Advanced International Journal for Research

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Volume 7 Issue 5
September-October 2026

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Toward Evidence-Based Applicant Tracking: A Framework for Evaluating AI-Assisted Recruitment Systems

Author(s) Samiyullaha Sayyed
Country India
Abstract Most evaluations of AI-assisted hiring systems still ask the same question: does this resume resemble the job posting? This paper argues that question is the wrong starting point. It proposes an evidence hierarchy—claim, then experience, then evidence, then assessment, then verified competency—where a resume statement counts for progressively more only as it is backed by progressively stronger proof, and argues that AI screening systems should be validated against both fairness and demonstrated competency jointly, not fairness alone. Companies now lean on Applicant Tracking Systems (ATS) to get through the sheer volume of resumes that online recruiting produces; that much is settled. What's less settled is whether these systems find people who can actually do the job, or whether they mostly find resumes that happen to echo the job posting's own wording. Generative AI has pulled this question into sharper focus. This paper does not report a completed experiment. It sets out a way to test these questions properly: an evaluation approach that looks past resume-to-job-description similarity and asks instead about candidate evidence, competency assessment, structured interviews, human review, fairness, and what happens after the person is actually hired. It proposes a Human-in-the-Loop Evidence-Based Candidate Evaluation Framework (HLECEF), lays out the research questions and hypotheses the framework is meant to test, and argues that ATS quality should ultimately be judged against demonstrated ability and on-the-job performance, not resume similarity.
Keywords Applicant Tracking System, Artificial Intelligence, Generative AI, Resume Screening, Recruitment, Candidate Selection, Algorithmic Bias, Human-in-the-Loop, Skills-Based Hiring, AI Ethics, Semantic Matching, Large Language Models
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 5, September-October 2026
Published On 2026-09-12
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E-ISSN 3048-7641
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