ChatGPT is used most widely among those between ** and ** around the world. The youngest group, those under **, are the second largest userbase, and together those under ** account for over ** 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 ** percent of total users in 2023.

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: 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.

Annual users of ChatGPT worldwide are expected to grown considerably in the coming months. In 2022, around 57 million people used ChatGPT, with this number increasing to roughly 100 million by January 2023.

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.

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.

In February 2025, ChatGPT was the most popular artificial intelligence (AI) application worldwide, with over 400.61 million monthly active users (MAU). The ByteDance-owned chatbot Doubao had around 81.91 million MAU, making it the most popular Chinese-based tool of this kind. ChatGPT-operated Nova Assistant ranked third with 62.79 million MAU and was followed by Chinese-based DeepSeek with around 61.81 million MAU.

UPDATE: Due to new Twitter API conditions changed by Elon Musk, now it's no longer free to use the Twitter (X) API and the pricing is 100 $/month in the hobby plan. So my automated ETL notebook stopped from updating new tweets to this dataset on May 13th 2023.

This dataset is was updated everyday with the addition of 1000 tweets/day containing any of the words "ChatGPT", "GPT3", or "GPT4", starting from the 3rd of April 2023. Everyday's tweets are uploaded 24-72h later, so the counter on tweets' likes, retweets, messages and impressions gets enough time to be relevant. Tweets are from any language selected randomly from all hours of the day. There are some basic filters applied trying to discard sensitive tweets and spam.

This dataset can be used for many different applications regarding to Data Analysis and Visualization but also NLP Sentiment Analysis techniques and more.

Consider upvoting this Dataset and the ETL scheduled Notebook providing new data everyday into it if you found them interesting, thanks! 🤗

Columns Description:

• tweet_id: Integer. unique identifier for each tweet. Older tweets have smaller IDs.

• tweet_created: Timestamp. Time of the tweet's creation.

• tweet_extracted: Timestamp. The UTC time when the ETL pipeline pulled the tweet and its metadata (likes count, retweets count, etc).

• text: String. The raw payload text from the tweet.

• lang: String. Short name for the Tweet text's language.

• user_id: Integer. Twitter's unique user id.

• user_name: String. The author's public name on Twitter.

• user_username: String. The author's Twitter account username (@example)

• user_location: String. The author's public location.

• user_description: String. The author's public profile's bio.

• user_created: Timestamp. Timestamp of user's Twitter account creation.

• user_followers_count: Integer. The number of followers of the author's account at the moment of the tweet extraction

• user_following_count: Integer. The number of followed accounts from the author's account at the moment of the Tweet extraction

• user_tweet_count: Integer. The number of Tweets that the author has published at the moment of the Tweet extraction.

• user_verified: Boolean. True if the user is verified (blue mark).

• source: The device/app used to publish the tweet (Apparently not working, all values are Nan so far).

• retweet_count: Integer. Number of retweets to the Tweet at the moment of the Tweet extraction.

• like_count: Integer. Number of Likes to the Tweet at the moment of the Tweet extraction.

• reply_count: Integer. Number of reply messages to the Tweet.

• impression_count: Integer. Number of times the Tweet has been seen at the moment of the Tweet extraction.

More info: Tweets API info definition: https://developer.twitter.com/en/docs/twitter-api/data-dictionary/object-model/tweet Users API info definition: https://developer.twitter.com/en/docs/twitter-api/data-dictionary/object-model/user

In March 2025, ChatGPT’s mobile app recorded over 64.26 million App Store and Google Play downloads worldwide. Google's Gemini AI Assistant mobile app was released on February 8, 2024, and was initially available in the U.S. market only. In the same month, the app registered around 13.92 million downloads. Regional preferences shape AI app adoption ChatGPT has a strong global presence with over 400.61 million monthly active users in February 2025, but regional preferences vary. In the United States, ChatGPT had a 45 percent download market share, compared to Google Gemini's 11 percent. However, Gemini emerged as the preferred generative AI app in India, representing a 52 percent market share. This competitive landscape now also includes Chinese-based players like ByteDance's Doubao and DeepSeek, indicating an even more diverse and evolving AI worldwide ecosystem. The AI-powered revolution in online search The global AI market has experienced substantial growth, exceeding 184 billion U.S. dollars in 2024 and projected to surpass 826 billion U.S. dollars by 2030. This expansion is mirrored in user behavior, with around 15 million adults in the United States using AI-powered tools as their first option for online search in 2024. Additionally, 68 percent of U.S. adults reported the use of AI-powered search engines for exploring new topics in 2024, with another 44 percent of respondents utilizing these tools to learn or explain concepts.

This dataset mainly consists of daily-updated user reviews and ratings for the ChatGPT Android App. It also contains data on the relevancy of these reviews and the dates they were posted.

A chatbot from Chinese AI lab DeepSeek sent shockwaves through the market in January, due to its ability to perform mathematics, coding and reasoning at a similar level to ChatGPT and other top-tier...

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.

As of 2023, about ** percent of the global population who are familiar with ChatGPT were using the tool at least once a month, while over ** percent reported using it weekly. Indian respondents were the most frequent users, having just over ** percent of their respondents claimed to use ChatGPT every day.

This research analyzes the factors that affect university students in the Jabodetabek area to continue using ChatGPT in their learning process. With the fast adoption of generative AI tools in education, ChatGPT is one of several platforms that has become widely used in the world for academic assistance. For the sustainability of ChatGPT usage not only gets affected by technical capabilities but also by psychological and social factors. This research used extended Technology Acceptance Model (TAM) by combining mediating variables like Perceived Trust and Satisfaction, and external variables like Competence and Social Influence. 415 valid respondents who are active university students and ChatGPT users collected with quantitative methods. SMART Partial Least Squares Structural Equation Modeling (PLS-SEM), SmartPLS 4 were used to analyze the data. All hypotheses proposed are significant statistically, with the most influential factors are Perceived Usefulness and Competence. Satisfaction and Trust serve as critical mediators, and Continuous Usage Behavior is also affected by Social Influence. The results found that the importance of improving digital competence of the students, trust, and fostering ethical AI usage in academic policies and support systems, provides valuable implications for future studies and academic exploration.

In March 2025, ChatGPT.com received approximately *** billion visits from users worldwide. The most recent year under analysis has seen an increase in traffic to OpenAI's artificial intelligence chatbot. This is the highest traffic volume achieved by the site to date, with values for the most recent analyzed month exceeding twice the average monthly visits for the entire examined period between April 2023 and April 2024.

🧠 Awesome ChatGPT Prompts [CSV dataset]

This is a Dataset Repository of Awesome ChatGPT Prompts View All Prompts on GitHub

  License

CC-0

Google Gemini Statistics: In 2023, Google unveiled the most powerful AI model to date. Google Gemini is the world’s most advanced AI leaving the ChatGPT 4 behind in the line. Google has 3 different sizes of models, superior to each, and can perform tasks accordingly. According to Google Gemini Statistics, these can understand and solve complex problems related to absolutely anything. Google even said, they will develop AI in such as way that it will let you know how helpful AI is in our daily routine. Well, we hope our next generation won’t be fully dependent on such technologies, otherwise, we will lose all of our natural talent! Editor’s Choice Google Gemini can follow natural and engaging conversations. According to Google Gemini Statistics, Gemini Ultra has a 90.0% score on the MMLU benchmark for testing the knowledge of and problem-solving on subjects including history, physics, math, law, ethics, history, and medicine. If you ask Gemini what to do with your raw material, it can provide you with ideas in the form of text or images according to the given input. Gemini has outperformed ChatGPT -4 tests in the majority of the cases. According to the report this LLM is said to be unique because it can process multiple types of data at the same time along with video, images, computer code, and text. Google is considering its development as The Gemini Era, showing the importance of our AI is significant in improving our daily lives. Google Gemini can talk like a real person Gemini Ultra is the largest model and can solve extremely complex problems. Gemini models are trained on multilingual and multimodal datasets. Gemini’s Ultra performance on the MMMU benchmark has also outperformed the GPT-4V in the following results Art and Design (74.2), Business (62.7), Health and Medicine (71.3), Humanities and Social Science (78.3), and Technology and Engineering (53.00).

In the period between its release in November 2022 and January 2024, ChatGPT saw the average duration of global visits to its web domain, chat.openai.com, increase sensibly. As of the last examined month, visitors worldwide spent *** seconds on average in the platform's domain, equating to ** minutes and ** seconds. The peak of the chatbot's website session length happened in October 2023, when users worldwide spent an average of *** seconds on the web page.

The AI audio glasses market is experiencing rapid growth, driven by increasing demand for hands-free communication and smart assistance features. The market, estimated at $2 billion in 2025, is projected to exhibit a robust Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching an estimated $12 billion by 2033. This expansion is fueled by several key drivers. Firstly, technological advancements in AI processing power and miniaturization enable sophisticated features like real-time translation, noise cancellation, and personalized audio experiences within compact wearable devices. Secondly, the growing adoption of smart assistants and the increasing integration of AI into everyday life are significantly boosting demand. Furthermore, the rising popularity of augmented reality (AR) and virtual reality (VR) applications is creating new use cases for AI audio glasses, particularly in fields like gaming and professional training. While high initial costs and potential privacy concerns pose certain restraints, the market’s overall trajectory remains positive. The market is segmented by application (personal and commercial) and type (ChatGPT-based, PanGu AI-based, and others), with the ChatGPT-based segment currently leading in terms of market share due to its extensive functionalities and established user base. Key players like Meta, Huawei, and Amazon are actively investing in research and development, driving innovation and competition within this burgeoning sector. The North American market currently holds a significant share, followed by Asia-Pacific, with growth expected across all regions, particularly in emerging economies driven by increasing smartphone penetration and rising disposable incomes. The diverse applications of AI audio glasses span various sectors. In the personal sphere, they provide enhanced convenience and accessibility for communication and information access. Commercial applications are rapidly expanding, finding utility in logistics, healthcare, and manufacturing for hands-free operation and improved worker efficiency. The "other" category encompasses emerging technologies and niche applications that are likely to gain prominence in the coming years, including advanced biometric sensing and integration with other wearables. Future growth will depend on continued innovation in AI algorithms, improved battery life, refined design, and addressing concerns related to data privacy and security to ensure widespread adoption. The competitive landscape is dynamic, characterized by established tech giants and innovative startups vying for market dominance through product differentiation and strategic partnerships. The ongoing evolution of AI capabilities and the ever-expanding demand for seamless integration of technology into daily life are poised to propel further growth in this exciting and evolving market.

Objective- The primary research objective for this study is to develop a reliable and valid scale to measure higher education students' knowledge, attitudes, and usage of GenAI.

GenAI refers to a type of artificial intelligence that generates content in response to prompts (Dwivedi et al., 2023 cited in Chiu, 2024). These prompts can include text, software code, images, videos, and music. The inception and rapid advancement of Generative AI in society has had a considerable impact on higher education.

Educators and students are beginning to apply GenAI for various purposes, including creating and enhancing learning environments, study resources, lesson plans, idea generation, data analysis, text summarisation and enhancement, and streamlining administrative processes (Francis, Jones & Smith, 2025; Freeman, 2025; Tillmans et al., 2025).

GenAI has the potential to enhance learning experiences by aiding learners, saving time, and empowering users to control their own educational journey. However, understanding students' knowledge, attitudes, and usage of GenAI is crucial for integrating these technologies effectively into educational settings. Despite the growing interest in GenAI, there is limited research on how students understand, perceive and use GenAI in their studies, and to our knowledge no freely available validated scale for effectively and quickly measuring knowledge, attitudes, and usage in a student cohort.

Understanding students' knowledge about GenAI is crucial for assessing their ability to engage with these technologies effectively and for the design of AI literacy training. Research carried out by Chan and Zhou (2023) on undergraduate and postgraduate students indicated that while students generally possess a basic understanding of GenAI applications and impacts, there is a significant gap in deeper technical knowledge and awareness of ethical implications. Educational interventions (e.g. online workshops) carried out by Putjorn and Putjorn (2023), emphasise the importance of integrating AI literacy into curricula to empower students with comprehensive knowledge of GenAI technologies which in turn prepares students for the modern job market where use of GenAI tools is becoming commonplace.

By studying people’s attitudes, we can better explain the decision-making and behaviour of individuals and communities, and create a supportive environment for responsible use (Cao et al., 2021). Factors that influence AI attitudes have been studied from demographic, personality, anxiety, and trust perspectives, with men reporting more positive attitudes towards AI (Liang and Lee, 2017, Schepman and Rodway, 2022). Regarding age and AI attitudes, the research results are contradictory (Kaya et al., 2022). However, most of the literature states that younger age is connected to more positive attitudes towards AI (Gillespie et al., 2021, Schepman and Rodway, 2022). Higher education has also been shown to relate to positive AI attitudes (European Commission, & Directorate-General for Communications Networks, Content and Technology, 2017, Neudert et al., 2020). However, a more comprehensive understanding of attitudes towards *Gen*AI among students will provide policy makers and educators with key insights needed to support student learning.

Research regarding students’ usage of AI is still emerging. Johnston et al. (2024) conducted a focus group where half of the students reported using or considering using GenAI for academic purposes. Although most students were supportive of using GenAI for grammar and spelling, most were unsupportive of the use of GenAI for assessment writing. Likewise, qualitative responses highlighted that students were unsupportive towards using GenAI for essay writing, as this was considered “cheating”, but GenAI could be used as an alternative to lecturers or to understand a concept. Smolansky et al. (2023) reported ‘moderate usage’ of GenAI tools among students for assignments and assessments relating to essay writing and coding. Findings from Smolansky et al. (2023) also highlighted concerns among students and educators regarding academic integrity and the use of GenAI in traditional assessments such as essay writing.

Currently, there is no comprehensive, validated and freely available tool specifically designed to measure students' knowledge, attitudes, and usage of GenAI. Indeed, knowledge, attitudes and usage are useful dimensions for measuring programs, products and technologies because understanding knowledge gaps, usage patterns, and attitudinal barriers helps in designing targeted interventions, policies, and educational programs. The creation of a questionnaire using these dimensions and tailored to GenAI will fill a measurement gap, providing a robust scale for future research and ongoing assessment. This scale will enable consistent tracking of changes over time and the effectiveness of interventions aimed at improving GenAI literacy and engagement. In conclusion, this study aims to fill the gap in the existing literature by developing and validating a scale to measure students' knowledge, attitudes, and usage of GenAI, and by exploring the factors that influence these dimensions. The findings will provide valuable insights for educators and policymakers to design GenAI education programs that are responsive to students' needs and concerns.

References Allam, H., Dempere, J., Akre, V., Parakash, D., Mazher, N., & Ahamed, J. (2023, May). Artificial intelligence in education: an argument of Chat-GPT use in education. In 2023 9th International Conference on Information Technology Trends (ITT) (pp. 151-156). IEEE. Bergdahl, J., Latikka, R., Celuch, M., Savolainen, I., Mantere, E. S., Savela, N., & Oksanen, A. (2023). Self-determination and attitudes toward artificial intelligence: Cross-national and longitudinal perspectives. Telematics and Informatics, 82, 102013. Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312. Chan, C. K. Y., & Zhou, W. (2023). Deconstructing student perceptions of generative AI (GenAI) through an expectancy value theory (EVT)-based instrument. arXiv preprint arXiv:2305.01186. Cotton, D. R., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in education and teaching international, 61(2), 228-239. Eurobarometer, S. (2017). 460-Attitudes Towards the Impact of Digitisation and Automation on Daily Life. European Commission, Brussels, EU. Francis, N. J., Jones, S., & Smith, D. P. (2025). Generative AI in higher education: Balancing innovation and integrity. British Journal of Biomedical Science, 81, 14048. Freeman, J. (2025). Student Generative AI Survey 2025. Technical report, HEPI, URL https://www. hepi. ac. uk/2025/02/26/student-generative-ai-survey-2025. Gillespie, N., Lockey, S., & Curtis, C. (2021). Trust in artificial intelligence: A five country study. Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Khan, I. H. (2023). Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 3(2), 100115. Johnston, H., Wells, R. F., Shanks, E. M., Boey, T., & Parsons, B. N. (2024). Student perspectives on the use of generative artificial intelligence technologies in higher education. International Journal for Educational Integrity, 20(1), 2. Liang, Y., & Lee, S. A. (2017). Fear of autonomous robots and artificial intelligence: Evidence from national representative data with probability sampling. International Journal of Social Robotics, 9, 379-384. Neudert, L. M., Knuutila, A., & Howard, P. N. (2020). Global attitudes towards AI, machine learning & automated decision making. Working paper 2020.10, Oxford Commission on AI & Good Governance. https://oxcaigg. oii. ox. ac. uk. Putjorn, T., & Putjorn, P. (2023, October). Augmented Imagination: Exploring Generative AI from the Perspectives of Young Learners. In 2023 15th International Conference on Information Technology and Electrical Engineering (ICITEE) (pp. 353-358). IEEE. Schepman, A., & Rodway, P. (2023). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human–Computer Interaction, 39(13), 2724-2741. Smolansky, A., Cram, A., Raduescu, C., Zeivots, S., Huber, E., & Kizilcec, R. F. (2023, July). Educator and student perspectives on the impact of generative AI on assessments in higher education. In Proceedings of the tenth ACM conference on Learning@ Scale (pp. 378-382) Stokel-Walker, C. (2022). AI bot ChatGPT writes smart essays-should academics worry?. Nature. Tillmanns, T., Salomão Filho, A., Rudra, S., Weber, P., Dawitz, J., Wiersma, E., ... & Reynolds, S. (2025). Mapping Tomorrow’s Teaching and Learning Spaces: A Systematic Review on GenAI in Higher Education. Trends in Higher Education, 4(1), 2. Yu, H. (2023). Reflection on whether Chat GPT should be banned by academia from the perspective of education and teaching. Frontiers in Psychology, 14, 1181712.