Key Points
- AI is now widely used by candidates when preparing job applications, including CVs, cover letters and application responses.
- Recent survey data suggests that a majority of graduates use AI tools during the application process, with over half admitting to exaggerating skills.
- AI-generated job applications are often screened by automated systems before being reviewed by humans.
- The primary risks for employers include misrepresentation of skills, lack of authenticity, and inconsistencies between written applications and interviews.
- AI use in job applications is not inherently problematic, but it increases the need for verification, structured assessment and consistent decision-making.
A new survey by Kahoot! has found that 65% of graduates now use artificial intelligence (AI) tools to assist them with their job applications. This represents a significant increase on 2025 data from the Early Careers Survey (which showed that 39% used them to refine applications, 30% to produce them wholesale, and 29% for interview preparation). The Kahoot! survey also found that 51% of graduates admit to exaggerating their skills set when using AI tools.
AI use in recruitment is no longer novel. What has changed is its scale and sophistication. For employers, the challenge is not whether candidates are using AI in relation to job applications, but how to assess the job applications fairly, accurately and consistently in an environment where AI-assisted drafting is becoming standard practice.
Job Applications & AI
Artificial intelligence is now embedded at multiple stages of the recruitment process. Candidates increasingly use AI tools to draft CVs and job applications, tailor cover letters, refine application answers and prepare for interviews. At the same time, employers routinely rely on applicant tracking systems (ATS) and automated screening tools to filter and rank candidates.
This creates an unusual dynamic: AI-generated applications are often being assessed by AI-driven systems before reaching a human reviewer. While this can improve efficiency, it also raises questions about authenticity, accuracy and transparency.
Importantly, AI use in itself is not inherently problematic. Many candidates use these tools as drafting aids or to improve clarity. The risk arises where AI-generated content overstates experience, masks gaps in knowledge, or creates a misleading impression of competence.
What Employers Need To Watch Out For
As AI-assisted job applications become the norm, employers should focus on the following practical risks.:-
- Authenticity of Application Materials: AI can produce highly polished CVs, job applications, and cover letters that read convincingly but may lack substance. Employers should be alert to vague achievements, generic language or applications that lack specific, verifiable detail. Targeted follow-up questions and role-relevant assessments can help confirm whether written claims reflect genuine experience.
- Skill Exaggeration and Misrepresentation: With over half of surveyed applicants admitting to exaggeration, self-assessed skills should be treated cautiously. Practical exercises, scenario-based questions and work simulations remain effective ways of testing real capability, particularly for technical or specialist roles.
- Generic Language and Overuse of Buzzwords: AI tools tend to rely on common phrases and industry jargon. Employers should look beyond surface-level language and ask candidates to explain how they applied skills in real situations, what outcomes they achieved, and what challenges they faced.
- Cultural Fit and Motivation: AI-generated applications can struggle to convey personality, motivation or alignment with organisational values. Interviews remain critical for assessing whether a candidate understands the organisation, shares its values and can articulate why the role genuinely interests them.
- Bias and Fairness: AI tools may reflect biases present in their training data. Employers should ensure that recruitment decisions are not influenced by superficial indicators, and that assessment criteria are applied consistently across candidates, regardless of how applications are drafted.
- Consistency Across Recruitment Stages: A disconnect between written applications and live interviews can indicate over-reliance on AI. Comparing written submissions with verbal explanations, presentations or problem-solving exercises can help identify whether communication skills and understanding are consistent.
- Verification of Experience: AI can generate plausible but inaccurate descriptions of roles, responsibilities or achievements. Robust reference checks, confirmation of qualifications and, where appropriate, evidence of past work remain essential safeguards.
Looking Ahead: Building Trust & Adaptability
AI is now a permanent feature of the recruitment landscape. The task for employers is not to resist it, but to adapt assessment processes to ensure they remain fair, accurate and meaningful.
A balanced approach is key. Technology can improve efficiency, but human judgement remains essential in evaluating credibility, motivation and cultural fit. Clear recruitment processes, practical assessments and consistent verification standards are more important than ever.
Ultimately, employers that combine AI-enabled efficiency with careful human oversight will be best placed to identify genuine talent, manage risk and build teams that perform effectively beyond what appears on paper.
In the age of AI-assisted job applications, rigour, consistency and transparency are what distinguish effective recruitment from costly misjudgement.
What Employers Should Do
- Review recruitment processes to ensure that job applications are assessed on evidence and substance, not presentation alone.
- Use practical assessments and scenario-based questions to test claimed skills and experience.
- Ensure consistency between written job applications and performance in interviews or assessments.
- Apply assessment criteria uniformly to reduce bias and avoid over-reliance on automated screening outputs.
- Maintain robust verification procedures, including reference checks and confirmation of qualifications.
