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Related Experiment Videos

Cross-tool evaluation of artificial intelligence-drafted informed consent documents: A 3-level study.

Shama U Rao¹, Akuma Ifeanyichukwu¹, Uma Kulkarni¹

¹Centre for Ethics, Yenepoya (Deemed to be University), Mangalore, Karnataka, India.

Perspectives in Clinical Research

|May 15, 2026

Summary

Generative AI tools draft informed consent documents with moderate ethical adequacy but often lack crucial details, requiring human oversight. A combined human-AI approach is recommended for robust consent document creation.

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Ethics in Research

Ethics in Research

Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, this has not always been the case. Modern researchers must demonstrate that the research they perform is ethically sound.

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Area of Science:

Bioethics
Artificial Intelligence
Clinical Research

Background:

Informed Consent Documents (ICDs) are vital for ethical human participant research.
Generative AI offers potential for drafting ethically-oriented documents.
Limited empirical data exists on AI's ethical robustness, completeness, and readability in ICDs.

Purpose of the Study:

To compare the performance of five generative AI tools (ChatGPT, Gemini, Copilot, Meta AI, Perplexity AI) in drafting ICDs.
To evaluate AI-generated ICDs across diverse research scenarios (survey, observational, interventional).

Main Methods:

An experimental, cross-sectional, cross-tool comparative design was employed.
Thirty ICDs were generated by five AI tools across six scenarios.

Keywords:

Comparative evaluation large language models readability research ethics research governance

Related Experiment Videos

Assessors evaluated ethical robustness and completeness using a validated rubric; readability was measured by Flesch-Kincaid Grade Level (FKGL).

Main Results:

AI tools demonstrated moderate ethical robustness (2.8-3.1) and completeness (2.3-2.7).
Meta AI led in ethical robustness (3.1), Gemini in completeness (2.7); ChatGPT showed balanced performance (2.9).
Most AI-generated ICDs had readability scores (FKGL 9.3-10.6) exceeding plain-language recommendations; inter-rater reliability was low (κ = -0.063).

Conclusions:

Generative AI can draft ICDs with moderate ethical and procedural adequacy.
AI tools often omit context-specific ethical details, requiring human review for compliance and participant protection.
A hybrid human-AI model is recommended for efficient and ethically sound ICD development.