Common limitations across all AI tools included alack of specificity and transferability. Most interventionplan outputs were described as too general and lackingclient-specific detail
Not necessarily written from a specific point of view but the article goes from present to past tense as it walks the readers through the experiment and then walks through the results after the experiment.
Descriptive statistics and nonparametric analysis wereutilized to summarize the basic features of the data acrossthe six AI tools.
Use of jargon and technical language.
otentially
Qualifier used to promote hedging.
AI Tool Selection
Example of subheadings being used.
Clinical Application of Large Language Modelsfor Intervention Plan Development inSpeech-Language Pathology
Long and technical title.
Using a mixed design including both quantitative and qualitative analy-
Qualitative and quantitative analysis was used. This method of research is most efficient for this research question because they are trying to determine how the quality of the AI generated lesson plans and the average scores and standard deviations.
Namhee Kim, a Mercy Homer,a and Hyeju Jangba California Baptist University, Riverside b Indiana University Indianapolis
There are three different authors each from different universities, two of them are from the same university and collaborated outside of their university.
(e.g., Price et al., 2024
Example of an article attribution of a similar research article.
Two versions of ChatGPT, developed by OpenAI,were included. ChatGPT-3.5 was chosen because it is oneof the most widely used and freely accessible generativeAI tools (OpenAI, 2023). ChatGPT-4o, OpenAI’s latestand more advanced paid version, was selected for itsenhanced performance compared to the free version(OpenAI, 2024). Both ChatGPT-3.5 and ChatGPT-4oshare similar training data sources, but ChatGPT-4ointroduces multimodal capabilities and includes data up toApril 2023, whereas ChatGPT-3.5 has a cutoff date ofJanuary 2022 (OpenAI, 2024)
This section presents the rationalization of choosing each form of AI and how it was applicable to the creation of clinical treatment plans.
This study compared the performance of outputsgenerated by six LLM-based AI tools across three fic-tional clinical cases.
This article is formatted as an IMRaD format and this is a description of the method they are using.
Currently, research on the use of AI tools for clinicalwriting or intervention plan development
present tense example
hey are not capable of producing transferable and clini-cally relevant plans that address individual needs
The research found that AI was a valuable resource but not the most dependable.
currently available for clinical writing in the fieldof speech-language pathology. This study aims to evaluate the potential appli-cations and limitations of these AI tools, as well as their ability to provide rele-vant and reliable information for developing intervention plans
The intended audience for this article would be clinicians because it is focused on treatment plans.
This study aims to evaluate the potential appli-cations and limitations of these AI tools, as well as their ability to provide rele-vant and reliable information for developing intervention plans.
The topic being discussed is how AI could be utilized in the realm of speech pathology