ChatGPT has taken the world by storm, setting a record for the fastest app to reach a 100 million users, which it hit in two months. The implications of this tool are far-reaching, universities...

Introduction

ChatGPT Statistics: ChatGPT, an innovation of OpenAI, has made a substantial entrance into the world of technology, shattering all records with its fast user growth. Chat GPT is an AI-generated chatbot that has been making waves in the technical world since its launch. It has a startling ability to mimic human conversation, making it a reliable tool for various tasks that range from drafting emails, answering queries, and writing essays to even assisting with coding as well.

The substructure of ChatGPT is built on OpenAI's GPT-3, which is a large language model that was showered as one of the enlightened language models when introduced in 2020. This article hunts through the captivating ChatGPT Statistics and traverses everything from user growth nationwide to revenue generation and much more.

Introduction

ChatGPT Statistics: In today’s technologically advancing world, Artificial Intelligence (AI) is no longer just science fiction; it has also become an integral part of everyday life. One of the most exciting examples of AI in action is ChatGPT, a powerful language model developed by OpenAI. ChatGPT is a conversational AI tool capable of generating human-like responses, assisting with a variety of tasks ranging from writing to coding, customer service, education, and more. In everyday life, the implementation of ChatGPT is growing enormously as it enables communication faster, smarter, and more intuitively.

This article examines how ChatGPT operates and its statistical analysis from various perspectives, including its practical applications, and the evolving conversations surrounding its benefits, limitations, and future potential.

ChatGPT is used most widely among those between 25 and 34 around the world. The youngest group, those under 24, are the second largest userbase, and together those under 34 account for over 60 percent of ChatGPT users. It is perhaps unsurprising that the younger age brackets use the chatbot more than older as that is the common trend with new technologies. Male users were far more numerous than female users, with males representing over 65 percent of total users in 2023.

According to a survey of adults in the United States conducted in January 2023, ** percent of respondents used ChatGPT to generate text themselves. In comparison, overall ** percent of the female respondents claimed to have never used nor seen anyone else use it, while ** percent of respondents reported having seen text being generated by the AI technology for someone else.

In a survey conducted across **** Southeast Asian countries in February 2023, almost half of the respondents selected collection of personal data as one of the concerns they had regarding the usage of chatbots like ChatGPT. In contrast, ethical issues related to data privacy and intellectual property were a concern for ** percent of the respondents.

Introduction

ChatGPT-4 Statistics: In 2024, ChatGPT-4 has seen a notable surge in user engagement, processing millions of queries daily. Its high accuracy and reliability have made it a popular choice for businesses and individuals. Over 70% of users report high satisfaction, reflecting the model's effectiveness across various applications, from customer service to content creation. ChatGPT-4 excels at interpreting and generating human-like text, thanks to continuous updates that enhance its ability to handle complex queries.

Developed by OpenAI, ChatGPT stands for "Chat Generative Pre-trained Transformer." This advanced model surpasses GPT-3.5 by offering improved accuracy, better context handling, and even image understanding. These features highlight ChatGPT-4's transformative role in AI-driven communication.

ChatGPT, an artificial intelligence (AI) powered chatbot, is most used by companies in the technical and education industries, with over *** companies using it in 2023. It is perhaps unsurprising that the technical field has embraced the use of ChatGPT, but it is interesting that so many educational institutes have begun to use it. While other industries do utilize the OpenAI-made chatbot, there are less than *** institutions and companies that use ChatGPT in other industries. This is especially true of agriculture, cultural, and legal industries, where only a single company is using ChatGPT in 2023.

This dataset shows the types of advice users sought from ChatGPT based on a 2025 U.S. survey, including education, financial, medical, and legal topics.

This dataset shows how men and women in the U.S. reported using ChatGPT in a 2025 survey, including whether they followed its advice or chose not to use it.

This dataset presents ChatGPT usage patterns across different age groups, showing the percentage of users who have followed its advice, used it without following advice, or have never used it, based on a 2025 U.S. survey.

This dataset presents ChatGPT usage patterns across U.S. Census regions, based on a 2025 nationwide survey. It tracks how often users followed, partially used, or never used ChatGPT by state region.

This dataset reflects how Americans perceive ChatGPT's broader societal impact, based on a 2025 survey that asked whether the AI will help or harm humanity.

This project investigated teacher attitudes towards Generative Artificial Intelligence Tools (GAITs). In excess of three hundred teachers were surveyed across a broad variety of teaching levels, demographic areas, experience levels, and disciplinary areas, to better understand how they believe teaching and assessment should change as a result of GAITs such as ChatGPT.Teachers were invited to complete an online survey relating to their perceptions of the open Artificial Intelligence (AI) tool ChatGPT, and how it will influence what they teach and how they assess. The purpose of the study is to provide teachers, policymakers, and society at large with an understanding of the potential impact of tools such as ChatGPT on Education.This dataset contains public data files used for the ChatGPT survey (XLSX) and the survey containing variable selection codes (DOCX). See the second sheet of the XLSX file for variable descriptions.

This dataset summarizes how ChatGPT users rated the outcomes of the advice they received, including whether it was helpful, harmful, neutral, or uncertain, based on a 2025 U.S. survey.

Large language models present new opportunities for teaching and learning. The response accuracy of these models, however, is believed to depend on the prompt quality which can be a challenge for students. In this study, we aimed to explore how undergraduate students use ChatGPT for problem-solving, what prompting strategies they develop, the link between these strategies and the model’s response accuracy, the existence of individual prompting tendencies, and the impact of gender in this context. Our students used ChatGPT to solve five problems related to embedded systems and provided the solutions and the conversations with this model. We analyzed the conversations thematically to identify prompting strategies and applied different quantitative analyses to establish relationships between these strategies and the response accuracy and other factors. The findings indicate that students predominantly employ three types of prompting strategies: single copy-and-paste prompting (SCP), single reformulated prompting (SRP), and multiple-question prompting (MQP). ChatGPT’s response accuracy using SRP and MQP was significantly higher than using SCP, with effect sizes of -0.94 and -0.69, respectively. The student-by-student analysis revealed some tendencies. For example, 26 percent of the students consistently copied and pasted the questions into ChatGPT without any modification. Students who used MQP showed better performance in the final exam than those who did not use this prompting strategy. As for gender, female students tended to make extensive use of SCP, whereas male students tended to mix SCP and MQP. We conclude that students develop different prompting strategies that lead to different response qualities and learning. More research is needed to deepen our understanding and inform effective educational practices in the AI era.

A major challenge of our time is reducing disparities in access to and effective use of digital technologies, with recent discussions highlighting the role of AI in exacerbating the digital divide. We examine user characteristics that predict usage of the AI-powered conversational agent ChatGPT. We combine behavioral and survey data in a web tracked sample of N=1376 German citizens to investigate differences in ChatGPT activity (usage, visits, and adoption) during the first 11 months from the launch of the service (November 30, 2022). Guided by a model of technology acceptance (UTAUT-2), we examine the role of socio-demographics commonly associated with the digital divide in ChatGPT activity and explore further socio-political attributes identified via stability selection in Lasso regressions. We confirm that lower age and higher education affect ChatGPT usage, but neither gender nor income do. We find full-time employment and more children to be barriers to ChatGPT activity. Using a variety of social media was positively associated with ChatGPT activity. In terms of political variables, political knowledge and political self-efficacy as well as some political behaviors such as voting, debating political issues online and offline and political action online were all associated with ChatGPT activity, with online political debating and political self-efficacy negatively so. Finally, need for cognition and communication skills such as writing, attending meetings, or giving presentations, were also associated with ChatGPT engagement, though chairing/organizing meetings was negatively associated. Our research informs efforts to address digital disparities and promote digital literacy among underserved populations by presenting implications, recommendations, and discussions on ethical and social issues of our findings.

As large language models (LLMs) such as GPT have become more accessible, concerns about their potential effects on students’ learning have grown. In data science education, the specter of students’ turning to LLMs raises multiple issues, as writing is a means not just of conveying information but of developing their statistical reasoning. In our study, we engage with questions surrounding LLMs and their pedagogical impact by: (a) quantitatively and qualitatively describing how select LLMs write report introductions and complete data analysis reports; and (b) comparing patterns in texts authored by LLMs to those authored by students and by published researchers. Our results show distinct differences between machine-generated and human-generated writing, as well as between novice and expert writing. Those differences are evident in how writers manage information, modulate confidence, signal importance, and report statistics. The findings can help inform classroom instruction, whether that instruction is aimed at dissuading the use of LLMs or at guiding their use as a productivity tool. It also has implications for students’ development as statistical thinkers and writers. What happens when they offload the work of data science to a model that doesn’t write quite like a data scientist? Supplementary materials for this article are available online.

Adults with the highest education level - particularly with a postgraduate degree - had the greatest level of familiarity with ChatGPT, or ** percent having some knowledge. The program, developed by startup OpenAI, was of far less concern to those with high school degrees or lower education. When looking at respondents with a little knowledge of ChatGPT, the ******* are far less drastically different. It is quite likely that the considerable coverage of the ChatGPT topic in media had an impact, giving most people some awareness of the topic.

Input and output data from long-term learning in mathematical statistics using ChatGPT