Brain versus bot: Distinguishing letters of recommendation authored by humans compared with artificial intelligence
Bibliographic record
Abstract
Objectives: Letters of recommendation (LORs) are essential within academic medicine, affecting a number of important decisions regarding advancement, yet these letters take significant amounts of time and labor to prepare. The use of generative artificial intelligence (AI) tools, such as ChatGPT, are gaining popularity for a variety of academic writing tasks and offer an innovative solution to relieve the burden of letter writing. It is yet to be determined if ChatGPT could aid in crafting LORs, particularly in high-stakes contexts like faculty promotion. To determine the feasibility of this process and whether there is a significant difference between AI and human-authored letters, we conducted a study aimed at determining whether academic physicians can distinguish between the two. Methods: A quasi-experimental study was conducted using a single-blind design. Academic physicians with experience in reviewing LORs were presented with LORs for promotion to associate professor, written by either humans or AI. Participants reviewed LORs and identified the authorship. Statistical analysis was performed to determine accuracy in distinguishing between human and AI-authored LORs. Additionally, the perceived quality and persuasiveness of the LORs were compared based on suspected and actual authorship. Results: A total of 32 participants completed letter review. The mean accuracy of distinguishing between human- versus AI-authored LORs was 59.4%. The reviewer's certainty and time spent deliberating did not significantly impact accuracy. LORs suspected to be human-authored were rated more favorably in terms of quality and persuasiveness. A difference in gender-biased language was observed in our letters: human-authored letters contained significantly more female-associated words, while the majority of AI-authored letters tended to use more male-associated words. Conclusions: Participants were unable to reliably differentiate between human- and AI-authored LORs for promotion. AI may be able to generate LORs and relieve the burden of letter writing for academicians. New strategies, policies, and guidelines are needed to balance the benefits of AI while preserving integrity and fairness in academic promotion decisions.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.159 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".