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.

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.

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.

Higher Education Students' Early Perceptions of ChatGPT: Global Survey Data

Description

The introduction of ChatGPT in November 2022 marked a significant milestone in the application of artificial intelligence in higher education. Due to its advanced natural language processing capabilities, ChatGPT quickly became popular among students worldwide. However, the increasing acceptance of ChatGPT among students has attracted significant attention, sparking both excitement and skepticism globally. In order to capture early students' perceptions about ChatGPT, the most comprehensive and large-scale global survey to date was conducted between the beginning of October 2023 and the end of February 2024. The questionnaire was prepared in seven different languages: English, Italian, Spanish, Turkish, Japanese, Arabic, and Hebrew. It covered several aspects relevant to ChatGPT, including sociodemographic characteristics, usage, capabilities, regulation and ethical concerns, satisfaction and attitude, study issues and outcomes, skills development, labor market and skills mismatch, emotions, study and personal information, and general reflections. The survey targeted higher education students who are currently enrolled at any level in a higher education institution, are at least 18 years old, and have the legal capacity to provide free and voluntary consent to participate in an anonymous survey. Survey participants were recruited using a convenience sampling method, which involved promoting the survey in classrooms and through advertisements on university communication systems. The final dataset consists of 23,218 student responses from 109 different countries and territories. The data may prove useful for researchers studying students' perceptions of ChatGPT, including its implications across various aspects. Moreover, also higher education stakeholders may benefit from these data. While educators may benefit from the data in formulating curricula, including designing teaching methods and assessment tools, policymakers may consider the data when formulating strategies for higher education system development in the future.

Categories

Arts and Humanities, Applied Sciences, Natural Sciences, Social Sciences, Mathematics, Health Sciences

Related Links:

Article

https://www.covidsoclab.org/chatgpt-student-survey/ is related to this dataset

Software

https://www.1ka.si/d/en is related to this dataset

Acknowledgements & Source

Dejan Ravšelj , et. al

Data Source: Mendeley Dataset

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 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.

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.

🧠 Awesome ChatGPT Prompts [CSV dataset]

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

  License

CC-0

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.

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 ...

RavenStack is a fictional AI-powered collaboration platform used to simulate a real-world SaaS business. This simulated dataset was created using Python and ChatGPT specifically for people learning data analysis, business intelligence, or data science. It offers a realistic environment to practice SQL joins, cohort analysis, churn modeling, revenue tracking, and support analytics using a multi-table relational structure.

The dataset spans 5 CSV files:

• accounts.csv – customer metadata

• subscriptions.csv – subscription lifecycles and revenue

• feature_usage.csv – daily product interaction logs

• support_tickets.csv – support activity and satisfaction scores

• churn_events.csv – churn dates, reasons, and refund behaviors

Users can explore trial-to-paid conversion, MRR trends, upgrade funnels, feature adoption, support patterns, churn drivers, and reactivation cycles. The dataset supports temporal and cohort analyses, and has built-in edge cases for testing real-world logic.

A survey conducted in Pakistan to determine the perception of health care professionals on the use of ChatGPT in clinical decision making. The survey was conducted online, in between March to April 2023 through online Google forms. Any healthcare professional practicing in Pakistan including doctors, paramedic staff, allied health care; physiotherapist, occupational & speech therapist, nurses with any age group, must be familiar with ChatGPT and had used it in their daily practices of clinical decision making were the targeted population of this survey. The undergraduate students who are practicing clinical for their learning are excluded due to amateur skills.

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.

A curated database of legal cases where generative AI produced hallucinated citations submitted in court filings.

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.

With the rise of ChatGPT and DeepSeek, people have been debating about the "trustworthiness" of these systems. But why and when should AI systems be "trustworthy"? Listen to this bite-sized explanation by Prof. Dr. Judith Simon and Jaana Müller-Brehm.

This is an excerpt from our series Conversations on AI Ethics (https://youtube.com/playlist?list=PLiv4TocTZt7NIu58pguXJK4hjeesIkS-N&feature=shared). Check it out for a more detailed discussion of the topic!

Paper | Github | Dataset| Model 📣📣📣: Do check our new multilingual dataset CatQA here used in Safety Vectors:📣📣📣

As a part of our research efforts toward making LLMs more safe for public use, we create HarmfulQA i.e. a ChatGPT-distilled dataset constructed using the Chain of Utterances (CoU) prompt. More details are in our paper Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment HarmfulQA serves as both-a new LLM safety benchmark and an alignment dataset… See the full description on the dataset page: https://huggingface.co/datasets/declare-lab/HarmfulQA.

This dataset has the following files. Feel free to do exploratory analysis on the same.

1) Purchase Details of Donors in Excel SBI Provided this data in PDF - I've converted it to xlsx format. 18871 records

2) Redemption details of Parties in Excel SBI Provided this data in PDF - I've converted it to xlsx format. 20421 records

3) Bond Purchased by Donor - But no redemption details Bonds were purchased by donors, but no one redeemed it. SBI has tagged them as expired - 130 records.

4) Redeemed by a recipient party - But no Donor Details This is a fishy section. SBI has said recipient parties have redeemed the money but has failed to mention the donor - 1680 records.

5) Donors Matched with the recipient parties This is the master sheet of donors matched to the recipients. SBI said it would take 130 days to tabulate this. Think they are still in stone age. Feel free to go crazy analyzing this sheet. Correlate the contributions with news surrounding the days -Use ChatGPT!

6) Parties and their total redemptions This is a confirmed list of parties and their total redemptions, derived from file 5 above.

7) Parties, their donors and contributions This is a confirmed list of parties - the respective donors to the parties and the donor's total donation to the parties, derived from file 5 above.

Dataset Card for UltraChat 200k

  Dataset Description

This is a heavily filtered version of the UltraChat dataset and was used to train Zephyr-7B-β, a state of the art 7b chat model. The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create UltraChat 200k, we applied the following logic:

Selection of a subset of data for faster supervised fine tuning. Truecasing of the dataset, as we observed around 5% of the data… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k.