Attitudes and Perceptions of Medical Researchers Towards the Use of Artificial Intelligence Chatbots in the Scientific Process: A Large-Scale, International Cross-Sectional Survey
Bibliographic record
Abstract
Abstract Background Chatbots are artificial intelligence (AI) programs designed to simulate conversations with human users through text or speech. The use of artificial intelligence chatbots (AICs) in scientific research presents benefits and challenges. Although the stances of journals and publishing organizations on AIC use is increasingly clear, little is known about researchers’ perceptions of AICs in research. This survey study explores attitudes, familiarity, perceived benefits, limitations, and factors influencing adoption of AIC by researchers. Methods A cross-sectional online survey of published researchers was conducted. Corresponding authors and their e-mail addresses were identified by querying PubMed for articles (any type) published in a MEDLINE indexed journal in the most recent two months and using R script on PubMed metadata. e-Mail invitations were sent to 61560 study authors. The survey, administered on SurveyMonkey, opened on July 9, 2023, and closed on August 11, 2023. Respondents had 3 weeks to complete the survey and were sent 2 reminder e-mails during the weeks of July 17, 2023, and July 24, 2023. Results 2165 respondents completed the survey (4.0% response rate; 94% completion rate of those who responded). Most were familiar with the concept of AICs (n=1294/2138, 60.5%). About half had used an AIC previously for purposes relating to the scientific process (n=1107/2125, 52.1%). Only 244/2137 (11.4%) respondents reported that their institution offered training on using AI tools of whom 64/244 (26.2%) completed the training. 211/2131 (9.9%) reported that their institution implemented policies regarding AIC use in the scientific process. Most respondents expressed interest in learning more and receiving training on AIC use in the scientific process (n=1428/2048, 69.7%). Respondents had mixed opinions about the potential benefits of using AICs, whereas most agreed on their cons/challenges. Respondents agreed AICs were most beneficial in reducing the workload and administrative burden on researchers (n=1299/1941, 66.9%) and they were most concerned about the lack of understanding behind how AICs make decisions and generate responses (n=1484/1923, 77.2%). Conclusions Most respondents are familiar with AICs and half used AICs in their own research. Although there is clear interest in understanding how AICs can be used, many hesitate due to existing limitations. Little formal instruction on using AICs is available across academic institutions.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".