Uncovering the Nexus Between Attitudes Toward LLMs and Problematic Dependency on Them: A Latent Profile Analysis

Assessing the Impact of dependency on LLM models on Human Functioning. We invite you to participate in our survey investigating people’s experiences with Large Language Models (LLMs) and their impact on affective, beavioural and cognitive functioning. In this study, LLMs refer to advanced AI systems designed to understand and generate human-like text. Examples include ChatGPT, DeepSeek, Gemini, Ernie and other similar models. You must be a daily user of at least one LLM site or application (or rely on it substantially), born and living in the UK and culturally identify as British and speak English as your native language. If you do not meet these criteria, please stop here. This survey must be completed on a PC. The survey will collect two types of information. First, we will gather demographic details, such as age and gender, to ensure participants meet the study criteria. Second, we will collect information about your familiarity with and usage of LLMs, as well as explore how these tools relate to personality traits, cognitive styles, and other factors. Your participation will contribute to a deeper understanding of the broader impact of LLMs on users’ daily lives. In total, the study consists of two parts and approximately takes about 16 to 18 minutes to complete. Please note that a growing number of scientific journals expect the raw data of a survey to be published alongside a scientific article. Therefore, we will follow such a practice with the present research and publish the survey data when deemed appropriate. The data collected through this survey may also be shared with research collaborators in other institutions who we work with. Anonymization will be ensured. If you do not agree with the procedure, please do not participate. While filling in the survey, you can also decide to stop participation by closing the browser. Please note your already filled in data cannot be deleted. Moreover, it is not possible to contact us to ask to delete your data, because the present survey is anonymous, and we will not be able to identify your responses to deleting it.

Publication Details

Journal
OSF Preprints (OSF Preprints)
Published
2025-01-01
DOI
https://doi.org/10.17605/osf.io/fhc2z
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Uncovering the Nexus Between Attitudes Toward LLMs and Problematic Dependency on Them: A Latent Profile Analysis

OSF Preprints (OSF Preprints)
Artificial Intelligence in Healthcare and Education
article

Uncovering the Nexus Between Attitudes Toward LLMs and Problematic Dependency on Them: A Latent Profile Analysis

article en

Abstract

Assessing the Impact of dependency on LLM models on Human Functioning. We invite you to participate in our survey investigating people’s experiences with Large Language Models (LLMs) and their impact on affective, beavioural and cognitive functioning. In this study, LLMs refer to advanced AI systems designed to understand and generate human-like text. Examples include ChatGPT, DeepSeek, Gemini, Ernie and other similar models. You must be a daily user of at least one LLM site or application (or rely on it substantially), born and living in the UK and culturally identify as British and speak English as your native language. If you do not meet these criteria, please stop here. This survey must be completed on a PC. The survey will collect two types of information. First, we will gather demographic details, such as age and gender, to ensure participants meet the study criteria. Second, we will collect information about your familiarity with and usage of LLMs, as well as explore how these tools relate to personality traits, cognitive styles, and other factors. Your participation will contribute to a deeper understanding of the broader impact of LLMs on users’ daily lives. In total, the study consists of two parts and approximately takes about 16 to 18 minutes to complete. Please note that a growing number of scientific journals expect the raw data of a survey to be published alongside a scientific article. Therefore, we will follow such a practice with the present research and publish the survey data when deemed appropriate. The data collected through this survey may also be shared with research collaborators in other institutions who we work with. Anonymization will be ensured. If you do not agree with the procedure, please do not participate. While filling in the survey, you can also decide to stop participation by closing the browser. Please note your already filled in data cannot be deleted. Moreover, it is not possible to contact us to ask to delete your data, because the present survey is anonymous, and we will not be able to identify your responses to deleting it.

OSF Preprints (OSF Preprints)
Quality Education
Openalex Percentile: Top 56%
Artificial Intelligence in Healthcare and Education
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Uncovering the Nexus Between Attitudes Toward LLMs and Problematic Dependency on Them: A Latent Profile Analysis · OSF Preprints (OSF Preprints) (2025) | TGRS Research Map | TGRS