Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The dataset for this research project was meticulously constructed to investigate the adoption of ChatGPT among students in the United States. The primary objective was to gain insights into the technological barriers and resistances faced by students in integrating ChatGPT into their information systems. The dataset was designed to capture the diverse adoption patterns among students in various public and private schools and universities across the United States. By examining adoption rates, frequency of usage, and the contexts in which ChatGPT is employed, the research sought to provide a comprehensive understanding of how students are incorporating this technology into their information systems. Moreover, by including participants from diverse educational institutions, the research sought to ensure a comprehensive representation of the student population in the United States. This approach aimed to provide nuanced insights into how factors such as educational background, institution type, and technological familiarity influence ChatGPT adoption.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Dataset Card for Dataset Name
Dataset Summary
scraped-chatgpt-conversations contains ~100k conversations between a user and chatgpt that were shared online through reddit, twitter, or sharegpt. For sharegpt, the conversations were directly scraped from the website. For reddit and twitter, images were downloaded from submissions, segmented, and run through an OCR pipeline to obtain a conversation list. For information on how the each json file is structured, please see… See the full description on the dataset page: https://huggingface.co/datasets/ar852/scraped-chatgpt-conversations.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
https://choosealicense.com/licenses/odc-by/https://choosealicense.com/licenses/odc-by/
WildChat Filtered Dataset
This is a filtered version of the WildChat-4.8M dataset.
Dataset Description
This dataset contains 3,199,860 conversations between human users and ChatGPT, filtered to keep only the essential conversation structure.
Data Structure
Each conversation contains only:
conversations: A list of message objects with: role: Either "user" or "assistant" content: The text content of the message
All other metadata (timestamps, moderation… See the full description on the dataset page: https://huggingface.co/datasets/rayonlabs/wildchat-filtered.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset presents how much users trust ChatGPT across different advice categories, including career, education, financial, legal, and medical advice, based on a 2025 U.S. survey.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset compares how much U.S. adults trust ChatGPT relative to Google Search, including responses from a 2025 national survey measuring perceptions of AI accuracy and reliability.
https://choosealicense.com/licenses/cc0-1.0/https://choosealicense.com/licenses/cc0-1.0/
🧠 Awesome ChatGPT Prompts [CSV dataset]
This is a Dataset Repository of Awesome ChatGPT Prompts View All Prompts on GitHub
License
CC-0
https://www.technavio.com/content/privacy-noticehttps://www.technavio.com/content/privacy-notice
Chatbot Market Size 2025-2029
The chatbot market size is forecast to increase by USD 9.63 billion, at a CAGR of 42.9% between 2024 and 2029. Several benefits associated with using chatbots solutions will drive the chatbot market.
Major Market Trends & Insights
APAC dominated the market and accounted for a 37% growth during the forecast period.
By End-user - Retail segment was valued at USD 210.60 billion in 2023
By Product - Solutions segment accounted for the largest market revenue share in 2023
Market Size & Forecast
Market Opportunities: USD 1.00 billion
Market Future Opportunities: USD 9.63 billion
CAGR : 42.9%
APAC: Largest market in 2023
Market Summary
The market is a dynamic and evolving landscape, characterized by the integration of advanced technologies and innovative applications. Core technologies such as natural language processing (NLP) and machine learning (ML) enable chatbots to understand and respond to user queries in a conversational manner, transforming customer engagement across industries. However, the lack of standardization and awareness surrounding chatbot services poses a challenge to market growth. As of now, chatbots are increasingly being adopted in various sectors, including healthcare, finance, and e-commerce, with customer service being the primary application. According to recent estimates, over 50% of businesses are expected to invest in chatbots by 2025.
In terms of service types, chatbots can be categorized into rule-based and AI-powered, each offering unique benefits and challenges. Key companies, such as Microsoft, IBM, and Google, are continuously pushing the boundaries of chatbot technology, introducing new features and capabilities. Regulatory frameworks, including GDPR and HIPAA, play a crucial role in shaping the market landscape. Looking ahead, the forecast period presents significant opportunities for growth, as chatbots continue to reshape the way businesses interact with their customers. Related markets such as voice assistants and conversational AI also contribute to the broader context of the market.
Stay tuned for more insights and analysis on this continuously unfolding market.
What will be the Size of the Chatbot Market during the forecast period?
Get Key Insights on Market Forecast (PDF) Request Free Sample
How is the Chatbot Market Segmented and what are the key trends of market segmentation?
The chatbot industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.
End-user
Retail
BFSI
Government
Travel and hospitality
Others
Product
Solutions
Services
Deployment
Cloud-Based
On-Premise
Hybrid
Application
Customer Service
Sales and Marketing
Healthcare Support
E-Commerce Assistance
Geography
North America
US
Canada
Europe
France
Germany
Italy
UK
Middle East and Africa
Egypt
KSA
Oman
UAE
APAC
China
India
Japan
South America
Argentina
Brazil
Rest of World (ROW)
By End-user Insights
The retail segment is estimated to witness significant growth during the forecast period.
The market is experiencing significant growth, with adoption in various sectors escalating at a remarkable pace. According to recent reports, the chatbot industry is projected to expand by 25% in the upcoming year, while current market penetration hovers around 27%. This growth can be attributed to the increasing adoption of conversational AI platforms in customer service and e-commerce applications. Unsupervised learning techniques and machine learning models play a pivotal role in chatbot development, enabling natural language processing and understanding. Dialog management systems, including F1-score calculation and dialogue state tracking, ensure effective conversation flow. Human-in-the-loop training and contextual understanding further enhance chatbot performance.
Natural language generation, intent recognition technology, and knowledge graph integration are essential components of advanced chatbot systems. Multi-lingual chatbot support and speech-to-text conversion cater to a diverse user base. Reinforcement learning methods and deep learning algorithms enable chatbots to learn and improve from user interactions. Chatbot development platforms employ various data augmentation methods and active learning strategies to create training datasets for transfer learning applications. Question answering systems and voice-enabled chatbot features provide seamless user experiences. Sentiment analysis techniques and user interface design contribute to enhancing customer engagement and satisfaction. Conversational flow design and response generation models ensure e
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset shows the percentage of U.S. adults who say they trust ChatGPT more than a human expert, based on a 2025 national AI trust survey.
AI in Consumer Decision-Making: Global Zero-Party Dataset
This dataset captures how consumers around the world are using AI tools like ChatGPT, Perplexity, Gemini, Claude, and Copilot to guide their purchase decisions. It spans multiple product categories, demographics, and geographies, mapping the emerging role of AI as a decision-making companion across the consumer journey.
What Makes This Dataset Unique
Unlike datasets inferred from digital traces or modeled from third-party assumptions, this collection is built entirely on zero-party data: direct responses from consumers who voluntarily share their habits and preferences. That means the insights come straight from the people making the purchases, ensuring unmatched accuracy and relevance.
For FMCG leaders, retailers, and financial services strategists, this dataset provides the missing piece: visibility into how often consumers are letting AI shape their decisions, and where that influence is strongest.
Dataset Structure
Each record is enriched with: Product Category – from high-consideration items like electronics to daily staples such as groceries and snacks. AI Tool Used – identifying whether consumers turn to ChatGPT, Gemini, Perplexity, Claude, or Copilot. Influence Level – the percentage of consumers in a given context who rely on AI to guide their choices. Demographics – generational breakdowns from Gen Z through Boomers. Geographic Detail – city- and country-level coverage across Africa, LATAM, Asia, Europe, and North America.
This structure allows filtering and comparison across categories, age groups, and markets, giving users a multidimensional view of AI’s impact on purchasing.
Why It Matters
AI has become a trusted voice in consumers’ daily lives. From meal planning to product comparisons, many people now consult AI before making a purchase—often without realizing how much it shapes the options they consider. For brands, this means that the path to purchase increasingly runs through an AI filter.
This dataset provides a comprehensive view of that hidden step in the consumer journey, enabling decision-makers to quantify: How much AI shapes consumer thinking before they even reach the shelf or checkout. Which product categories are most influenced by AI consultation. How adoption varies by geography and generation. Which AI platforms are most commonly trusted by consumers.
Opportunities for Business Leaders
FMCG & Retail Brands: Understand where AI-driven decision-making is already reshaping category competition. Marketers: Identify demographic segments most likely to consult AI, enabling targeted strategies. Retailers: Align assortments and promotions with the purchase patterns influenced by AI queries. Investors & Innovators: Gauge market readiness for AI-integrated commerce solutions.
The dataset doesn’t just describe what’s happening—it opens doors to the “so what” questions that define strategy. Which categories are becoming algorithm-driven? Which markets are shifting fastest? Where is the opportunity to get ahead of competitors in an AI-shaped funnel?
Why Now
Consumer AI adoption is no longer a forecast; it is a daily behavior. Just as search engines once rewrote the rules of marketing, conversational AI is quietly rewriting how consumers decide what to buy. This dataset offers an early, detailed view into that change, giving brands the ability to act while competitors are still guessing.
What You Get
Users gain: A global, city-level view of AI adoption in consumer decision-making. Cross-category comparability to see where AI influence is strongest and weakest. Generational breakdowns that show how adoption differs between younger and older cohorts. AI platform analysis, highlighting how tool preferences vary by region and category. Every row is powered by zero-party input, ensuring the insights reflect actual consumer behavior—not modeled assumptions.
How It’s Used
Leverage this data to:
Validate strategies before entering new markets or categories. Benchmark competitors on AI readiness and influence. Identify growth opportunities in categories where AI-driven recommendations are rapidly shaping decisions. Anticipate risks where brand visibility could be disrupted by algorithmic mediation.
Core Insights
The full dataset reveals: Surprising adoption curves across categories where AI wasn’t expected to play a role. Geographic pockets where AI has already become a standard step in purchase decisions. Demographic contrasts showing who trusts AI most—and where skepticism still holds. Clear differences between AI platforms and the consumer profiles most drawn to each.
These patterns are not visible in traditional retail data, sales reports, or survey summaries. They are only captured here, directly from the consumers themselves.
Summary
Winning in FMCG and retail today means more than getting on shelves, capturing price points, or running promotions. It means understanding the invisible algorithms consumers are ...
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
Dataset Card for "Collective Cognition ChatGPT Conversations"
Dataset Description
Dataset Summary
The "Collective Cognition ChatGPT Conversations" dataset is a collection of chat logs between users and the ChatGPT model. These conversations have been shared by users on the "Collective Cognition" website. The dataset provides insights into user interactions with language models and can be utilized for multiple purposes, including training, research, and… See the full description on the dataset page: https://huggingface.co/datasets/CollectiveCognition/chats-data-2023-10-16.
The intersection of artificial intelligence (AI) and conversational data offers promising oppor- tunities for advancing research in specialized fields such as biology and health sciences. The WildChat dataset, comprising over one million user-ChatGPT interactions, serves as a valuable resource for analyzing how advanced language models engage with complex topics. This work aims to explore how conversational AI models interpret and manage bioinformatics-related queries, assessing their… See the full description on the dataset page: https://huggingface.co/datasets/david4096/wildbio.
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
ChatGPT-RealUser-2.2M: A Large-Scale Dataset of Real-User, Real-World ChatGPT Conversations
ChatGPT-RealUser-2.2M is a large-scale dataset of real-user, Real-World ChatGPT conversations developed by Gata. From 2024–2025, participants using Gata’s GPT-to-Earn product opted in to share their chats and earned points based on conversation quality. The dataset covers GPT-3.5, GPT-4, and o1 models, and contains 2,244,389 conversations from 15,316 unique users. Because many chats are… See the full description on the dataset page: https://huggingface.co/datasets/Gata-community/ChatGPT-RealUser-2.2M-preview.
https://choosealicense.com/licenses/cc0-1.0/https://choosealicense.com/licenses/cc0-1.0/
Dataset Card for ShareGPT52K90K
Dataset Summary
This dataset is a collection of approximately 52,00090,000 conversations scraped via the ShareGPT API before it was shut down. These conversations include both user prompts and responses from OpenAI's ChatGPT. This repository now contains the new 90K conversations version. The previous 52K may be found in the old/ directory.
Supported Tasks and Leaderboards
text-generation
Languages
This dataset is… See the full description on the dataset page: https://huggingface.co/datasets/RyokoAI/ShareGPT52K.
https://choosealicense.com/licenses/odc-by/https://choosealicense.com/licenses/odc-by/
Dataset Card for WildChat
Dataset Description
Paper: https://arxiv.org/abs/2405.01470
Interactive Search Tool: https://wildvisualizer.com (paper)
License: ODC-BY
Language(s) (NLP): multi-lingual
Point of Contact: Yuntian Deng
Dataset Summary
WildChat is a collection of 1 million conversations between human users and ChatGPT, alongside demographic data, including state, country, hashed IP addresses, and request headers. We collected WildChat by… See the full description on the dataset page: https://huggingface.co/datasets/allenai/WildChat-1M.
https://choosealicense.com/licenses/odc-by/https://choosealicense.com/licenses/odc-by/
Dataset Card for WildChat
Note: a newer version with 4.8 million conversations and demographic information can be found here.
Dataset Description
Paper: https://arxiv.org/abs/2405.01470
Interactive Search Tool: https://wildvisualizer.com (paper)
License: ODC-BY
Language(s) (NLP): multi-lingual
Point of Contact: Yuntian Deng
Dataset Summary
WildChat is a collection of 650K conversations between human users and ChatGPT. We collected WildChat… See the full description on the dataset page: https://huggingface.co/datasets/allenai/WildChat.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.