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.

In a survey conducted across four Southeast Asian countries in February 2023, ** percent of the respondents in Singapore selected the collection of personal data as one of the concerns they had regarding the usage of chatbots like ChatGPT. In contrast, this was an issue for ** percent of respondents in Indonesia.

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...

In January 2024, ChatGPT online domain chat.openai.com registered over **** percent of its traffic as originating in the United States. Users based in India generated approximately **** percent of the total visits to the chatbot platform, while users in Indonesia accounted for *** percent of the total visits to the website. Visits from Brazil represented the fourth-largest group for the platform, generating more than **** percent of the total traffic recorded in the examined period.

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.

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.

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.

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.

As of 2023, around ** percent of the global population claim to be aware of ChatGPT. The countries with the highest awareness worldwide were India, Kenya, Indonesia, and Pakistan — all with over ** percent of awareness among the respondents.

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.

ChatGPT, an artificial intelligence (AI) powered chatbot, is most used by companies in the technical and education industries, with over 200 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 a 100 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.

IntroductionIn recent years, numerous AI tools have been employed to equip learners with diverse technical skills such as coding, data analysis, and other competencies related to computational sciences. However, the desired outcomes have not been consistently achieved. This study aims to analyze the perspectives of students and professionals from non-computational fields on the use of generative AI tools, augmented with visualization support, to tackle data analytics projects. The focus is on promoting the development of coding skills and fostering a deep understanding of the solutions generated. Consequently, our research seeks to introduce innovative approaches for incorporating visualization and generative AI tools into educational practices.MethodsThis article examines how learners perform and their perspectives when using traditional tools vs. LLM-based tools to acquire data analytics skills. To explore this, we conducted a case study with a cohort of 59 participants among students and professionals without computational thinking skills. These participants developed a data analytics project in the context of a Data Analytics short session. Our case study focused on examining the participants' performance using traditional programming tools, ChatGPT, and LIDA with GPT as an advanced generative AI tool.ResultsThe results shown the transformative potential of approaches based on integrating advanced generative AI tools like GPT with specialized frameworks such as LIDA. The higher levels of participant preference indicate the superiority of these approaches over traditional development methods. Additionally, our findings suggest that the learning curves for the different approaches vary significantly. Since learners encountered technical difficulties in developing the project and interpreting the results. Our findings suggest that the integration of LIDA with GPT can significantly enhance the learning of advanced skills, especially those related to data analytics. We aim to establish this study as a foundation for the methodical adoption of generative AI tools in educational settings, paving the way for more effective and comprehensive training in these critical areas.DiscussionIt is important to highlight that when using general-purpose generative AI tools such as ChatGPT, users must be aware of the data analytics process and take responsibility for filtering out potential errors or incompleteness in the requirements of a data analytics project. These deficiencies can be mitigated by using more advanced tools specialized in supporting data analytics tasks, such as LIDA with GPT. However, users still need advanced programming knowledge to properly configure this connection via API. There is a significant opportunity for generative AI tools to improve their performance, providing accurate, complete, and convincing results for data analytics projects, thereby increasing user confidence in adopting these technologies. We hope this work underscores the opportunities and needs for integrating advanced LLMs into educational practices, particularly in developing computational thinking skills.

Introduction

OpenAI Statistics: OpenAI, Inc. is an AI company based in San Francisco, California, and was started in December 2015. Its main goal is to build powerful and safe AI systems. OpenAI wants to create smart machines, called AGI, that can do most jobs better than humans, especially the ones that add economic value. This is also best known for developing advanced AI tools like ChatGPT, designed to solve real-world problems and improve daily life. Its mission is to make powerful AI available to everyone in a way that benefits society.

This article includes several current statistical analyses that are taken from different insights, which will guide in understanding the topic effectively as it covers the overall market, sales, user demographics, usage shares, website traffic, and many other factors.

Large Language Models (LLMs) have recently gathered attention with the release of ChatGPT, a user-centered chatbot released by OpenAI. In this perspective article, we retrace the evolution of LLMs to understand the revolution brought by ChatGPT in the artificial intelligence (AI) field.The opportunities offered by LLMs in supporting scientific research are multiple and various models have already been tested in Natural Language Processing (NLP) tasks in this domain.The impact of ChatGPT has been huge for the general public and the research community, with many authors using the chatbot to write part of their articles and some papers even listing ChatGPT as an author. Alarming ethical and practical challenges emerge from the use of LLMs, particularly in the medical field for the potential impact on public health. Infodemic is a trending topic in public health and the ability of LLMs to rapidly produce vast amounts of text could leverage misinformation spread at an unprecedented scale, this could create an “AI-driven infodemic,” a novel public health threat. Policies to contrast this phenomenon need to be rapidly elaborated, the inability to accurately detect artificial-intelligence-produced text is an unresolved issue.

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.

We tested ChatGPT on 25 tasks focusing on solving common NLP problems and requiring analytical reasoning. These tasks include (1) a relatively simple binary classification of texts like spam, humor, sarcasm, aggression detection, or grammatical correctness of the text; (2) a more complex multiclass and multi-label classification of texts such as sentiment analysis, emotion recognition; (3) reasoning with the personal context, i.e., personalized versions of the problems that make use of additional information about text perception of a given user (user’s examples provided to ChatGPT); (4) semantic annotation and acceptance of the text going towards natural language understanding (NLU) like word sense disambiguation (WSD), and (5) answering questions based on the input text. More information in the paper: https://www.sciencedirect.com/science/article/pii/S156625352300177X

This dataset provides a collection of user reviews for the ChatGPT mobile application on iOS. It captures valuable user insights and sentiments, making it suitable for understanding customer satisfaction, evaluating app performance, and identifying emerging trends. The data was gathered by scraping ChatGPT reviews from the App Store.

Columns

• date: The date the review was posted.
• title: The heading or title of the user's review.
• review: The full text of the user's review.
• rating: The star rating provided by the user.

Distribution

The dataset is typically provided in a CSV file format. It includes 2058 unique date values and 2257 unique review texts. The reviews span from 18th May 2023 to 25th July 2023. Review counts by period are as follows: * 18th May 2023 - 25th May 2023: 1,475 reviews * 25th May 2023 - 1st June 2023: 267 reviews * 1st June 2023 - 7th June 2023: 117 reviews * 7th June 2023 - 14th June 2023: 82 reviews * 14th June 2023 - 21st June 2023: 60 reviews * 21st June 2023 - 28th June 2023: 59 reviews * 28th June 2023 - 4th July 2023: 73 reviews * 4th July 2023 - 11th July 2023: 45 reviews * 11th July 2023 - 18th July 2023: 57 reviews * 18th July 2023 - 25th July 2023: 57 reviews

Rating distribution is also available: * 1.00 - 1.40 stars: 495 reviews * 1.80 - 2.20 stars: 139 reviews * 3.00 - 3.40 stars: 220 reviews * 3.80 - 4.20 stars: 304 reviews * 4.60 - 5.00 stars: 1,134 reviews

Usage

This dataset is ideal for: * Sentiment analysis to gauge user emotions and opinions regarding the ChatGPT app. * Performance evaluation to identify factors contributing to high or low user ratings. * Pattern identification to uncover recurring themes and common issues in user feedback.

Coverage

The dataset covers reviews globally, spanning a time range from 18th May 2023 to 25th July 2023.

License

CC-BY-NC

Who Can Use It

• Data scientists and analysts for performing sentiment analysis and machine learning tasks on user-generated text.
• App developers and product managers seeking to understand user satisfaction and improve app features based on direct feedback.
• Market researchers for insights into user perceptions of AI applications and large language models.

Dataset Name Suggestions

• ChatGPT iOS App Reviews
• Mobile ChatGPT User Feedback
• App Store ChatGPT Review Data
• iOS App User Sentiment for ChatGPT

Attributes

Original Data Source: ChatGPT App Reviews

Supplemental Material Contents: · 1-Demographic Information.xlsx: contains the demographic information of the participants in the study. · 2-Forms.zip: contains the forms and questionnaires used to collect data for the experiment: demographic form, pre-study, post-study, and AAR/AI questionnaires. · 3-GitHub-Repository.zip: a copy of the GitHub repository used in the study. · 4-Tutorial Scripts.zip: script used in the experiment with the groups to be consistent with all participants. · 5-Logs-Rubric-Grades.zip: contains the participant data log (commit and PR), rubric for grading submissions, and grades. · 6-RQ1-Data-and-Analysis.zip: contains the data and analysis with respect to RQ1. · 7-RQ2-Data-and-Analysis.zip: contains the data and analysis with respect to RQ2. · 8-Participant Prompts.xlsx: contains the experimental group participant prompts with ChatGPT. 2. Forms.zip The forms zip contains the following files: · Demographics.pdf: a form used to collect demographic information from participants before the study. · Control Pre-Study Questionnaire.pdf: Pre study questionnaire control group (Self-Efficacy Questionnaire) · Control Post-Study Questionnaire.pdf: Post study questionnaire control group (NASA-TLX, Self-Efficacy Questionnaire) · Treatment - AAR_AI task.pdf: Pre and Post task AAR/AI questionnaire for experimental group. · Experimental Pre-Study Questionnaire.pdf: Pre study questionnaire experimental group (Self-Efficacy Questionnaire, Question for Familiarity with AI) · Experimental-Post Study Questionnaire.pdf: Post study questionnaire experimental group (AAR/AI step 7, Continuance Intention, NASA-TLX, HAI Guideline Questions, Self-Efficacy Questionnaire) 3-GitHub-Repository.zip The GitHub repository used in the study: contains the main.py code file and the Readme.md file (having the written instructions for the participants). 4-Tutorial Scripts.zip Contains: · Control-Script.pdf: Script for the control group. · Experimental-Script.pdf: Script for the experimental group. 5-Logs-Rubric-Grades.zip · rubric.pdf: Created rubric for grading task performance. · GitHub-Task3-Log.xlsx: File containing the data regarding the status of commit made and PR raised for each participant. · grades.xlsx: Detailed grades for each participant in experimental (treatment) and control groups. 6-RQ1-Data-and-Analysis.zip Note: The term 'treatment' has been used in the files of this folder to represent the experimental group: participants using ChatGPT for the tasks. · NASA TLX: folder containing the participant data (TLX.xlsx), code for statistical analysis (Stat-TLX.py) and statistical reports (analysis-TLX.csv). · Task Performance: folder containing the participant data (grades.xlsx & Scores.xlsx(overall grade)), code for statistical analysis (Stat-Correctness.py) and statistical reports (analysis.csv). · Self-Efficacy: folder containing: o Self-Efficacy-detailed.xlsx: participant data o Paired Stats: folder containing data (Total Self Efficacy.csv), code for statistical analysis(paired-stats.py), and statistical reports (analysis.csv). o Box plot: folder containing the code for generating the box plot and its output. · Continuance Intention.xlsx: participant data (experimental) for continuance intention of ChatGPT. · Stat-Table-H1-2-Paper.xlsx: Statistics table for NASA TLX and task performance as presented in the paper. 7-RQ2-Data-and-Analysis.zip · AAR_AI-Responses.xlsx: AAR/AI responses filled by participants in experimental group. · Quotation Manager-Faults&Conseq.xlsx: Contains the quotations from AAR/AI responses along with corresponding codes. Also contains the quotes that link faults to consequences in a separate sheet. · Codebook.xlsx: The final codebook (faults and consequences). · HAI-data.xlsx: Contains the reported guideline violations along with disaggregated analysis (grouped by gender). · Likert Plot-HAI: folder contains the code for generating the Likert plot figure presented in the paper.

This dataset provides a daily-updated collection of user reviews and ratings specifically for the ChatGPT Android application. It includes crucial information such as the review text, associated ratings, and the dates when reviews were posted. The dataset also details the relevancy of each review. It serves as a valuable resource for understanding user sentiment, tracking app performance over time, and analysing trends within the AI and Large Language Model (LLM) application landscape.

Columns

• reviewId: A unique identifier assigned to each user review.
• id: An additional, distinct identifier associated with each review.
• userName: The name of the user who submitted the review.
• content: The textual body or comment provided by the user in their review.
• score: The numerical rating given by the user, typically on a scale from 1 to 5, indicating their satisfaction or experience.
• thumbsUpCount: The total number of 'likes' or positive reactions that a particular review has received.
• appVersion: The specific version of the ChatGPT Android application that the user was running when they posted their review.
• reviewDate: The precise date and time at which the user review was originally posted.

Distribution

The dataset is primarily available in a tabular format, typically a CSV file, facilitating easy integration and analysis. It comprises over 637,000 unique reviews, reflecting a substantial volume of user feedback. This dataset is updated on a daily basis, ensuring access to the latest user opinions and rating trends. While the exact file size is not specified, the number of records indicates a considerable volume of data.

Usage

This dataset is ideal for various analytical applications, including: * Sentiment Analysis: Extracting and understanding user emotions and opinions towards the ChatGPT Android app. * Natural Language Processing (NLP) Tasks: Training and testing NLP models for text classification, entity recognition, and language generation based on real-world user input. * App Performance Monitoring: Tracking changes in user ratings and feedback over time to gauge application performance and identify areas for improvement. * Market Research: Gaining insights into user perception of AI and LLM applications within the mobile market. * Competitive Analysis: Comparing user feedback for the ChatGPT app against other similar applications. * Feature Prioritisation: Identifying desired features or common pain points mentioned by users to inform product development.

Coverage

This dataset offers global coverage, collecting reviews from users across the world. The time range for the reviews spans from 25 July 2023 to 30 June 2025. This extensive period allows for longitudinal studies of user sentiment and app evolution. It captures feedback from a diverse demographic of ChatGPT Android app users. Some data points, such as appVersion, may occasionally have null values.

License

CC-BY-NC-SA

Who Can Use It

• Data Scientists and Analysts: For conducting sentiment analysis, topic modelling, and training machine learning models on large volumes of text data.
• App Developers and Product Managers: To understand user feedback, identify bugs, track feature requests, and plan future app enhancements.
• Market Researchers: To assess market trends, consumer behaviour, and the reception of AI-powered applications.
• Academic Researchers: For studies on human-AI interaction, user experience, and public perception of emerging technologies.
• Businesses in the AI/LLM Sector: To perform competitive analysis and inform strategic decisions based on real user feedback.

Dataset Name Suggestions

• ChatGPT Android App Reviews
• Daily ChatGPT Mobile Ratings
• AI App User Feedback Data
• ChatGPT Google Play Reviews
• User Sentiment for ChatGPT App

Attributes

Original Data Source: ChatGPT reviews [DAILY UPDATED]

This dataset provides user reviews for ChatGPT, offering valuable qualitative feedback, satisfaction ratings, and submission dates. It captures a diverse array of user sentiments, from concise remarks to more detailed feedback. The ratings are provided on a scale of 1 to 5, indicating different levels of user satisfaction. The dataset spans several months, which allows for temporal analysis of sentiment trends, as each review includes a timestamp. This data is ideal for gaining insights into user characteristics and for improving application features and services.

Columns

• Review Id: A unique identifier for each individual review. This is formatted as a String, typically in a UUID structure.
• Review: The actual text of the user's feedback, offering qualitative insights into their experience with the application. This is a String data type.
• Ratings: User-submitted numerical ratings, ranging from 1 (lowest satisfaction) to 5 (highest satisfaction), indicating their level of contentment. This is an Integer data type.
• Review Date: The timestamp when the review was originally submitted, recorded in MM/DD/YYYY HH:MM format, serving as a Date_Time data type.

Distribution

The dataset is provided as a free resource. While a sample file will be updated separately to the platform, the data quality is assessed as 5 out of 5, with the current version being 1.0. It was listed on 08/06/2025, with 1 view and 0 downloads recorded so far. The dataset contains approximately 193,154 unique reviews.

Usage

This dataset is particularly useful for various analytical applications, including: * Sentiment Analysis: Developing models to predict the emotional tone or sentiment conveyed in user reviews. * Customer Feedback Analysis: Extracting actionable insights that can inform and guide improvements to application features and services. * Review Classification: Building machine learning models to categorise user reviews, for instance, as positive or negative. * Data Visualisation: Creating visual representations of review patterns and trends. * Exploratory Data Analysis: Investigating the characteristics and underlying patterns within the review data. * Natural Language Processing (NLP): Applying NLP techniques to understand and process the textual feedback. * Text Mining: Discovering patterns and insights from the large collection of text reviews. * Time-Series Analysis: Examining how sentiment and ratings evolve over time based on review timestamps.

Coverage

This dataset comprises user reviews for ChatGPT collected from 25th July 2023 to 24th August 2024. The data collection is global, reflecting feedback from users worldwide.

License

CCO

Who Can Use It

This dataset is ideal for a range of users interested in understanding user feedback and sentiment, including: * Data Scientists and Machine Learning Engineers for building and training sentiment analysis and classification models. * Product Managers and App Developers to gain actionable insights for product improvement and feature development. * Market Researchers to understand user satisfaction and market perception of AI applications. * Academic Researchers studying human-computer interaction, natural language processing, or user behaviour.

Dataset Name Suggestions

• ChatGPT User Reviews
• GPT User Review Sentiment Data
• AI App User Feedback Dataset
• ChatGPT User Satisfaction Ratings

Attributes

Original Data Source: ChatGPT Users Reviews