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.

IntroductionProviding one-on-one support to large cohorts is challenging, yet emerging AI technologies show promise in bridging the gap between the support students want and what educators can provide. They offer students a way to engage with their course material in a way that feels fluent and instinctive. Whilst educators may have views on the appropriates for AI, the tools themselves, as well as the novel ways in which they can be used, are continually changing.MethodsThe aim of this study was to probe students' familiarity with AI tools, their views on its current uses, their understanding of universities' AI policies, and finally their impressions of its importance, both to their degree and their future careers. We surveyed 453 psychology and sport science students across two institutions in the UK, predominantly those in the first and second year of undergraduate study, and conducted a series of five focus groups to explore the emerging themes of the survey in more detail.ResultsOur results showed a wide range of responses in terms of students' familiarity with the tools and what they believe AI tools could and should not be used for. Most students emphasized the importance of understanding how AI tools function and their potential applications in both their academic studies and future careers. The results indicated a strong desire among students to learn more about AI technologies. Furthermore, there was a significant interest in receiving dedicated support for integrating these tools into their coursework, driven by the belief that such skills will be sought after by future employers. However, most students were not familiar with their university's published AI policies.DiscussionThis research on pedagogical methods supports a broader long-term ambition to better understand and improve our teaching, learning, and student engagement through the adoption of AI and the effective use of technology and suggests a need for a more comprehensive approach to communicating these important guidelines on an on-going basis, especially as the tools and guidelines evolve.

Creativity is core to being human. Generative AI—made readily available by powerful large language models (LLMs)—holds promise for humans to be more creative by offering new ideas, or less creative by anchoring on generative AI ideas. We study the causal impact of generative AI ideas on the production of short stories in an online experiment where some writers obtained story ideas from an LLM. We find that access to generative AI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers. However, generative AI-enabled stories are more similar to each other than stories by humans alone. These results point to an increase in individual creativity at the risk of losing collective novelty. This dynamic resembles a social dilemma: with generative AI, writers are individually better off, but collectively a narrower scope of novel content is produced. Our results have implications for researchers, policy-makers, and practitioners interested in bolstering creativity. Methods This dataset is based on a pre-registered, two-phase experimental online study. In the first phase of our study, we recruited a group of N=293 participants (“writers”) who are asked to write a short, eight sentence story. Participants are randomly assigned to one of three conditions: Human only, Human with 1 GenAI idea, and Human with 5 GenAI ideas. In our Human only baseline condition, writers are assigned the task with no mention of or access to GenAI. In the two GenAI conditions, we provide writers with the option to call upon a GenAI technology (OpenAI’s GPT-4 model) to provide a three-sentence starting idea to inspire their own story writing. In one of the two GenAI conditions (Human with 5 GenAI ideas), writers can choose to receive up to five GenAI ideas, each providing a possibly different inspiration for their story. After completing their story, writers are asked to self-evaluate their story on novelty, usefulness, and several emotional characteristics. In the second phase, the stories composed by the writers are then evaluated by a separate group of N=600 participants (“evaluators”). Evaluators read six randomly selected stories without being informed about writers being randomly assigned to access GenAI in some conditions (or not). All stories are evaluated by multiple evaluators on novelty, usefulness, and several emotional characteristics. After disclosing to evaluators whether GenAI was used during the creative process, we ask evaluators to rate the extent to which ownership and hypothetical profits should be split between the writer and the AI. Finally, we elicit evaluators’ general views on the extent to which they believe that the use of AI in producing creative output is ethical, how story ownership and hypothetical profits should be shared between AI creators and human creators, and how AI should be credited in the involvement of the creative output. The data was collected on the online study platform Prolific. The data was then cleaned, processed and analyzed with Stata. For the Writer Study, of the 500 participants who began the study, 169 exited the study prior to giving consent, 22 were dropped for not giving consent, and 13 dropped out prior to completing the study. Three participants in the Human only condition admitted to using GenAI during their story writing exercise and—as per our pre-registration—they were therefore dropped from the analysis, resulting in a total number of writers and stories of 293. For the Evaluator Study, each evaluator was shown 6 stories (2 stories from each topic). The evaluations associated with the writers who did not complete the writer study and those in the Human only condition who acknowledged using AI to complete the story were dropped. Thus, there are a total of 3,519 evaluations of 293 stories made by 600 evaluators. Four evaluations remained for five evaluators, five evaluations remained for 71, and all six remained for 524 evaluators.

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 in generative large language models (LLMs) have raised questions about how this technology will affect higher education. We present results from two original surveys, collected in collaboration with the American Political Science Association, examining how instructors perceive the impact of generative LLMs. We find that educators are slightly pessimistic about generative LLMs, but support for AI tools varies based on application. Despite a professed importance for students to learn how to use AI tools, results show that educators' responses would primarily come through the prevention and detection of AI use rather than integrating AI into the curriculum. However, there are notable issues with detection and AI-ban enforcement. Respondents correctly determined whether students or AI wrote an essay in our essay bank no better than a coin flip; detection software are inaccurate. Based on these findings and suggestions from surveyed colleagues, we conclude with recommendations for dealing with generative AI in the classroom.

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.

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.

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.

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

The Data Catalog Marketsize was valued at USD 878.8 USD million in 2023 and is projected to reach USD 2749.95 USD million by 2032, exhibiting a CAGR of 17.7 % during the forecast period. Data catalog is another concept that is used to refer to a unified list of all the data resources within an organization and their descriptions that are crucial in the course of data search. It can also sort data, effectively making it easier to find and use data sets that the user requires. based on their usage, data catalogs can be distinguished into business, technical, and operation catalogs; business use for business intelligence, technical for providing metadata for technical use, and operational use for tracking operational data. Some of the significant elements of data catalogs are data lineage, metadata management, search and discovery features, data governance, and collaboration. They are actively utilized in industries for increasing data quality, satisfying the requirements of compliance, and optimizing the analysis to support better decision-making and increase efficiency in business operations. Recent developments include: February 2024 – Collibra launched Collibra AI Governance, built on their Data Intelligence Platform, enabling organizations to deliver trusted AI effectively through the use of Collibra Data Catalog. It aided teams in collaborating for compliance, improved model performance, reduced risk, and led to faster production timelines., September 2023 – AWS Lake Formation launched a Hybrid Access Module for the AWS Glue Data Catalog, allowing users to selectively enable Lake Formation for tables and databases without interrupting existing users or workloads. This feature provided flexibility and an integral path for enabling Lake Formation, reducing the need for coordination among owners and consumers., July 2023 – Teradata acquired Stemma Technologies to enhance its analytics capabilities, particularly in data discovery and delivery. Stemma’s automated data catalog bolstered Teradata’s offerings, aiming to improve user experience and accelerate ML and AI analytics growth., June 2023 – Acryl Data secured USD 21 million in Series A funding led by 8VC to enhance its open-source data catalog platform. This investment enhanced their cloud offerings and expanded their vision towards a data control plane., May 2023 – data.world launched its new Data Catalog Platform, integrating generative AI bots to enhance data discovery. With over 2 million users, the platform aimed to make data discovery and knowledge unlocking accessible to users of all expertise levels., February 2023 – data.world, a data governance platform, launched the first AI Lab for the data catalog industry. This Artificial Intelligence (AI) Lab would be important in bringing partners and customers together to enhance data team productivity using AI technology., November 2022 – Amazon Web Services (AWS) launched DataZone, a new machine learning-based data management service to help enterprises catalog, share, govern, and discover their data quickly.. Key drivers for this market are: Exponential Growth of Data Volume and Data Analytics to Fuel Market Growth. Potential restraints include: High Initial Deployment Cost and Privacy Concerns to Hinder Market Growth. Notable trends are: Growing Adoption of AI and Automation Technologies to Amplify Market Growth.

Dunhuang dance is an artistic treasure of Chinese traditional culture, which originates from Dunhuang mural paintings and is an important component of Dunhuang culture, and its digital preservation, display and research are of great significance. In order to promote the digitization process and development of Dunhuang Dance, this study proposes to combine Dunhuang Dance with three-dimensional human posture estimation technology to construct a three-dimensional movement database of Dunhuang Dance. For Dunhuang dance, the Dunhuang dance is divided, choreographed and captured according to character images, and a video action database of Dunhuang dance is constructed; for posture estimation, AMASS (Archive of Motion Capture as Surface Shapes) large-scale 3D human body motion capture dataset is used as the basis for generative adversarial training, and ResNet is used to extract the image features, and a gated recurrent network is used as the time encoder. The gated recurrent network is used as a time encoder, the parameter regressor regresses the SMPL parameter model, and the attention mechanism is used to amplify the degree of contribution of different frames of the sequence to give the probability of estimating the pose to be true or false on the basis of the human body's real motion AMASS, and then construct the Dunhuang dance 3D action database. This database divides Dunhuang dance into 7 themes, 83 basic movements and 16 long movements. Better results were obtained in quantitative, qualitative and manual evaluation, which laid the foundation for the preservation, application and development of Dunhuang dance; and provided new ideas for the research, promotion and inheritance of Dunhuang culture. Subsequent utilization of this database can be applied to generative artificial intelligence, digital exhibition and performance of Dunhuang dance culture, education and research of Dunhuang dance, and digital media and entertainment of Dunhuang dance.

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!

The market for artificial intelligence grew beyond *** billion U.S. dollars in 2025, a considerable jump of nearly ** billion compared to 2023. This staggering growth is expected to continue, with the market racing past the trillion U.S. dollar mark in 2031. AI demands data Data management remains the most difficult task of AI-related infrastructure. This challenge takes many forms for AI companies. Some require more specific data, while others have difficulty maintaining and organizing the data their enterprise already possesses. Large international bodies like the EU, the US, and China all have limitations on how much data can be stored outside their borders. Together, these bodies pose significant challenges to data-hungry AI companies. AI could boost productivity growth Both in productivity and labor changes, the U.S. is likely to be heavily impacted by the adoption of AI. This impact need not be purely negative. Labor rotation, if handled correctly, can swiftly move workers to more productive and value-added industries rather than simple manual labor ones. In turn, these industry shifts will lead to a more productive economy. Indeed, AI could boost U.S. labor productivity growth over a 10-year period. This, of course, depends on various factors, such as how powerful the next generation of AI is, the difficulty of tasks it will be able to perform, and the number of workers displaced.

The digital age is transforming education, especially now that generative AI expands the possibilities for students and teachers. This change requires critical reflection on the educational and social impact of technology and a rethinking of methods and priorities for advanced and sustainable interventions by educators (Williamson et al., 2020). Added to this is how the information landscape is undergoing a significant transformation too, meeting the spread of the Open Education concept and the growth of Open Educational Resources (OER). The Open approach seeks to promotes a culture of shared knowledge to remove barriers, leveraging digital technologies, and connecting formal and informal learning (Inamorato dos Santos et al., 2016).

Within this paradigm, teachers continue to play a key role especially in the quality of instruction and learning (Darling-Hammond et al., 2017). Improving their continuos training is widely recognized as a priority in international educational policies (OECD, 2019) and European strategies (European Council, 2020).

From this perspective, the ENCORE (ENriching Circular use of OeR for Education) Project emerges (https://project-encore.eu/ ). The ENCORE Approach is based on the idea of developing an innovative AI-based system to support the search and collection of high-quality OER to improve the teaching-learning process in a context of global challenges and lifelong learning (Raffaghelli et al., 2023). During the second part of the piloting of the ENCORE approach and platform, external workshops were organized for HE teachers, VET trainers and learners (external pilots). Eight external pilots were organized for the occasion, involving 226 participants. To collect data on the acceptance level of the tested instrument, the questionnaire used was inspired by a shortened version of the UTAUT model (Venkatesh et al., 2003; Kurelovic, 2020; Raffaghelli et al., 2022).

This dataset presents the 92 questionnaires collected by individual partners, already polished for study or use. The dataset introduces:

1. A codebook with relevant variables adopted to collect sample distribution data (Provenance and Institution) and inherent to the survey object (Area of Knowledge, Level of Usefulness, Intention to Use, ENCORE Best and Least and ENCORE improvements).
2. The procedures and questionnaire’s questions adopted.
3. The main dataset.

This dataset is complementary to the narrative reports produced by each institution and the final report to be constructed later.

References

Darling-Hammond, L., Hyler, M. E., & Gardner, M. (2017). Effective Teacher Professional Development. Learning Policy Institute, 1-76. https://learningpolicyinstitute.org/sites/default/files/productfiles/Effective_Teacher_Professional_Development_REPORT.pdf

European Council (2020). Council conclusions on “European teachers and trainers for the future”. Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52020XG0609(02)&rid=5

Inamorato Dos Santos, A., Punie, Y., & Castaño, M. J. (2016). Opening up Education: A Support Framework for Higher Education Institutions. JRC Publications Repository. https://doi.org/10.2791/293408

Kurelovic, E. K. (2020). Acceptance of open educational resources driven by the culture of openness. In INTED2020 Proceedings (pp. 429-435). IATED

OECD (2019). TALIS 2018 Results (Vol. 1): Teachers and School Leaders as Lifelong Learners. OECD Publishing. https://doi.org/10.1787/1d0bc92a-en

Raffaghelli, J.E., Foschi, L.C., Crudele, F., Doria, B., Grion, V., & Cecchinato, G. (2023). The ENCORE Approach. Pedagogy of an AI-driven system to integrate OER in Higher Education & V ET. In ENCORE project results [Report]. ENCORE. https://www.research.unipd.it/handle/11577/3502320

Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F.D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly. https://doi.org/10.2307/30036540

Williamson, B., Eynon, R., & Potter, J. (2020). Pandemic politics, pedagogies and practices: Digital technologies and distance education during the coronavirus emergency. Learning, Media and Technology, 45(2). https://doi.org/10.1080/17439884.2020.1761641

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.

Ebitda-Per-Share Time Series for Intapp Inc. Intapp, Inc., through its subsidiary, Integration Appliance, Inc., provides AI-powered solutions in the United States, the United Kingdom, and internationally. Its solutions include DealCloud, a deal and relationship management solution that manages client relationships, prospective clients and investments, current engagements and deal processes, and operations and compliance activities; compliance solutions, that help firms thoroughly evaluate new business, onboard clients quickly, and monitor relationships for risk throughout their business lifecycle; time solutions provides AI-enabled software solutions includes time management software that accelerates billing, enhances realization, and experience for the firm's clients; collaboration solutions offers intelligent client-centric collaboration, seamless content governance, and client experiences leveraging Microsoft 365, Teams, and SharePoint; integration solutions that connects firm data into a single platform, tailored to the needs of professional, and financial services firms, as well as solutions that extend the value of our platform with third-party data; and Assist, that leverages the unique client data in the Intapp intelligent cloud and applies generative AI to power business development, reduce manual work, and help professionals make better-informed and faster decisions. The company also operates technology platforms , such as cloud-based architecture, low-code configurability and personalized UX, applied AI, and industry-specific data architecture. It serves private capital, investment banking, legal, accounting, and consulting firms. The company sells its software on a subscription basis through a direct enterprise sales model. The company was formerly known as LegalApp Holdings, Inc. and changed its name to Intapp, Inc. in February 2021. Intapp, Inc. was founded in 2000 and is headquartered in Palo Alto, California.

Free-Cash-Flow-To-The-Firm Time Series for The Hackett Group Inc. The Hackett Group, Inc. operates as an intellectual property platform-based generative artificial intelligence strategic consulting and executive advisory digital transformation in the United States, Europe, and internationally. The company offers Hackett Connect, an online searchable repository of practices, performance metrics, conference presentations, and associated research; Best Practice Accelerators, a dedicated web-based access to practices, customized software configuration tools, and practice process flows; Advisor Inquiry for access to fact-based advice on proven approaches and methods; research and insight derived from Hackett benchmark, performance, and transformation studies; Peer Interaction, a regular member-led webcasts, annual best practice conferences, annual member forums, membership performance surveys, and client-submitted content; and introduction of new gen AI content and vendor IP- centric offerings. It also provides benchmarking services that conduct studies for selling, general and administrative, finance, human resources, information technology, procurement, enterprise performance management, and shared services; operational assessments, process and organization design, change management, and application of technology. In addition, the company offers oracle solutions that help clients to choose and deploy oracle applications that meet needs and objectives; SAP solutions, including planning, architecture, and vendor evaluation and selection through implementation, customization, testing, and integration; post-implementation support, change and exception management, process transparency, system documentation, and end-user training; off-shore application development, and application maintenance and support services; and sells SAP suite of applications. The company was formerly known as Answerthink, Inc. and changed its name to The Hackett Group, Inc. in 2008. The Hackett Group, Inc. was founded in 1991 and is headquartered in Miami, Florida.

The size of the Supply Chain Management Market market was valued at USD 23.26 billion in 2023 and is projected to reach USD 48.90 billion by 2032, with an expected CAGR of 11.2 % during the forecast period. The supply chain management (SCM) market is made up of technologies and services that help in the planning, management and execution of supply chain with its key subsystems of materials, services, information and cash. Some of the systems include those for, logistics, supply chain inventory, procurement, and demand planning. SCM is vital in improving on operational performance, cost reduction and in meeting the customer needs. Uses are in manufacturing, retailing, and in health care domain among others. Today’s trends include the use of innovative technologies – Artificial Intelligence, Machine Learning and Blockchain for improved transparency, real-time tracking and analytics-based predicting. Because of e commerce ad globalization the supply chain is continuously on the look out for more advanced and developed SCM that can effectively and efficiently work with more complicated and constantly changing supply networks. Recent developments include: In May 2023, Accenture and Blue Yonder, Inc. announced the expansion of their strategic partnership to enhance organizations' supply chains by leveraging Accenture's technology and industry expertise. Accenture's cloud-native platform engineers and industry experts will collaborate with Blue Yonder to develop new solutions on the Blue Yonder Luminate Platform, offering end-to-end supply chain synchronization. The partnership aimed to help clients achieve a more modular, digitized, and agile supply chain of the future through co-innovation and the Vertical of emerging technologies such as generative artificial intelligence and robotics process automation. , In April 2023, Oracle introduced advanced artificial intelligence (AI) and automation capabilities designed to assist customers in optimizing their supply chain management processes. These new features leveraged AI and automation technologies to enhance efficiency, streamline operations, and enable better decision-making within supply chain management for its customers. The updates included improved quote-to-cash procedures in Oracle Fusion Vertical s and new planning, usage-based pricing, and rebate management features in Oracle Fusion Cloud Supply Chain & Manufacturing (SCM). .

Computer vision has been receiving increasing attention with the recent complex Generative AI models released by tech industry giants, such as OpenAI and Google. However, there is a specific subfield that we wanted to focus on, that is, Image-Guided Object Detection. A detailed literature survey directed us towards a successful study called Simple Open-Vocabulary Object Detection with Vision Transformers (OWL-ViT) [1], which is a multifunctional complex model that can also perform image-guided object detection as a side function. In this study, some experiments have been conducted utilizing OWL-ViT architecture as the base model and manipulated the necessary parts to achieve a better one-shot performance. Code and models are available on GitHub.

The size of the Enterprise Governance Risk Compliance Market was valued at USD 67.8 billion in 2023 and is projected to reach USD 177.30 billion by 2032, with an expected CAGR of 14.72% during the forecast period. EGRC stands for Enterprise Governance, Risk, and Compliance. The Solution can assist organizations to improve management of the overall governance framework besides helping in discovering risks, as well as enabling the tracking of compliance with external laws and regulations along with their internal policies. The risk assessment, monitoring of policies, and handling compliance procedures are also streamlined on a unified platform with key features of risk identification, automated reporting, auditing, and real-time monitoring. These platforms are employed across industries including finance, health care, manufacturing, and IT in managing risks and enforcing policies about regulation. The typical technologies in the EGRC market are AI, ML, cloud computing, and data analytics. In this respect, the EGRC solutions significantly have an impact in streamlining the governance processes enhancing the organizational transparency and also reducing risk. The key benefits include better decision-making, reduced compliance costs, and strengthened internal controls. Increasing complexity in regulatory regimes and real-time risk mitigation are major drivers for market growth. In the face of stronger regulations and greater cyber risks, EGRC solutions are crucial to maintain business continuity and protect corporate integrity. Recent developments include: June 2023: Copyleaks, the industry-leading platform for AI-based text analysis, plagiarism detection, and AI-content detection, today declared the official launch of the Generative AI Governance, Risk, & Compliance (GRC) solution, a comprehensive set of security measures designed to guarantee generative AI enterprise compliance, lower overall risk, and protect confidential information. It is more important than ever for the Chief Information Security Officers to identify the content created by AI in order to prevent any potential privacy, accuracy, and security vulnerabilities across the enterprise. This is due to the proliferation of AI-generated content, the risk of unintended use of copyrighted content or code without a license, as well as potential AI regulation on the horizon. In response, Copyleaks has created a new product specifically for corporate organizations using its award-winning plagiarism & AI content detection capabilities., May 2023: A crucial round of investment has been successfully obtained for Onspring, a SaaS (software as a service) platform which specializes in governance, risk, & compliance (GRC).The automated SaaS GRC software provides no-code connectivity throughout an organization, providing firms with a creative and comprehensive solution. Capital IP, a leading strategic partner meant to support Onspring's future growth, contributed this round of funding. The money will be used for a variety of upgrades, such as new product development and platform advancements in AI (artificial intelligence), integrations, & user experience. The ultimate objective is to hasten market share growth within the emerging GRC industry., January 2023: Exterro, a provider of Legal Governance, Risk and Compliance (GRC) software, is a portfolio company of Leeds Equity Partners. It has acquired a majority stake in Zapproved, a provider of e-discovery software for corporate legal teams. Vista Equity Partners ("Vista"), a chief global investment firm exclusively focused on enterprise software, data, and technology-enabled businesses, announced the transaction today. Vista will continue to own a small minority share in the growing company. The deal's financial specifics were not made public., March 2022: IBM came up with IBM OpenPages embedded with Watson 8.3. This integrated GRC platform does deliver a task-focused user interface for assisting organizations in the maintenance of risk and compliance initiatives., January 2022: SAP launched access violation management technology to assist in real-time risk analysis & provisioning. Additionally, it helps in user access reviews, in the management of roles, and provides emergency access management for various enterprise applications., May 2021: Wolter Kluwer's Compliance Solution announced the Deposit and IRA Document Suite (DIDS). The Suite features a full library of deposit and IRA (individual retirement account) banking documents, with access to both tailored and static content, including IRA amendments and unlimited rights to warrantied deposit disclosure language.. Notable trends are: Increasing Compliance Requirements to Drive the Market.