The researcher tests the QA capability of ChatGPT in the medical field from the following aspects:1. Test their reserve capacity for medical knowledge2. Check their ability to read literature and understand medical literature3. Test their ability of auxiliary diagnosis after reading case data4. Test its error correction ability for case data5. Test its ability to standardize medical terms6. Test their evaluation ability to experts7. Check their ability to evaluate medical institutionsThe conclusion is:ChatGPT has great potential in the application of medical and health care, and may directly replace human beings or even professionals at a certain level in some fields;The researcher preliminarily believe that ChatGPT has basic medical knowledge and the ability of multiple rounds of dialogue, and its ability to understand Chinese is not weak;ChatGPT has the ability to read, understand and correct cases;ChatGPT has the ability of information extraction and terminology standardization, and is quite excellent;ChatGPT has the reasoning ability of medical knowledge;ChatGPT has the ability of continuous learning. After continuous training, its level has improved significantly;ChatGPT does not have the academic evaluation ability of Chinese medical talents, and the results are not ideal;ChatGPT does not have the academic evaluation ability of Chinese medical institutions, and the results are not ideal;ChatGPT is an epoch-making product, which can become a useful assistant for medical diagnosis and treatment, knowledge service, literature reading, review and paper writing.

The dataset was compiled to examine the use of ChatGPT 3.5 in educational settings, particularly for creating and personalizing concept maps.
The data has been organized into three folders: Maps, Texts, and Questionnaires. The Maps folder contains the graphical representation of the concept maps and the PlanUML code for drawing them in Italian and English. The Texts folder contains the source text used as input for the map's creation The Questionnaires folder includes the students' responses to the three administered questionnaires.

along with the corresponding answers from students and ChatGPT.

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.

Large Language Model content safety considerations text data, about 570,000 in total, this dataset can be used for tasks such as LLM training, chatgpt

This dataset contains the 30 questions that were posed to the chatbots (i) ChatGPT-3.5; (ii) ChatGPT-4; and (iii) Google Bard, in May 2023 for the study “Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard”. These 30 questions describe mathematics and logic problems that have a unique correct answer. The questions are fully described with plain text only, without the need for any images or special formatting. The questions are divided into two sets of 15 questions each (Set A and Set B). The questions of Set A are 15 “Original” problems that cannot be found online, at least in their exact wording, while Set B contains 15 “Published” problems that one can find online by searching on the internet, usually with their solution. Each question is posed three times to each chatbot. This dataset contains the following: (i) The full set of the 30 questions, A01-A15 and B01-B15; (ii) the correct answer for each one of them; (iii) an explanation of the solution, for the problems where such an explanation is needed, (iv) the 30 (questions) × 3 (chatbots) × 3 (answers) = 270 detailed answers of the chatbots. For the published problems of Set B, we also provide a reference to the source where each problem was taken from.

In 2023, more than half of Polish respondents had no opinion on whether ChatGPT would store wrong information in the algorithm's database.

🧠 Awesome ChatGPT Prompts [CSV dataset]

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

  License

CC-0

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.

Generative Artificial Intelligence (AI) models such as OpenAI’s ChatGPT have the potential to revolutionize Statistical Process Control (SPC) practice, learning, and research. However, these tools are in the early stages of development and can be easily misused or misunderstood. In this paper, we give an overview of the development of Generative AI. Specifically, we explore ChatGPT’s ability to provide code, explain basic concepts, and create knowledge related to SPC practice, learning, and research. By investigating responses to structured prompts, we highlight the benefits and limitations of the results. Our study indicates that the current version of ChatGPT performs well for structured tasks, such as translating code from one language to another and explaining well-known concepts but struggles with more nuanced tasks, such as explaining less widely known terms and creating code from scratch. We find that using new AI tools may help practitioners, educators, and researchers to be more efficient and productive. However, in their current stages of development, some results are misleading and wrong. Overall, the use of generative AI models in SPC must be properly validated and used in conjunction with other methods to ensure accurate results.

The data set records the perceptions of Bangladeshi university students on the influence that AI tools, especially ChatGPT, have on their academic practices, learning experiences, and problem-solving abilities. The varying role of AI in education, which covers common usage statistics, what AI does to our creative abilities, its impact on our learning, and whether it could invade our privacy. This dataset reveals perspective on how AI tools are changing education in the country and offering valuable information for researchers, educators, policymakers, to understand trends, challenges, and opportunities in the adoption of AI in the academic contex.

Methodology Data Collection Method: Online survey using google from Participants: A total of 3,512 students from various Bangladeshi universities participated. Survey Questions:The survey included questions on demographic information, frequency of AI tool usage, perceived benefits, concerns regarding privacy, and impacts on creativity and learning.

Sampling Technique: Random sampling of university students Data Collection Period: June 2024 to December 2024

Privacy Compliance This dataset has been anonymized to remove any personally identifiable information (PII). It adheres to relevant privacy regulations to ensure the confidentiality of participants.

For further inquiries, please contact: Name: Md Jhirul Islam, Daffodil International University Email: jhirul15-4063@diu.edu.bd Phone: 01316317573

This repository contains two datasets used in the study exploring the impact of Generative AI, specifically ChatGPT, on the public sector workforce in the United States. The datasets provide detailed information on the core tasks of public sector occupations and their estimated performance metrics, including potential for automation and augmentation by ChatGPT. These estimations are generated by OpenAI’s GPT-4 model (GPT-4-1106-preview) through OpenAI API.

DeepSeek vs. ChatGPT: AI Performance & User Behavior (July 2023 - Feb 2025)

This synthetically generated dataset provides a realistic AI performance comparison between ChatGPT (GPT-4-turbo) and DeepSeek (DeepSeek-Chat 1.5) over a 1.5-year period. With 10,000+ rows, it captures key user interaction metrics, platform performance indicators, and AI response characteristics to analyze trends in accuracy, engagement, and adoption.

Key Features:

• Time-Series Ready – Granular date and time columns for trend and seasonality analysis.
• Comparative AI Analysis – Compare user engagement, retention rates, and response quality.
• User Behavior Insights – Analyze session durations, input text complexity, and user ratings.
• Technical Performance Metrics – Evaluate AI response accuracy and processing speed.
• Data Cleaning Practice – Includes intentionally introduced null values for preprocessing exercises.

Ideal For:

• AI benchmarking and platform performance studies
• Time-series forecasting and trend analysis
• Data preprocessing and feature engineering
• Power BI, SQL, and Python-based analytical dashboards

📜 License: MIT – Free for research, projects, and analysis.

This dataset contains a collection of tweets with the hashtag #chatgpt. The tweets were scraped from Twitter and cover a range of topics related to the ChatGPT language model. The dataset includes the following information for each tweet:

Tweet text User information (username, user ID, location, etc.) Tweet timestamp Retweet and favorite count Hashtags used in the tweet URLs The dataset provides a glimpse into the online conversation surrounding the ChatGPT language model and can be used for various natural language processing and machine learning tasks, such as sentiment analysis, topic modeling, and more. It allows understanding the community, the level of interest, and the use of chatGPT.

License

CC0

Original Data Source: ChatGPT Twitter Dataset

This project investigated teacher attitudes towards Generative Artificial Intelligence Tools (GAITs). In excess of three hundred teachers were surveyed across a broad variety of teaching levels, demographic areas, experience levels, and disciplinary areas, to better understand how they believe teaching and assessment should change as a result of GAITs such as ChatGPT.

Teachers were invited to complete an online survey relating to their perceptions of the open Artificial Intelligence (AI) tool ChatGPT, and how it will influence what they teach and how they assess. The purpose of the study is to provide teachers, policymakers, and society at large with an understanding of the potential impact of tools such as ChatGPT on Education.

This dataset contains public data files used for the ChatGPT survey (XLSX) and the survey containing variable selection codes (DOCX). See the second sheet of the XLSX file for variable descriptions.

Dataset Summary

The dataset contains a total of 9984 incident records and 9 columns. Some of the columns contain ground truth values whereas others contain information generated by ChatGPT based on the incident Narratives. The creation of this dataset is aimed at providing researchers with columns generated by using ChatGPT API which is not freely available.

  Dataset Structure

The column names present in the dataset and their descriptions are provided below:

Column… See the full description on the dataset page: https://huggingface.co/datasets/archanatikayatray/ASRS-ChatGPT.

This is a dataset of paraphrases created by ChatGPT. Model based on this dataset is avaible: model

  We used this prompt to generate paraphrases

Generate 5 similar paraphrases for this question, show it like a numbered list without commentaries: {text} This dataset is based on the Quora paraphrase question, texts from the SQUAD 2.0 and the CNN news dataset. We generated 5 paraphrases for each sample, totally this dataset has about 420k data rows. You can make 30 rows from a row from… See the full description on the dataset page: https://huggingface.co/datasets/humarin/chatgpt-paraphrases.

The ChatGPT App Reviews dataset is a comprehensive collection of user reviews from the ChatGPT mobile app on iOS, capturing valuable insights and sentiments. The dataset enables the understanding of user satisfaction, evaluation of app performance, and identification of emerging patterns.

The way data was collected Scraping ChatGPT reviews on App Store

Ideas for using this dataset Sentiment analysis What makes the application receive 1-star and 5-star

License

Attribution-NoDerivatives 4.0

Original Data Source: ChatGPT App Reviews

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.

OpenAssistant Conversations Dataset (OASST1)

  Dataset Summary

In an effort to democratize research on large-scale alignment, we release OpenAssistant Conversations (OASST1), a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus is a product of a worldwide crowd-sourcing effort… See the full description on the dataset page: https://huggingface.co/datasets/RajChat/Chatgpt.