ChatGPT vs human expertise in the context of IT recruitment

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Tomasz Szandała
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Abstract

This study aims to address the gap in understanding the extent to which AI can replace human technical interviewers in the recruitment process. It investigates the potential of a Large Language Models, specifically ChatGPT, Google Gemini and Mistral, in assessing candidates’ competencies in Information Technology (IT) compared to evaluations made by human experts. The experiment involved three experienced DevOps specialists who assessed the written responses of 21 candidates to ten industry-relevant questions; each limited to 500 characters. The evaluation was conducted using a simple yet effective −2 to 2 scale, with −2 indicating a negative assessment for incorrect answers, 0 for ambiguous or incomplete answers, and 2 for excellent responses. The same set of responses was then evaluated by LLMs, adhering to the identical criteria and scale. This comparative analysis aims to determine the reliability and accuracy of AI in replicating expert human judgement in IT recruitment. The study’s findings, backed by the Fleiss kappa test, show that human reviewers are not perfectly aligned in their judgement. On the other hand, the AI tool also lacks consistency, as the consequent repetition of the same review request may result in a different decision. The results are anticipated to contribute to the ongoing discourse on AI-assisted decision-making and its practical applications in human resource management and recruitment.

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来源期刊
Expert Systems with Applications
Expert Systems with Applications 工程技术-工程:电子与电气
CiteScore
13.80
自引率
10.60%
发文量
2045
审稿时长
8.7 months
期刊介绍: Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.
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