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Optimization of University Counseling Consent Forms With Large Language Models: Multidimensional Comparative

Jianchen Luo1,2, Jing Ma3, Danni Zhan4

  • 1Department of Liver Surgery, West China Hospital of Sichuan University, 37 Guoxue Alley, Wuhou District, Chengdu, 610041, China, 86 18980601895.

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Summary

Large language models (LLMs) significantly improved university counseling informed consent forms (ICFs), enhancing readability and comprehension. LLM-rewritten ICFs offer a scalable solution for clearer mental health documentation.

Keywords:
higher educationinformed consent formslarge language modelsmental health accessibilityuniversity counseling

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Area of Science:

  • Mental Health
  • Information Science
  • Artificial Intelligence

Background:

  • University student mental health is a growing concern, often hindered by limited counseling resources and complex informed consent forms (ICFs).
  • Existing ICFs frequently suffer from ambiguity, technical jargon, and incompleteness, impeding timely help-seeking and client understanding.
  • Large language models (LLMs) present a potential solution for enhancing the clarity and accessibility of these crucial documents.

Purpose of the Study:

  • To evaluate the effectiveness of LLM-based rewriting in improving university counseling ICFs.
  • To compare the performance of two advanced LLMs, ChatGPT (GPT-5) and Grok-4, in rewriting ICFs.
  • To assess improvements in structure, readability, content quality, and comprehensibility.

Main Methods:

  • Comparative evaluation of 33 original Chinese university counseling ICFs against versions rewritten by ChatGPT (GPT-5) and Grok-4.
  • A multidimensional framework assessed textual structure, readability (Lee-Yang Readability Index), expert-rated content quality, and volunteer-rated reading comprehension.
  • Statistical analysis included Wilcoxon signed rank tests and linear mixed-effects models to validate findings.

Main Results:

  • Both LLM-rewritten ICFs demonstrated significant improvements in readability, with decreased Lee-Yang Readability Index scores.
  • Expert-rated content quality and volunteer-rated comprehension scores substantially increased, indicating enhanced clarity and acceptability.
  • Grok-4 produced longer documents, suggesting a potential trade-off between content enhancement and document length.

Conclusions:

  • LLM-based rewriting effectively enhances the readability, content quality, and comprehensibility of university counseling ICFs.
  • LLMs offer a scalable method to optimize counseling documentation, improving student access to mental health services.
  • Implementation requires consideration of practical factors like document length and the necessity of human oversight.