ChatGPT, can you take my job interview? Examining artificial intelligence cheating in the asynchronous video interview
Bibliographic record
Abstract
Abstract Artificial intelligence (AI) chatbots, such as Chat Generative Pre‐trained Transformer (ChatGPT), may threaten the validity of selection processes. This study provides the first examination of how AI cheating in the asynchronous video interview (AVI) may impact interview performance and applicant reactions. In a preregistered experiment, Prolific respondents ( N = 245) completed an AVI after being randomly assigned to a non‐ChatGPT, ChatGPT‐Verbatim (read AI‐generated responses word‐for‐word), or ChatGPT‐Personalized condition (provided their résumé/contextual instructions to ChatGPT and modified the AI‐generated responses). The ChatGPT conditions received considerably higher scores on overall performance and content than the non‐ChatGPT condition. However, response delivery ratings did not differ between conditions and the ChatGPT conditions received lower honesty ratings. Both ChatGPT conditions rated the AVI as lower on procedural justice than the non‐ChatGPT condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".