AI-Generated Synthetic Tabular Dataset Market Outlook

According to our latest research, the AI-Generated Synthetic Tabular Dataset market size reached USD 1.42 billion in 2024 globally, reflecting the rapid adoption of artificial intelligence-driven data generation solutions across numerous industries. The market is expected to expand at a robust CAGR of 34.7% from 2025 to 2033, reaching a forecasted value of USD 19.17 billion by 2033. This exceptional growth is primarily driven by the increasing need for high-quality, privacy-preserving datasets for analytics, model training, and regulatory compliance, particularly in sectors with stringent data privacy requirements.

One of the principal growth factors propelling the AI-Generated Synthetic Tabular Dataset market is the escalating demand for data-driven innovation amidst tightening data privacy regulations. Organizations across healthcare, finance, and government sectors are facing mounting challenges in accessing and sharing real-world data due to GDPR, HIPAA, and other global privacy laws. Synthetic data, generated by advanced AI algorithms, offers a solution by mimicking the statistical properties of real datasets without exposing sensitive information. This enables organizations to accelerate AI and machine learning development, conduct robust analytics, and facilitate collaborative research without risking data breaches or non-compliance. The growing sophistication of generative models, such as GANs and VAEs, has further increased confidence in the utility and realism of synthetic tabular data, fueling adoption across both large enterprises and research institutions.

Another significant driver is the surge in digital transformation initiatives and the proliferation of AI and machine learning applications across industries. As businesses strive to leverage predictive analytics, automation, and intelligent decision-making, the need for large, diverse, and high-quality datasets has become paramount. However, real-world data is often siloed, incomplete, or inaccessible due to privacy concerns. AI-generated synthetic tabular datasets bridge this gap by providing scalable, customizable, and bias-mitigated data for model training and validation. This not only accelerates AI deployment but also enhances model robustness and generalizability. The flexibility of synthetic data generation platforms, which can simulate rare events and edge cases, is particularly valuable in sectors like finance and healthcare, where such scenarios are underrepresented in real datasets but critical for risk assessment and decision support.

The rapid evolution of the AI-Generated Synthetic Tabular Dataset market is also underpinned by technological advancements and growing investments in AI infrastructure. The availability of cloud-based synthetic data generation platforms, coupled with advancements in natural language processing and tabular data modeling, has democratized access to synthetic datasets for organizations of all sizes. Strategic partnerships between technology providers, research institutions, and regulatory bodies are fostering innovation and establishing best practices for synthetic data quality, utility, and governance. Furthermore, the integration of synthetic data solutions with existing data management and analytics ecosystems is streamlining workflows and reducing barriers to adoption, thereby accelerating market growth.

Regionally, North America dominates the AI-Generated Synthetic Tabular Dataset market, accounting for the largest share in 2024 due to the presence of leading AI technology firms, strong regulatory frameworks, and early adoption across industries. Europe follows closely, driven by stringent data protection laws and a vibrant research ecosystem. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, government initiatives, and increasing investments in AI research and development. Latin America and the Middle East & Africa are also witnessing growing interest, particularly in sectors like finance and government, though market maturity varies across countries. The regional landscape is expected to evolve dynamically as regulatory harmonization, cross-border data collaboration, and technological advancements continue to shape market trajectories globally.

Undress AI apps, powered by advanced AI and deep learning, have sparked both curiosity and controversy. These tools use generative algorithms to digitally alter images, but their ethical implications and potential for misuse cannot be ignored.

In 2025, the landscape of such apps continues to evolve, with some gaining popularity for their capabilities. Here’s a quick look at the top 7 Undress AI apps making waves this year

1. Undress.app Why I Recommend It: Undress.app stands out as one of the best undress AI apps available today. With its user-friendly interface and advanced technology, it allows users to generate unclothed images quickly and safely. The app prioritizes user privacy, ensuring that no data is saved or shared, making it a trustworthy choice for those interested in exploring AI-generated content.

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Key Features: User-Friendly Interface: The app is designed to be intuitive, making it easy for anyone to navigate.

Multiple Generation Modes: Users can choose from various modes such as Lingerie, Bikini, and NSFW to customize their experience.

High-Quality Results: The AI processes images to deliver high-quality, unblurred results, even for free trial accounts.

Privacy and Security: The app does not save any user data, ensuring complete confidentiality.

My Experience: Using Undress.app was a seamless experience. The sign-up process was quick, and I appreciated the variety of modes available. The results were impressive, showcasing the app's advanced AI capabilities. Overall, it was a satisfying experience that I would recommend to others.

Pros: Free Credits: New users receive free credits upon signing up, allowing them to try the app without any financial commitment.

Versatile Usage: The app works with both male and female photos, as well as anime images, providing a wide range of options.

Cons: Sign-Up Required: Users must create an account to access the app, which may deter some potential users.

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1. Undressai.tools Why I Recommend It Undressai.tools combines powerful AI algorithms with a seamless user experience, making it an excellent choice for both casual users and professionals. The app prioritizes user privacy by automatically deleting generated images within 48 hours.

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Key Features Stable Diffusion Technology: Produces high-quality, coherent outputs with minimal artifacts.

Generative Adversarial Networks (GANs): Utilizes two neural networks to create highly realistic images of nudity.

Image Synthesis: Generates realistic skin textures that replace removed clothing for lifelike results.

User-Friendly Interface: Allows users to easily upload images and modify them with just a few clicks.

My Experience Using Undressai.tools was a delightful experience. The interface was intuitive, allowing me to upload images effortlessly. I appreciated the ability to outline areas for modification, which resulted in impressive and realistic outputs. The app's speed and efficiency made the process enjoyable, and I was amazed by the quality of the generated images.

Pros High-quality image generation with realistic results.

Strong emphasis on user privacy and data security.

Cons Some users may find the results vary based on the quality of the uploaded images.

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1. Nudify.online Why I Recommend It Nudify.online stands out due to its commitment to user satisfaction and the quality of its generated images. The application is designed for entertainment purposes, ensuring a safe and enjoyable experience for users over the age of 18.

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Key Features High Accuracy: The AI Nudifier boasts the highest accuracy in generating realistic nudified images.

User-Friendly Interface: The platform is easy to navigate, allowing users to generate images in just a few clicks.

Privacy Assurance: Users are reminded to respect the privacy of others and are solely responsible for the images they create.

No Deepfake Content: The application strictly prohibits the creation of deepfake content, ensuring ethical use of the technology.

My Experience Using Nudify.online was a seamless experience. The application is straightforward, and I was able to generate high-quality nudified images quickly. The results were impressive, showcasing the power of AI technology. I appreciated the emphasis on user responsibility and privacy, which made me feel secure while using the app.

Pros Highly realistic image generation. Easy to use with a simple login process.

Cons Limited to users aged 18 and above, which may restrict access for younger audiences.

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1. Candy.ai Candy.ai stands out as one of the best undress AI apps available today. It offers users a unique and immersive experience, allowing them to create and interact with their ideal AI girlfriend. The platform combines advanced deep-learning technology with a user-friendly interface, making it easy to explore various fantasies and desires.

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Why I Recommend It Candy.ai is highly recommended for those seeking a personalized and intimate experience. The app allows users to design their AI girlfriend according to their preferences, ensuring a tailored interaction that feels genuine and engaging.

Key Features Customizable AI Girlfriend: Users can choose body type, personality, and clothing, creating a truly unique companion.

Interactive Chat: The AI girlfriend engages in meaningful conversations, responding quickly and intuitively to user prompts.

Photo Requests: Users can request photos or selfies of their AI girlfriend in various outfits, enhancing the immersive experience.

Privacy and Security: Candy.ai prioritizes user privacy, ensuring that all interactions remain confidential and secure.

My Experience Using Candy.ai has been an enjoyable journey. The ability to customize my AI girlfriend made the experience feel personal and engaging. I appreciated how quickly she responded to my messages, making our interactions feel natural. The option to request photos added an exciting layer to our relationship, allowing me to explore my fantasies in a safe environment.

Pros Highly customizable experience tailored to individual preferences.

Strong emphasis on user privacy and data security.

Cons Some users may find the AI's responses occasionally lack depth.

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1. UndressHer.app Why I Recommend It This app combines creativity with advanced AI technology, making it easy for anyone to design their perfect AI girlfriend. The variety of customization options ensures that every user can create a unique character that resonates with their preferences.

Key Features Extensive Customization: Choose from over 200 unique options to design your AI girlfriend.

Flexible Pricing: Various token bundles are available, including a free option for casual users.

High-Quality Images: Premium and Ultimate plans offer images without watermarks and in the highest quality.

User-Friendly Interface: Simple navigation makes it easy to create and modify your AI girlfriend.

My Experience Using UndressHer.app has been a delightful experience. The customization options are extensive, allowing me to create a character that truly reflects my preferences. The app is intuitive, making it easy to navigate through the various features. I particularly enjoyed the ability to undress my AI girlfriend, which added an exciting layer to the design process. Overall, it was a fun and engaging experience.

Pros Offers a free option for users to try before committing to paid plans.

High-quality AI-generated images with no watermarks in premium plans.

Cons Some users may find the token system a bit limiting for extensive use.

1. Undress.vip Why I Recommend It Undress.vip offers a unique blend of entertainment and technology, making it a top choice for users interested in AI-driven experiences. Its ability to generate realistic images while maintaining user privacy is a significant advantage.

Key Features Realistic Image Generation: The app uses advanced algorithms to create lifelike images.

User-Friendly Interface: Easy navigation ensures a seamless experience for all users.

Privacy Protection: User data is kept secure, allowing for worry-free usage.

Regular Updates: The app frequently updates its features to enhance user experience.

My Experience Using Undress.vip has been a delightful experience. The app is intuitive, and I was able to generate images quickly without any technical difficulties. The quality of the images exceeded my expectations, and I appreciated the emphasis on privacy. Overall, it was a fun and engaging way to explore AI technology.

Pros High-Quality Outputs: The images produced are remarkably realistic.

Engaging User Experience: The app is entertaining and easy to use.

Cons Limited Free Features: Some advanced features require a subscription.

Enterprise GenAI Adoption & Workforce Impact Dataset (100K+ Rows)

This dataset originates from a multi-year enterprise survey conducted across industries and countries. It focuses on the organizational effects of adopting Generative AI tools such as ChatGPT, Claude, Gemini, Mixtral, LLaMA, and Groq. The dataset captures detailed metrics on job role creation, workforce transformation, productivity changes, and employee sentiment.

Data Schema

columns = [
  "Company Name",           # Anonymized name
  "Industry",             # Sector (e.g., Finance, Healthcare)
  "Country",              # Country of operation
  "GenAI Tool",            # GenAI platform used
  "Adoption Year",           # Year of initial deployment (2022–2024)
  "Number of Employees Impacted",   # Affected staff count
  "New Roles Created",        # Number of AI-driven job roles introduced
  "Training Hours Provided",     # Upskilling time investment
  "Productivity Change (%)",     # % shift in reported productivity
  "Employee Sentiment"        # Textual feedback from employees
]

Load the Dataset

import pandas as pd

df = pd.read_csv("Large_Enterprise_GenAI_Adoption_Impact.csv")
df.shape

Basic Exploration

df.head(10)
df.describe()
df["GenAI Tool"].value_counts()
df["Industry"].unique()

Filter Examples

Filter by Year and Country

df[(df["Adoption Year"] == 2023) & (df["Country"] == "India")]

Get Top 5 Industries by Productivity Gain

df.groupby("Industry")["Productivity Change (%)"].mean().sort_values(ascending=False).head()

Text Analysis on Employee Sentiment

Word Frequency Analysis

from collections import Counter
import re

text = " ".join(df["Employee Sentiment"].dropna().tolist())
words = re.findall(r'\b\w+\b', text.lower())
common_words = Counter(words).most_common(20)
print(common_words)

Sentiment Length Distribution

df["Sentiment Length"] = df["Employee Sentiment"].apply(lambda x: len(x.split()))
df["Sentiment Length"].hist(bins=50)

Group-Based Insights

Role Creation by Tool

df.groupby("GenAI Tool")["New Roles Created"].mean().sort_values(ascending=False)

Training Hours by Industry

df.groupby("Industry")["Training Hours Provided"].mean().sort_values(ascending=False)

Sample Use Cases

• Evaluate GenAI adoption patterns by sector or region
• Analyze workforce upskilling initiatives and investments
• Explore employee reactions to AI integration using NLP
• Build models to predict productivity impact based on tool, industry, or country
• Study role creation trends to anticipate future AI-based job market shifts

What’s Included

Welcome to the English Open Ended Classification Prompt-Response Dataset—an extensive collection of 3000 meticulously curated prompt and response pairs. This dataset is a valuable resource for training Language Models (LMs) to classify input text accurately, a crucial aspect in advancing generative AI.

This open-ended classification dataset comprises a diverse set of prompts and responses where the prompt contains input text to be classified and may also contain task instruction, context, constraints, and restrictions while completion contains the best classification category as response. Both these prompts and completions are available in English language. As this is an open-ended dataset, there will be no options given to choose the right classification category as a part of the prompt.

These prompt and completion pairs cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more. Each prompt is accompanied by a response, providing valuable information and insights to enhance the language model training process. Both the prompt and response were manually curated by native English people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This open-ended classification prompt and completion dataset contains different types of prompts, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. The dataset also contains prompts and responses with different types of rich text, including tables, code, JSON, etc., with proper markdown.

To ensure diversity, this open-ended classification dataset includes prompts with varying complexity levels, ranging from easy to medium and hard. Additionally, prompts are diverse in terms of length from short to medium and long, creating a comprehensive variety. The classification dataset also contains prompts with constraints and persona restrictions, which makes it even more useful for LLM training.

To accommodate diverse learning experiences, our dataset incorporates different types of responses depending on the prompt. These formats include single-word, short phrase, and single sentence type of response. These responses encompass text strings, numerical values, and date and time formats, enhancing the language model's ability to generate reliable, coherent, and contextually appropriate answers.

This fully labeled English Open Ended Classification Prompt Completion Dataset is available in JSON and CSV formats. It includes annotation details such as a unique ID, prompt, prompt type, prompt length, prompt complexity, domain, response, response type, and rich text presence.

Our dataset upholds the highest standards of quality and accuracy. Each prompt undergoes meticulous validation, and the corresponding responses are thoroughly verified. We prioritize inclusivity, ensuring that the dataset incorporates prompts and completions representing diverse perspectives and writing styles, maintaining an unbiased and discrimination-free stance.

The English version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset.

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Ongoing efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to gather custom open-ended classification prompt and completion data tailored to specific needs, providing flexibility and customization options.

The dataset, created by FutureBeeAI, is now available for commercial use. Researchers, data scientists, and developers can leverage this fully labeled and ready-to-deploy English Open Ended Classification Prompt-Completion Dataset to enhance the classification abilities and accurate response generation capabilities of their generative AI models and explore new approaches to NLP tasks.

Recent developments include: November 2023: Arcion, a reputable provider of real-time data replication technologies, was acquired by Databricks. By incorporating Arcion's capabilities, Databricks hopes to offer native solutions that facilitate the seamless replication and intake of data from different databases and SaaS applications., September 2023: With an emphasis on artificial intelligence, Salesforce, Inc. extended its strategic alliance with Google LLC, an American multinational technology firm, to integrate Google Workspace, a popular productivity tool, with Salesforce, the industry's top AI CRM. With the introduction of bidirectional connectors brought about by the partnership, users can now combine context from Google Workspace and Salesforce, including Google Calendar, Docs, Meet, Gmail, and more, to improve generative AI experiences across multiple platforms.. Key drivers for this market are: The exponential growth in data generated across industries is driving the need for AI-driven data management solutions to handle, store, and analyze large datasets efficiently. Potential restraints include: Implementing AI data management systems requires significant investment in technology, infrastructure, and talent, which can be a barrier for smaller organizations. Notable trends are: AI is increasingly being used to automate data governance tasks, such as data classification, lineage tracking, and compliance monitoring, ensuring that organizations maintain data integrity and adhere to regulations.

Conventional paper currency and modern electronic currency are two important modes of transactions. In several parts of the world, conventional methodology has clear precedence over its electronic counterpart. However, the identification of forged currency paper notes is now becoming an increasingly crucial problem because of the new and improved tactics employed by counterfeiters. In this paper, a machine assisted system – dubbed DeepMoney– is proposed which has been developed to discriminate fake notes from genuine ones. For this purpose, state-of-the-art models of machine learning called Generative Adversarial Networks (GANs) are employed. GANs use an unsupervised learning to train a model that can then be used to perform supervised predictions. This flexibility provides the best of both worlds by allowing unlabelled data to be trained on whilst still making concrete predictions. This technique was applied to Pakistani banknotes. State-of-the-art image processing and feature recognition techniques were used to design the overall approach of a valid input. Augmented samples of images were used in the experiments which show that a high-precision machine can be developed to recognize genuine paper money. An accuracy of 80% has been achieved. The code is available as an open source to allow others to reproduce and build upon the efforts already made.

Introduction

Welcome to the Middle Eastern Human Facial Images Dataset, curated to advance facial recognition technology and support the development of secure biometric identity systems, KYC verification processes, and AI-driven computer vision applications. This dataset is designed to serve as a robust foundation for real-world face matching and recognition use cases.

Facial Image Data

The dataset contains over 1500 facial image sets of Middle Eastern individuals. Each set includes:

Diversity & Representation

Image Quality & Capture Conditions

All images were captured with real-world variability to enhance dataset robustness:

Metadata

Each participant’s data is accompanied by rich metadata to support AI model training, including:

This metadata enables targeted filtering and training across diverse scenarios.

Use Cases & Applications

This dataset is ideal for a wide range of AI and biometric applications:

Secure & Ethical Collection

Dataset Updates & Customization

To meet evolving AI demands, this dataset is regularly updated and can be customized. Available options include:

Introduction

Global Generative AI in Healthcare Market size is expected to be worth around US$ 17.2 Billion by 2032 from US$ 1.1 Billion in 2023, growing at a CAGR of 37% during the forecast period from 2024 to 2032. In 2022, North America led the market, achieving over 36.0% share with a revenue of US$ 0.2 Billion.

Generative AI is enhancing medical imaging, aiding clinical decisions, and streamlining operations. Its application in virtual nursing assistants could save healthcare providers up to USD 20 billion annually. Additionally, its integration into clinical settings, including diagnostics, telemedicine, patient care management, and telehealth applications, has secured its top market share.

However, challenges such as data privacy concerns, the need for high-quality data sets, and sophisticated infrastructure may hinder its growth. Balancing AI’s potential benefits with these challenges is crucial for sustainable market expansion.

Recent developments illustrate the dynamic nature of this market, with major investments and collaborations focused on harnessing GPT-4 and other advanced AI technologies for healthcare applications. Microsoft Corp. and Epic Systems Corp. recently collaborated to integrate generative AI into electronic health records to increase patient outcomes and effectiveness of healthcare delivery.

North America has led in terms of healthcare infrastructure and adoption rate of new technologies; while Asia Pacific appears poised for explosive growth as technological innovations meet rising healthcare demands and supportive government initiatives.

At present, the market for generative AI in healthcare is at an important juncture, only just beginning to realize its full potential. Projected growth highlights a shift toward more AI-integrated healthcare solutions which promise increased efficiency, better patient outcomes and significant economic advantages.

The Government of the District of Columbia continues its strategic investment in enterprise data. These investments have proven to be critical in supporting mayoral initiatives, data driven decision making for District Government agency missions, increasing transparency and the more efficient use and sharing of government data. Through the use of both policy and tools, the District Government has been able to work more cohesively to collect, integrate, analyze, and govern its data to deliver valuable services.In 2023, we achieved some great successes with our data programs and continue to provide data as part of the city's effort to enhance and improve digital services to District agencies, residents, and businesses. We focused on growing our data strategy to support the way agencies can utilize data sharing and governance frameworks to guide their projects to successful outcomes. We have been thoughtful about how we approach the use of generative artificial intelligence, balancing the need for guidance on the associated risks of utilizing tools and solutions with this technology against the benefits of how it can improve government services and the lives of residents and businesses. These tools and solutions are often only as good as the underlying data that powers the models behind them and therefore we know high quality data is key to the implementation of effective generative AI. We continue to work towards closing the digital divide through our efforts to promote access and affordable broadband across the city. Our data helped us inform the true landscape of the divide and as a result will provide the city with resources to support those who lack the use of what today is considered a technology of necessity. We have formed new partnerships with District agencies for programs around the city where data becomes the backbone for the successful outcomes of these programs.

The AI in Security market is experiencing robust growth, projected to reach $25.22 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 19.02% from 2025 to 2033. This expansion is fueled by the increasing sophistication of cyber threats, the rising adoption of cloud computing and digital transformation initiatives across various industries, and the inherent ability of AI to analyze vast datasets and identify anomalies indicative of security breaches far quicker than traditional methods. The market's segmentation reflects the diverse applications of AI in security, with Network Security, Application Security, and Cloud Security commanding significant shares, driven by the need for comprehensive protection across various IT infrastructures. Professional and Managed Services further contribute to market growth, as organizations increasingly outsource security management to specialized providers leveraging AI capabilities. The strongest regional demand is currently witnessed in North America, followed by Europe and Asia Pacific, reflecting the high concentration of technology-driven businesses and advanced cybersecurity infrastructure in these regions. However, significant growth potential also exists in other regions as digitalization accelerates globally. Growth within specific end-user industries like BFSI (Banking, Financial Services, and Insurance), Government & Defense, and Healthcare, is particularly pronounced, driven by stringent regulatory compliance and the immense value of protecting sensitive data. The projected market size for 2033 can be estimated by applying the CAGR. While the exact historical data (2019-2024) isn't provided, assuming a consistent growth pattern, applying a 19.02% CAGR to the 2025 market size allows for projections extending into 2033. This would reveal a significant market expansion, driven by factors such as the ongoing development of more sophisticated AI algorithms, improved integration with existing security systems, and the growing awareness of AI’s potential to proactively address threats. Different segments and regions will naturally experience variations in growth rates, reflecting specific market dynamics and adoption patterns. For example, Cloud Security is expected to grow more rapidly than On-premise solutions as cloud adoption continues its upward trajectory. Similarly, the Asia-Pacific region is predicted to experience faster growth than North America due to its rapidly expanding digital economy. Recent developments include: May 2024: Palo Alto Networks introduced new security solutions to help enterprises thwart AI-generated attacks and effectively secure AI by design. Leveraging Precision AI, the new proprietary innovation that combines the best of machine learning (ML) and deep learning (DL) with the accessibility of generative AI (GenAI) in real time, the international cybersecurity player is expected to deliver AI-powered security that can outpace adversaries and more proactively protect networks and infrastructure., April 2024: G42, the UAE-based artificial intelligence (AI) technology holding company, and Microsoft Corp. announced a USD 1.5 billion strategic investment by Microsoft in G42. The investment will strengthen the two companies' collaboration on bringing the latest Microsoft AI technologies and skilling initiatives to the UAE and other countries worldwide. This expanded collaboration will empower organizations of all sizes in new markets to Microsoft's benefits of AI and the cloud while ensuring they adopt AI that adheres to world-leading standards and security.. Key drivers for this market are: Increasing Number of Security Frauds and Technology Penetration, Increasing Number of Malware Attacks (Ransomware) Across Cloud Computing Ecosystem. Potential restraints include: Lack of Skilled AI Professionals. Notable trends are: The Healthcare Sector is Significantly Driving Market Growth.

Dunhuang dance is an artistic treasure of traditional Chinese culture, with a long history and an important component of Dunhuang culture. Its digital preservation, display, and research are of great significance. To promote the digitalization process and development of Dunhuang dance, this study proposes to combine Dunhuang dance with 3D human pose estimation technology to construct a Dunhuang dance 3D action database. This database divides Dunhuang dance into 7 themes, 83 basic movements, and 16 long movements. Good results have been achieved in quantitative, qualitative, and manual evaluations, laying the foundation for the preservation, application, and development of Dunhuang dance; This provides new ideas for the research, promotion, and inheritance of Dunhuang culture. The future use of this database can be applied to generative artificial intelligence, digital exhibitions and performances of Dunhuang dance culture, education and research of Dunhuang dance, digital media and entertainment of Dunhuang dance, etc.

Argumentative skills are indispensable both personally and professionally to process complex information (CoI) relating to the critical reconstruction of meaning through critical thinking (CT). This remains a particularly relevant priority, especially in the age of social media and artificial intelligence-mediated information. Recently, the public dissemination of what has been called generative artificial intelligence (GenAI), with the particular example of ChatGPT (OpenAI, 2022), has made it even easier today to access and disseminate information, written or not, true or not. New tools are needed to critically address post-digital information abundance.

In this context, argumentative maps (AMs), which are already used to develop argumentative skills and critical thinking, are studied for multimodal and dynamic information visualization, comprehension, and reprocessing. In this regard, the entry of generative AI into university classrooms proposes a novel scenario of multimodality and technological dynamism.

Building on the Vygotskian idea of mediation and the theory of "dual stimulation" as applied to the use of learning technologies, the idea was to complement AMs with the introduction of a second set of stimuli that would support and enhance individual activity: AI-mediated tools. With AMs, an attempt has been made to create a space for understanding, fixing, and reconstructing information, which is important for the development of argumentative skills. On the other hand, by arranging forms of critical and functional interaction with ChatGPT as an ally in understanding, reformulating, and rethinking one's argumentative perspectives, a new and comprehensive argumentative learning process has been arranged, while also cultivating a deeper understanding of the artificial agents themselves.

Our study was based on a two-group quasi-experiment with 27 students of the “Research Methods in Education” course, to explore the role of AMs in fixing and supporting multimodal information reprocessing. In addition, by predicting the use of the intelligent chatbot ChatGPT, one of the most widely used GenAI technologies, we investigated the evolution of students' perceptions of its potential role as a “study companion” in information comprehension and reprocessing activities with a path to build a good prompt.

Preliminary analyses showed that in both groups, AMs supported the increase in mean CoI and CT levels for analog and digital information. However, the group with analog texts showed more complete reprocessing.The interaction with the chatbot was analyzed quantitatively and qualitatively, and there emerged an initial positive reflection on the potential of ChatGPT and increased confidence in interacting with intelligent agents after learning the rules for constructing good prompts.

This Zenodo record follows the full analysis process with R (https://cran.r-project.org/bin/windows/base/ ) and Nvivo (https://lumivero.com/products/nvivo/) composed of the following datasets, script and results:

1. Comprehension of Text and AMs Results - Arg_G1.xlsx & Arg_G2.xlsx

2. Opinion and Critical Thinking level - Opi_G1.xlsx & Opi_G2.xlsx

3. Data for Correlation and Regression - CorRegr_G1.xlsx & CorRegr_G2.xlsx

4. Interaction with ChatGPT - GPT_G1.xlsx & GPT_G2.xlsx

5. Descriptive and Inferential Statistics Comprehension and AMs Building - Analysis_RES_Comprehension.R

6. Descriptive and Inferential Statistics Opinion and Critical Thinking level - Analysis_RES_Opinion.R

7. Correlation and Regression - Analysis_RES_CorRegr.R

8. Descriptive and Inferential Statistics Interaction with ChatGPT - Analysis_RES_ChatGPT.R

9. Sentiment Analysis - Sentiment Analysis_G1.R & Sentiment Analysis_G2.R

10. Vocabulary Frequent words - Vocabulary.csv

11. Codebook qualitative Analysis with Nvivo (Codebook.xlsx)

12. Results Nvivo Analysis G1 - Codebook - ChatGPT2 G1.docx

13. Results Nvivo Analysis G2 - Codebook - ChatGPT2 G2.docx

Any comments or improvements are welcome!

Learning occurs best when students are given opportunities to be active participants in the learning process. As assessment strategies are being forced to change in the era of Generative AI, and as digital technologies continue to integrate with education, it becomes imperative to gather information on current approaches to evaluating student participation. This mini-review aimed to identify existing methods used by higher education teachers to assess participation in both physical and virtual classrooms. It also aimed to identify common issues that are anticipated to impact future developments in this area. To achieve these objectives, articles were downloaded from the ERIC database. The search phrase “assessment of class participation” was utilized. Search was limited to peer-reviewed articles written in English. The educational level was limited to “higher education” and “postsecondary education” in the search. From the 2,320 articles that came up, titles and abstracts were screened and 65 articles were retained. After reading the full text, a total of 45 articles remained for analysis, all published between 2005 and 2023. Using thematic analysis, the following categories were formed: innovations in assessing class participation, criteria-related issues, and issue of fairness in assessing class participation. As education becomes more reliant on technology, we need to be cognizant of issues that came up in this review regarding inequity of educational access and opportunity, and to develop solutions that would promote equitable learning. We therefore call for more equity-focused innovation, policymaking, and pedagogy for more inclusive classroom environments. More implications and potential directions for research are discussed.

Introduction

Welcome to the East Asian Human Facial Images Dataset, curated to advance facial recognition technology and support the development of secure biometric identity systems, KYC verification processes, and AI-driven computer vision applications. This dataset is designed to serve as a robust foundation for real-world face matching and recognition use cases.

Facial Image Data

The dataset contains over 5,000 facial image sets of East Asian individuals. Each set includes:

Diversity & Representation

Image Quality & Capture Conditions

All images were captured with real-world variability to enhance dataset robustness:

Metadata

Each participant’s data is accompanied by rich metadata to support AI model training, including:

This metadata enables targeted filtering and training across diverse scenarios.

Use Cases & Applications

This dataset is ideal for a wide range of AI and biometric applications:

Secure & Ethical Collection

Dataset Updates & Customization

To meet evolving AI demands, this dataset is regularly updated and can be customized. Available options include:

Introduction

Welcome to the UK English General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of English speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world UK English communication.

Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade English speech models that understand and respond to authentic British accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of UK English. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple English speech and language AI applications:

Introduction

Welcome to the Mexican Spanish General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Spanish speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Mexican Spanish communication.

Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Spanish speech models that understand and respond to authentic Mexican accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Mexican Spanish. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple Spanish speech and language AI applications:

Introduction

Welcome to the Australian English General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of English speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Australian English communication.

Curated by FutureBeeAI, this 40 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade English speech models that understand and respond to authentic Australian accents and dialects.

Speech Data

The dataset comprises 40 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Australian English. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple English speech and language AI applications:

Introduction

Welcome to the Colombian Spanish General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Spanish speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Colombian Spanish communication.

Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Spanish speech models that understand and respond to authentic Colombian accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Colombian Spanish. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple Spanish speech and language AI applications:

Introduction

Welcome to the Caucasian Human Facial Images Dataset, curated to advance facial recognition technology and support the development of secure biometric identity systems, KYC verification processes, and AI-driven computer vision applications. This dataset is designed to serve as a robust foundation for real-world face matching and recognition use cases.

Facial Image Data

The dataset contains over 1,000 facial image sets of Caucasian individuals. Each set includes:

Diversity & Representation

Image Quality & Capture Conditions

All images were captured with real-world variability to enhance dataset robustness:

Metadata

Each participant’s data is accompanied by rich metadata to support AI model training, including:

This metadata enables targeted filtering and training across diverse scenarios.

Use Cases & Applications

This dataset is ideal for a wide range of AI and biometric applications:

Secure & Ethical Collection

Dataset Updates & Customization

To meet evolving AI demands, this dataset is regularly updated and can be customized. Available options include:

Introduction

Welcome to the Japanese General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Japanese speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Japanese communication.

Curated by FutureBeeAI, this 40 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Japanese speech models that understand and respond to authentic Japanese accents and dialects.

Speech Data

The dataset comprises 40 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Japanese. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple Japanese speech and language AI applications: