JMIR Publications

ABSTRACT

Background:

Mental health problems among university students are a growing global concern, yet limited resources and inadequate understanding of counseling procedures often delay support. Informed consent forms (ICFs) are vital for protecting rights and autonomy, but many are incomplete, ambiguous, or overly technical, and few institutions can effectively optimize them. Large language models (LLMs) offer scalable, low-cost solutions to enhance clarity and accessibility.

Objective:

This study aimed to evaluate whether LLM-based optimization could improve the structure, readability, content quality, and comprehensibility of university counseling ICFs, and to compare the performance of two advanced models—ChatGPT-5 and Grok-4.

Methods:

Counseling ICFs from 33 Chinese universities were collected and optimized using two advanced LLMs, ChatGPT-5 and Grok-4. A multidimensional framework assessed textual structure and readability, content quality from counselors’ perspectives, and comprehension from clients’ perspectives. Evaluations were conducted by mental health professionals and student volunteers. Wilcoxon signed-rank tests and linear mixed-effects models were applied for comparison and validation.

Results:

Compared with the originals, LLM-optimized ICFs demonstrated significant gains across all dimensions. The Lee–Yang readability index decreased from 28.68(5.69) to 22.39(2.13) with ChatGPT-5 and 24.37(2.32) with Grok-4 (both P<.001), while tone friendliness increased from 2.57(0.29) to 2.67(0.12) and 2.67(0.13), respectively. Expert-rated content quality improved from 45.33(8.74) to 52.54(7.92) and 55.49(7.81) (P<.001), primarily through enhanced specificity and existence of key items. Client comprehension scores rose from 19.02(1.32) to 22.33(0.81) and 22.05(0.90) (P<.001), reflecting higher clarity, readability, and acceptability. Linear mixed-effects models confirmed these findings.

Conclusions:

LLM-based rewriting markedly improved the clarity, completeness, and readability of counseling consent forms. By enhancing linguistic accessibility and professional precision, these models can support clearer communication and stronger counselor–client understanding. For universities with limited counseling resources, integrating LLM-assisted optimization may represent a practical step toward standardized, comprehensible, and client-centered counseling documentation. Clinical Trial: Not applicable.