This study evaluates whether GPT-3.5 and GPT-4 can score personality and interview performance from responses elicited in asynchronous video interviews. Despite the video framing, the models receive only Google Speech-to-Text transcripts, the interview questions and, in the primary condition, information identifying the factor or facet targeted by each question. The final sample contains 685 participants answering eight questions for four Extraversion and four Conscientiousness facets in a fictional management-trainee application. Criteria are HEXACO-60 self-reports, ratings by four trained observers, and five interview ratings provided by two professional recruiters per participant. A ten-fold cross-validated supervised BoW-SBERT model serves as the comparator.
The positive result is concentrated in apparent Extraversion. Against observer ratings, GPT-4 reaches R²=.53 and GPT-3.5 .40, above BoW-SBERT's .36; GPT-4 also obtains R² values from .32 to .61 across the four Extraversion facets. The picture reverses for Conscientiousness: GPT-4 explains .11 and GPT-3.5 .08 of observer variance versus .23 for the baseline; both LLMs are below zero against self-report and also fail on Prudence and Perfectionism. Most importantly for hiring, every R² for the four recruiter-rated competencies and overall interview performance is negative, so the models perform worse than assigning the mean.
Reliability is measured only with Pearson correlation. When exactly the same text is scored twice, GPT-4 obtains r=.79 for Extraversion, .61 for Conscientiousness and .83 for interview performance; with a new interview 7-24 months later, the values are .59, .39 and .85. These correlations do not test absolute agreement, and the latter combines instrument inconsistency with genuine changes in responses. Raising temperature slightly increases observer-Extraversion R² while sharply reducing consistency. Scores are compressed and skew high. The fairness analysis compares gender differences and correlations with age, education and attractiveness, but it does not test measurement invariance, equal error, differential prediction or adverse impact; lower association than human observers is not sufficient evidence of fairness.
The candidate-selection simulation does not validate operational use. Its quantity labeled “accuracy” is actually recall or true-positive rate, and ties allow the selected set to exceed K; overlap therefore rises mechanically as K grows. There is no random-ranking baseline, specificity, decision utility or prospective workplace outcome. Semantic similarity between explanations and HEXACO items is also relative and partly circular because the prompt discloses the constructs and distances are normalized within each explanation.
The author preprint links a public repository containing 11,522 model-answer files and prediction artifacts at commit b2a3a70, a meaningful transparency contribution. It does not contain the OpenAI request code, exact model identifiers, complete messages, BoW-SBERT training, temperature/selection analyses or mixed-effects models. There is no README, license, dependency manifest, test suite or CI, and legacy pickles fail under pandas 2.2.3. The sole linguistic-analysis script has broken paths and variables and cannot produce the published figure. Its 685 output identifiers also link directly to a 710-row CSV containing demographics and psychometric ratings, without a reuse-governance note.
The faithful conclusion is narrow but useful: historical GPT-4 approximated apparent Extraversion well from text in this setting, but the study did not validate general personality assessment or employment decisions. Conscientiousness, job-performance validity, score agreement, fairness and operational utility remain weak or unestablished. The study itself supplies evidence against direct deployment: highly correlated interview scores can coexist with negative R².