Abstract
Faking on personality tests continues to be a challenge in hiring practices, and with the increased accessibility to free, generative AI large language models (LLM), the difference between human and algorithmic responses is difficult to distinguish. Four LLMs–GPT-3.5, Jasper, Google Bard, and GPT-4 were prompted to provide ideal responses to personality measures, specific to a provided job description. Responses collected from the LLM's were compared to a previously collected student population sample who were also directed to respond in a ideal fashion to the same job description. Overall, score comparisons indicate the superior performance of GPT-4 on both the single stimulus and forced-choice personality assessments and reinforce the need to consider more advanced options in preventing faking on personality assessments. Additionally, results from this study indicate the need for future research, especially as generative AI improves and becomes more accessible to a range of candidates.
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