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.

ChatGPT was the chatbot that kickstarted the generative AI revolution, which has been responsible for hundreds of billions of dollars in data centres, graphics chips and AI startups. Launched by...

ChatGPT's monthly active users in India grossed just under ** million as of the second quarter of 2025. This was a growth of *** percent over the same period in 2024. While ChatGPT was the leading AI platform used in India in terms of downloads and MAUs, Perplexity was the fastest growing of the two.

Global AI Tool Adoption Across Industries and Regions (2023–2025)

A comprehensive, research-grade dataset capturing the adoption, usage, and impact of leading AI tools—such as ChatGPT, Midjourney, Stable Diffusion, Bard, and Claude—across multiple industries, countries, and user demographics. This dataset is designed for advanced analytics, machine learning, natural language processing, and business intelligence applications.

Dataset Overview

This dataset provides a panoramic view of how AI technologies are transforming business, industry, and society worldwide. Drawing inspiration from real-world adoption surveys, academic research, and industry reports, it enables users to:

• Analyze adoption rates of popular AI tools across regions and sectors.
• Study user demographics and company profiles influencing AI integration.
• Explore textual user feedback for sentiment and topic modeling.
• Perform time series analysis on AI adoption trends from 2023 to 2025.
• Benchmark industries, countries, and company sizes for AI readiness.

To add a column descriptor (column description) to your Kaggle dataset's Data Card, you should provide a clear and concise explanation for each column. This improves dataset usability and helps users understand your data structure, which is highly recommended for achieving a 10/10 usability score on Kaggle[2][9].

Below is a ready-to-copy Column Descriptions table for your dataset. You can paste this into the "Column Descriptions" section of your Kaggle Data Card (after clicking the pencil/edit icon in the Data tab)[2][9]:

Column Descriptions

Example Data

country,industry,ai_tool,adoption_rate,daily_active_users,year,user_feedback,age_group,company_size
USA,Technology,ChatGPT,78.5,5423,2024,"Great productivity boost for our team!",25-34,Enterprise
India,Healthcare,Midjourney,62.3,2345,2024,"Improved patient engagement and workflow.",35-44,SME
Germany,Manufacturing,Stable Diffusion,45.1,1842,2023,"Enhanced our design process.",45-54,Enterprise
Brazil,Retail,Bard,33.2,1200,2024,"Helped automate our customer support.",18-24,Startup
UK,Finance,Claude,55.7,2100,2023,"Increased accuracy in financial forecasting.",25-34,SME

How to Use This Dataset

1. Load and Preview the Data

import pandas as pd

df = pd.read_csv('/path/to/ai_adoption_dataset.csv')
print(df.head())
print(df.info())

2. Analyze Adoption Rates by Industry and Country

industry_adoption = df.groupby(['industry', 'country'])['adoption_rate'].mean().reset_index()
print(industry_adoption.sort_values(by='adoption_rate', ascending=False).head(10))

3. Visualize AI Tool Popularity

import matplotlib.pyplot as plt

tool_counts = df['ai_tool'].value_counts()
tool_counts.plot(kind='bar', title='AI Tool Usage Distribution')
plt.xlabel('AI Tool')
plt.ylabel('Number of Records')
plt.show()

4. Sentiment Analysis on User Feedback

from textblob import TextBlob

df['feedback_sentiment'] = df['user_feedback'].apply(lambda x: TextBlob(x).sentiment.polarity)
print(df[['user_feedback', 'feedback_sentiment']].head())

5. Time Series Analysis of Adoption Trends

yearly_trends = df.groupby(['year', 'ai_tool'])['adoption_rate'].mean().unstack()
yearly_trends.plot(marker='o', title='AI Tool Adoption Rate Over Time')
plt.xlabel('Year')
plt.ylabel('Average Adoption Rate (%)')
plt.show()

**6. Demographic Insights*...

IntroductionThe recent advances of large language models (LLMs) have opened a wide range of opportunities, but at the same time, they pose numerous challenges and questions that research needs to answer. One of the main challenges are the quality and correctness of the output of LLMs as well as the overreliance of students on the output without critically reflecting on it. This poses the question of the quality of the output of LLMs in educational tasks and what students and teachers need to consider when using LLMs for creating educational items. In this work, we focus on the quality and characteristics of conceptual items developed using ChatGPT without user-generated improvements.MethodsFor this purpose, we optimized prompts and created 30 conceptual items in kinematics, which is a standard topic in high-school level physics. The items were rated by two independent experts. Those 15 items that received the highest rating were included in a conceptual survey. The dimensions were designed to align with the ones in the most commonly used concept inventory, the Force Concept Inventory (FCI). We administered the designed items together with the FCI to 172 first-year university students. The results show that ChatGPT items have a medium difficulty and discriminatory index but they overall exhibit a slightly lower average values as the FCI. Moreover, a confirmatory factor analysis confirmed a three factor model that is closely aligned with a previously suggested expert model.Results and discussionIn this way, after careful prompt engineering, thorough analysis and selection of fully automatically generated items by ChatGPT, we were able to create concept items that had only a slightly lower quality than carefully human-generated concept items. The procedures to create and select such a high-quality set of items that is fully automatically generated require large efforts and point towards cognitive demands of teachers when using LLMs to create items. Moreover, the results demonstrate that human oversight or student interviews are necessary when creating one-dimensional assessments and distractors that are closely aligned with students' difficulties.

In October 2025, ChatGPT’s mobile app recorded ***** 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 latest month observed, the app registered ***** million downloads, a *****percent decline from September 2025, but higher than the download count of ChatGPT. Regional preferences shape AI app adoption ChatGPT has a strong global presence with over ****** million monthly active users in February 2025, but regional preferences vary. In the United States, ChatGPT had a **-percent download market share, compared to Google Gemini's ** percent. However, Gemini emerged as the preferred generative AI app in India, representing a **-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 *** billion U.S. dollars in 2024 and projected to surpass *** billion U.S. dollars by 2030. This expansion is mirrored in user behavior, with around ** million adults in the United States using AI-powered tools as their first option for online search in 2024. Additionally, ** percent of U.S. adults reported the use of AI-powered search engines for exploring new topics in 2024, with another ** percent of respondents utilizing these tools to learn or explain concepts.

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.

Have you ever missed a tiny but critical requirement in your project? I have. And I thought what if an AI could catch those gaps before QA or PMs do?

That’s how the idea for AI Requirements Guardian was born a smart, self-learning assistant for system and business analysts.

What It Does

The MVP is simple but powerful:

• You send your requirement or user story to the AI.
• It analyzes it for missing details — SLA, timeout rules, error handling, edge cases, etc.
• It learns from feedback. Every time an analyst confirms or rejects its suggestion, the AI updates its internal patterns.
• It builds a private knowledge base of your team’s typical mistakes and improvements.

In short, it’s like having an experienced senior analyst watching your back — 24/7.

How It Works

The app is built on Python + FastAPI + LangChain + FAISS, powered by OpenAI embeddings. It connects to tools like Jira, Confluence, and Git, where it collects real examples of past tickets and requirements. Over time, it recognizes recurring problems — missing NFRs, incomplete acceptance criteria, weak retry policies — and gives context-aware feedback based on your own company’s data.

Why It’s Different from ChatGPT

ChatGPT can help you rewrite a requirement, but it doesn’t remember your past mistakes. AI Requirements Guardian does. It becomes smarter with every feedback round — adapting to your style, your terminology, and even your team’s blind spots.

This isn’t a chat model — it’s a continuous learning assistant that grows with your team.

How Analysts and Developers Can Use It

• Analysts can use it to verify requirements before sending them to devs.
• Developers can use it to spot missing details early — before coding starts.
• Teams can build their own “corporate memory” of quality standards and reuse it across projects.

Over time, it can even highlight your most common requirement gaps — helping you continuously improve.

This small MVP is a step toward something bigger an AI that learns your process, not just the language. It doesn’t replace analysts. It empowers them — by automating the boring, repetitive review work and letting humans focus on the creative part of analysis.

Project Structure:

ai-req-guardian/ app/ main.py # FastAPI endpoints (/analyze, /ingest, /feedback, /stats) analyzer.py # Core analysis logic: patterns, similarity search, AI summary embedding_store.py # FAISS + metadata wrapper for embeddings and vector storage ingestion/ jira_client.py # Jira REST API integration confluence_client.py # Confluence REST API integration git_client.py # GitHub/GitLab integration for commits and issue context self_train.py # Feedback handler for self-learning and weight updates patterns.py # Default patterns for requirement gaps (SLA, retry, ACL, etc.) data/ faiss.index # Vector index for embedded requirement examples meta.db # SQLite DB storing examples, feedback, and pattern stats prompts/ analyze_requirement.md # Prompt template for AI summary generation requirements.txt # Project dependencies README.md # Documentation

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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.

Comparison of AI model performance metrics by Model

Comprehensive comparison of Artificial Analysis Intelligence Index vs. Total Parameters (Billions, Log Scale) by Model

As of October 2025, Google represented ***** percent of the global online search engine referrals on desktop devices. Despite being much ahead of its competitors, this represents a modest increase from the previous months. Meanwhile, its longtime competitor Bing accounted for ***** percent, as tools like Yahoo and Yandex held shares of over **** percent and **** percent respectively. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of **** trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly ****** billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than ** percent of internet users in Russia used Yandex, whereas Google users represented little over ** percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over ** percent of users in Mexico said they used Yahoo.

As of August 2024, ************** held the record among selected online platforms and services for reaching a total of *** million users. The social media platform achieved the milestone in just ********. ******* surpassed the 100 million user threshold in just two months, and video app ****** surpassed the 100 million followers mark in ************