Attitudes and Perceptions of Medical Researchers Towards the Use of Artificial Intelligence Chatbots in the Scientific Process: A Protocol for a Cross-Sectional Survey
Bibliographic record
Abstract
Abstract Artificial intelligence (AI) refers to computer systems or robots that can perform tasks associated with human intelligence, such as reasoning, problem-solving, and learning. While AI programs have not matched human versatility, they are increasingly used in various domains like self-driving cars, speech transcription, medical diagnosis, and smart assistants. AI has benefited fields like medicine, healthcare, and scientific research by improving productivity, reducing errors, and lowering costs. AI chatbots are conversational programs used for customer service, mental health support, and education. In scientific research, chatbots have the potential to automate tasks like literature searches, data analysis, and manuscript writing, improving efficiency and addressing the reproducibility crisis. However, there are challenges to overcome, including accuracy, reliability, ethical concerns, and limitations of current chatbot models. Scholarly publishing faces debates about authorship and guidelines have been established by journals and publishing organizations regarding the use of AI chatbots. To understand researchers’ attitudes towards AI chatbots, an international survey is proposed to explore their familiarity, perceived benefits, limitations, and factors influencing adoption. Findings can guide policy development and implementation of AI chatbots in scientific research.
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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 | 0.052 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".