusing on job-specific criteria and applying the same standards across the board.

That remains true even in the age of AI. Compared to resumes or interviews alone, assessments offer a more objective source of insight for decision-making because they help reduce bias and keep the focus on candidate potential. When grounded in science and used as part of a holistic system, they continue to strengthen hiring outcomes. This is why organizations that use validated assessments consistently experience better alignment between people and roles, resulting in stronger retention, reduced turnover, and more reliable performance over time.

Why Personality Assessments Are Harder to Fake

Knowledge-based or skill tests may be easier for candidates to research, practice, or receive outside help on. But personality assessments operate differently.

Personality assessments aren’t about selecting the “right” answer. They are designed to measure patterns in behavior, preferences, and work style, such as adaptability or sociability. These are difficult to fake consistently, especially in well-designed assessments that look for reliability and validity in responses.

While AI may help someone generate a well-crafted answer in the moment, it cannot easily replicate the deeper behavioral tendencies that will show up in real working environments over time. This is why personality-based insights continue to play an important role in modern hiring. They shift the focus away from polished self-presentation and toward the traits that predict long-term success and fit.

Customized Benchmarking as a Strong Safeguard

One of the most effective ways to strengthen assessment reliability in the age of AI is through customized benchmarking. While generic assessments can provide helpful insight, benchmarking adds essential context and an additional layer of defensibility. It allows organizations to define what success truly looks like based on validated data from within their own workforce. In doing so, hiring teams can evaluate candidates against the traits and characteristics most commonly found in top performers in a specific role.

At Prevue, every role is measured against job benchmarks tailored to the organization and the position itself. This makes the process less about appearing impressive and more about demonstrating genuine fit. An important part of that safeguard is that candidates, and the AI tools they may use, do not know what the job benchmark actually looks like. Because these benchmarks are built from role-specific data, they cannot be easily inferred or targeted using generic advice or AI-generated responses. As benchmarks become more specific to the role and work environment, it becomes much harder to rely on superficial shortcuts to “game the system.”

This is especially important in the conversation around AI cheating. Many approaches rely on general assumptions, such as what sounds desirable, what an employer wants to hear, or how an “ideal candidate” might respond. By shifting the focus away from appearing good on paper and toward authentic alignment with the demands of the job, these assumptions become far less effective. Customized benchmarking significantly strengthens the predictive value of pre-employment assessments by keeping the evaluation focused on job relevance and real performance indicators.

The Best Response to AI Cheating Is Better Hiring Design

As concerns about AI cheating grow, some organizations are tempted to respond with increasingly aggressive monitoring tactics. But hiring has never been about catching every possible workaround. It has always been about making informed decisions using the best available information. In practice, hiring works best when it is built on structure, fairness, and trust, not surveillance.

The strongest response to AI is not to search for a single “cheat-proof” solution, but to design a process that is naturally more resilient. Effective hiring comes from layering multiple sources of evidence rather than relying on one measure alone. Assessments play an important role in that system, but they are most powerful when used alongside role-specific benchmarks, structured interviews, and clearly defined evaluation criteria.

Thoughtful design also includes setting expectations around integrity. Behavioral research shows that when individuals are asked to commit to honesty before completing a task, they are more likely to respond truthfully. A study published in Nature Human Behavior found that simple honesty prompts can meaningfully reduce dishonest behavior by activating internal ethical standards rather than relying on external enforcement.

These principles are reflected in Prevue’s approach to assessment integrity, which includes built-in anti-cheating measures and candidate honesty commitments designed to preserve reliable results. When expectations are clear and candidates are encouraged to engage authentically, the impression management tactics decrease, and the evaluation remains focused on job-relevant criteria. Instead of trying to eliminate every possible way a candidate might gain an advantage, organizations are better served by building structured hiring processes that consistently measure what matters most: job alignment, performance potential, and long-term success.

Hiring in the Age of AI: What Matters Most Hasn’t Changed

AI is influencing how candidates prepare, how employers evaluate, and how hiring conversations evolve. But it hasn’t changed the fundamental challenge hiring teams have always faced: making confident decisions about who will succeed in a role.

Cheating and impression management have always existed in hiring in one form or another. AI is simply the latest chapter. What matters is ensuring hiring tools remain grounded in validated science, focused on job relevance, and tailored to the realities of each organization.

Pre-employment assessments continue to be one of the most effective ways to bring consistency, fairness, and predictive insight into hiring decisions. And as hiring evolves, it’s worth remembering: the goal has never been perfection — it’s better prediction, better fit, and stronger outcomes.