ArticleScientific reports2024
Testing the capacity of Bard and ChatGPT for writing essays on ethical dilemmas: A cross-sectional study.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of AI-Generated Content in Medical Education: Systematic Review of the Impact on Critical Thinking Abilities of Medical Students.JMIR medical education · 2026Pooled it
- Deciphering AI-chatbots utilization in clinical practice, research and education among Chinese medical students in generative AI era: a cross-sectional study.BMC medical education · 2026Article
- Large language models as information providers for appropriate antimicrobial use: computational text analysis and expert-rated comparison of ChatGPT, Claude and Gemini.BMJ health & care informatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advancements in artificial intelligence (AI) have led to concerns about its potential misuse in education. As large language models (LLMs) such as ChatGPT and Bard can generate human-like text, researchers and educators noted the potential redundancy of tasking students with writing academic essays. We aimed to explore if the two LLMs could generate unstructured essays on medical students' personal experiences of challenges and ethical dilemmas that are indistinguishable from human-written texts. We collected 47 original student-written essays from which we extracted keywords to develop prompts for the LLMs. We then used these prompts to generate an equivalent number of essays using ChatGPT and Bard. We analysed the essays using the Language Inquiry and Word Count (LIWC) 22 software, extracting the main LIWC summary measures and variables related to social and psychological processes. We also conducted sub-analyses for sixteen student essays that were presumably written entirely or in part by AI, according to two AI detectors. We found that AI-written essays used more language related to affect, authenticity, and analytical thinking compared to student-written essays after we removed AI-co-written student essays from the analysis. We observed that, despite the differences in language characteristics compared to student-written essays, both LLMs are highly effective in generating essays on students' personal experiences and opinions regarding real-life ethical dilemmas.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.