More and more frequently, digital applications make use of Artificial Intelligence (AI) capabilities to provide advanced features; on the other hand, human-in-the-loop approaches are on the rise to involve people in AI-powered pipelines for data collection, results validation and decision making. Does the introduction of AI features affect user acceptance? Does the AI result quality affect people’s willingness to use such applications? Does the additional user effort required in human-in-the-loop mechanisms change the application adoption and use? This study aims to provide a reference approach to answer those questions. We propose a model that extends the Technology Acceptance Model (TAM) with further constructs explicitly related to AI – user trust in AI and perceived quality of AI output, from explainable AI (XAI) literature – and collaborative intention – willingness to contribute to AI pipelines. We tested the proposed model with an application for car damage claim reporting with AI-powered damage estimation for insurance customers. The results showed that the XAI related factors have a strong and positive effect on behavioral intention, perceived usefulness, and ease of use of the application. Moreover, there is a strong link between behavioral intention and collaborative intention, indicating that indeed human-in-the-loop approaches can be successfully adopted in final user applications.

Users were invited to test the interactive prototype of the BumpOut application and to report the given car accident from start to finish. These are the two interactive prototypes experienced by users:

FlawlessAI-Group prototype

FailingAI-Group prototype

This study is shared as a research object adopting the RO-Crate specification.

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.

IntroductionArtificial Intelligence (AI) is a transformative technology impacting various sectors of society and the economy. Understanding the factors influencing AI adoption is critical for both research and practice. This study focuses on two key objectives: (1) validating an extended version of the Technology Acceptance Model (TAM) in the context of AI by integrating the Big Five personality traits and AI mindset, and (2) conducting an exploratory k-prototype analysis to classify AI adopters based on demographics, AI-related attitudes, and usage patterns.MethodsA sample of N = 1,007 individuals individuals (60% female; M = 30.92; SD = 8.63 years) was collected. Psychometric data were obtained using validated scales for TAM constructs, Big Five personality traits, and AI mindset. Regression analysis was used to validate TAM, and a k-prototype clustering algorithm was applied to classify participants into adopter categories.ResultsThe psychometric analysis confirmed the validity of the extended TAM. Perceived usefulness was the strongest predictor of attitudes towards AI usage (β = 0.34, p 

In the year 2024, the market value of agentic artificial intelligence (AI) stood at 5.1 billion U.S.dollars. It is anticipated that this market value will surpass 47 billion U.S.dollars, with a compound annual growth rate of over 44 percent, as reported by Capgemini. This tremendous growth demonstrates the potential of agentic AI to transform industries through autonomous action and decision-making.

IntroductionMedia students' acceptance and use of intelligent technology requires not only external environmental support but also the stimulation of internal driving forces. This study incorporates mindfulness into the classical Technology Acceptance Model (TAM) to investigate its role in shaping students' perceptions and behavioral intentions toward artificial intelligence (AI) tools. Specifically, the research examines: (1) the impact of mindfulness on media students' perceptions of AI technology; (2) the influence of mindfulness on their continuous intention to use AI technology; and (3) the moderating effect of perceived risk on the intention to adopt AI tools.MethodsBased on a conceptual model integrating mindfulness with TAM, this study conducted an offline questionnaire survey among 588 media students. The data were analyzed using SmartPLS and SPSS to test the structural equation modeling and moderating effects.ResultsThe findings revealed three key outcomes. First, mindfulness exerts a significant, direct, and positive influence on personal innovativeness (PI), perceived usefulness (PU), and perceived ease of use (PEU). Second, PI functions as a mediating variable in the relationship between mindfulness and AI-based behavioral intention (AIBI). Third, perceived risk (PR) significantly weakens the relationships between PI, PU, and AIBI.DiscussionThis study demonstrates that mindfulness enhances media students' intention to adopt AI tools by strengthening their perceptions of usefulness, ease of use, and personal innovativeness. However, perceived risk undermines these positive effects. By integrating mindfulness into the Technology Acceptance Model (TAM), this research extends the theoretical understanding of AI technology acceptance and provides practical insights for media education. The findings highlight that embedding mindfulness training and reducing perceived risks can effectively foster rational acceptance and the innovative application of AI tools, thereby contributing to the cultivation of intelligent media talent.

This dataset supports a meta-analytic structural equation modelling (MASEM) study investigating the factors influencing students’ behavioural intention to use educational AI (EAI) technologies. The research integrates constructs from the Technology Acceptance Model (TAM), Theory of Planned Behaviour (TPB), and Artificial Intelligence Literacy (AIL), aiming to resolve inconsistencies in previous studies and improve theoretical understanding of EAI technology adoption.

Research Hypotheses The study hypothesized that: Students’ behavioural intention (INT) to use EAI technologies is influenced by perceived usefulness (PU), perceived ease of use (PEU), attitude (ATT), subjective norm (SN), and perceived behavioural control (PBC), as described in TAM and TPB. AI literacy (AIL) directly and indirectly predicts PU, PEU, ATT, and INT. These relationships are moderated by contextual factors such as academic level (K–12 vs. higher education) and regional economic development (developed vs. developing countries).

What the Data Shows The meta-analytic dataset comprises 166 empirical studies involving over 69,000 participants. It includes pairwise Pearson correlations among seven constructs (PU, PEU, ATT, SN, PBC, INT, AIL) and is used to compute a pooled correlation matrix. This matrix was then used to test three models via MASEM: A baseline TAM-TPB model, An internal-extended model with additional TPB internal paths, An AIL-integrated extended model. The AIL-integrated model achieved the best fit (CFI = 0.997, RMSEA = 0.053) and explained 62.3% of the variance in behavioural intention.

Notable Findings AI literacy (AIL) is the strongest predictor of intention to use EAI technologies (Total Effect = 0.408). PU, ATT, and SN also significantly influence intention. The effect of PEU on intention is fully mediated by PU and ATT. Moderation analysis showed that the relationships differ between developed and developing countries and between K–12 and higher education populations.

How the Data Can Be Interpreted and Used The dataset includes bivariate correlations between variables, publication metadata, sample sizes, coding information, and reliability values (e.g., CR scores). Suitable for replication of MASEM procedures, moderation analysis, and meta-regression. Researchers may use it to test additional theoretical models or assess the influence of new moderators (e.g., AI tool type). Educators and policymakers can leverage insights from the meta-analytic results to inform AI literacy training and technology adoption strategies.

Agentic AI Market is estimated to reach USD 196.6 billion By 2034, Riding on a Strong 43.8% CAGR throughout the forecast period.

The market for artificial intelligence (AI) cybersecurity is expected to show significant growth in the coming years. While valued at **** billion U.S. dollars in 2023, the AI cybersecurity market is forecast to double by 2026, before reaching nearly *** billion U.S. dollars by 2030.

Financial overview and grant giving statistics of Ai Tam Educational Organization Inc.

The data evaluates the factors influencing the adoption of AI Copilot among faculty and students at Cebu Technological University. It operates under the hypothesis that perceived usefulness and perceived ease of use of TAM alongside with the pedagogical dimension of AI Ecological Education Policy Framework(AIEEPF), impact behavioral intentions toward AI Copilot usage.

The study utilized a quantitative approach through structured surveys targeted at students and instructors. A stratified random sampling method ensured representation across different educational levels and roles. The participants were informed about the study's purpose and confidentiality rights, providing written consent before responding. Surveys were distributed via university email, yielding 414 responses. After excluding 18 low-variability responses, the data were analyzed using Structural Equation Modeling (SEM), including means and correlation analyses.

Findings suggest that respondents scored perceived usefulness at approximately 3.87, and perceived ease of use at 3.71, indicating general agreement on the tool's value and usability for academic tasks. A notable relationship was found between the pedagogical dimension and perceived usefulness, with a path coefficient of 0.446, confirming that well-aligned AI tools are more beneficial. High agreement levels emerged concerning the integration of AI in assessments(mean=3.89), the development of holistic competencies(mean=4.09), and the preparation of an AI-driven workforce(mean=4.02), reflecting strong support for AI-enhanced educational practices.

The data suggests favorable perceptions of AI Copilot among students, highlighting the importance of aligning AI technologies with educational goals. It indicates that institutions should integrate AI tools into curricula, invest in ongoing professional development for faculty, customize AI applications for specific educational settings, and address ethical implications, such as bias and transparency.

This data serves as a resource for understanding user perceptions and behavioral intentions regarding AI in education. Educational leaders can leverage these insights to inform AI integration strategies, ensuring they align with pedagogical and ethical needs.

The study advocates for further research on the longitudinal effects of AI adoption, facilitating effective implementations that enhance learning outcomes and prepare students for future challenges in an AI-driven environment.

Storage for data. Data has been used for the PLS-SEM study.

The artificial intelligence (AI) chip market is experiencing rapid growth, with projections indicating it will reach **** billion U.S. dollars in 2025. This surge reflects the increasing demand for AI technologies across various industries. The market's expansion is driven by advancements in machine learning, deep learning, and generative AI applications. Nvidia leads the AI chip race Nvidia has emerged as a dominant player in the AI chip market, with its data center revenue skyrocketing in its 2026 fiscal year. The company's graphics processing units (GPUs) are crucial for training and running large language models, including OpenAI's ChatGPT. Nvidia's success helped propel it into the exclusive tech four trillion club, ahead of industry giants like Microsoft and Apple. GPU market growth and AI applications The global GPU market has caught much of the world’s attention. This growth has been fueled by the expanding AI market, particularly in machine learning and deep learning applications. The generative AI market has also contributed significantly, with projections suggesting it will surpass *** billion U.S. dollars in 2026. These trends underscore the increasing importance of AI chips in powering next-generation technologies and applications.

The purpose of this study is to gain insights into significant decision factors that influence the adoption of mobile banking in the Netherlands. Gaining insights on factors affecting user adoption of mobile banking will help banks to formulate interventions to improve user acceptance. Especially now, with the Corona crisis, such initiatives in mobile technology are even more relevant for banking. The survey is completely anonymous, it consists of 20 different questions divided over sections A, B, C, and D which will take approximately 10 minutes to answer.

This dataset contains responses from 507 university students in Jakarta regarding their readiness and perceptions toward AI-based learning media in the context of social media-supported collaborative learning. The data were used in a Structural Equation Modeling (SEM-PLS) analysis to test an extended Technology Acceptance Model (TAM). The final dataset used in the analysis consisted of 401 valid responses.

This study, based on a quantitative analysis of 221 Chinese middle school teachers, systematically explores the multi-dimensional mechanisms underlying the adoption of artificial intelligence (AI) educational technology. The key findings are as follows: (1) In terms of technology acceptance, perceived usefulness (β = .443) and ease of use (β = .353) collectively account for 59.3% of the variance in teaching cognition, thereby validating the applicability of the Technology Acceptance Model (TAM) in the context of educational AI; (2) Demographic factors exhibit selective influence, with ICT subject teachers demonstrating significantly higher teaching cognition than their non-ICT counterparts (ΔM = 0.82, p < .01), and male teachers showing stronger willingness to adopt AI (U = 3.21, p < .05). These results challenge the widely accepted "gender neutrality" conclusion in Western research; (3) The predictive mechanism of ethical cognition reveals unique characteristics, with 19.1% of its explanatory power primarily attributed to ease of use (β = .378) rather than risk perception (p > .05), indicating that middle school teachers prioritize the operational feasibility of AI over abstract ethical concerns. Theoretically, this study delineates the boundaries of TAM's applicability in an Eastern educational context while identifying cultural specificities in gender differences (U = 3.21) and subject background (ΔM = 0.82). Practically, it proposes a three-tiered development framework—demand-oriented curriculum design, low-threshold skill progression, and interdisciplinary collaboration communities—and innovates context-specific ethical training approaches to enhance the operationalization of ethical considerations and increase AI tool usage rates. Future research is encouraged to expand geographically into underdeveloped regions in central and western China and explore the integration of large-scale AI models into teaching assistants to further validate their impact on perceived usefulness. This study contributes new empirical evidence toward constructing a more universal theory of educational technology diffusion.

This dataset explores the application of the Technology Acceptance Model (TAM) in the context of smart lighting systems. It encompasses a comprehensive collection of observations and measurements about user perceptions and interactions with AI-driven lighting controls. The dataset includes variables representing key TAM constructs such as Perceived Ease of Use (PEOU), Perceived Usefulness (PU), and Behavioral Intention (BI), alongside additional metrics quantifying aspects like accuracy and comfort. The dataset offers a valuable resource for researchers and practitioners seeking to investigate the impact of AI integration on user acceptance and comfort in smart lighting environments. Its rich and structured nature facilitates in-depth analyses, model development, and validating hypotheses related to technology adoption and user experience in smart lighting.

Highly evolved and capable, ChatGPT is an intelligent chatbot with great implications for fostering active student learning due to its capacity to respond quickly to academic queries as well as to engage in dynamic interactions with the learner. In the present research which was conducted within the Saudi university context, we studied how intrinsic motivation and factors related to TAM (technology acceptance model) influenced undergraduate students’ acceptance of ChatGPT as a tool for learning actively. The study adopted a structural equation approach to investigate the extended TAM model in tertiary education. The results of the revealed that intrinsic motivation, perceived usefulness, and perceived ease of use were found to be significant predictors of behavioral intention. Finally, the study highlights that AI-based tools as user-friendly, beneficial, engaging and intriguing promote students’ active learning and enhance their involvement in the learning process and, thus, their acquisition of new knowledge.

Translation (Gun Mai) Long long ago in a village, there were two men who had already married. Although a married man was very poor, he drove a chariot and could support well his family. He supported his family needlessly. He also used to give the money that he made to his wife. He also loved his wife very much. Another married man was rich but he did not know how to show love to his wife. He got drunk and used to abuse his wife as well as his children. He did not know to love his children and just used to rebuke and persecute. Thus, since the wife also could not tolerent anymore, she returned home. So, the man was left alone. The man just continued to get drunk. And, he saw how the other man loved and supported well the family. So, the rich man thought to himself that "I am such a bad guy. Even though I have children, I could not support and feed them well. Even I live alone, I show love no one and take care of no one." One day, the rich married man was about to die. So, he called the poor married man, who was living next to him and the man gave his two pairs of horses to the poor man. And, he also gave his house. "You are a good man. You could support well your wife and children. May be you are only such a man. Although I had a wife and children, they have gone now because I could not support them well. Thus, I gave you everything that I have. Just pray for me. Take my properties and continue to take care of your wife and children," the rich married man told the poor married man. Transcription (Lu Awng) Moi shawng e da kahtawng langai mi kaw she da numshang sha ai la 2 nga ai da. Num shang sha ai la 2 nga ai da, dai la langai mi gaw da grai matsan timmung da shi gaw da gumra leng gawt di na madu jan ni kasha ni hpe a tsawm sha chye tam jaw ai da. Tam jaw na she kasha ni madu jan ni hpe ra n rawng hkra tawn ai da, lu wa ai ja gumhpraw ni hpe mung madu jan hpe wa ap da ai da, wa ap da re shaloi she da madu hpe mung grai tsawra ai majaw dai hku nga nga re shaloi da lu lauban ai la numshang sha ai la wa gaw da madu jan e mung n chye tsawra da. Madu jan ni e mung tsa lu wa na wa gayet da, kashu kasha lu timmung da kashu kasha ni e mung n chye tsawra, kasha ni hpe mung pawt zingri dai hku re na nga ai da. Nga re shaloi she da shi gaw madu jan ni gaw dai shi gayet pawt zingri ai dai hpe n lu hkam mat na wa mat ai da. Hprawng mat wa re shaloi she da shi gaw shi hkrai sha nga ra sai da. Shi hkrai sha nga ra na tsa lu hkrai lu, dai hku sha nga nga nga re shaloi she da oh ra maga de na shi manang wa hpe yu ai da, shi gaw yu ai shaloi she shi manang dai wa gaw da grai tam chye da wai da, dum nta hpe mung shi tsawra ai majaw shi atsawm re galaw jaw ai da dai hpe mu la ai da. Shi gaw mu la ai shaloi she da aw da ngai gaw da ngai n hkru n kaja ai she re nga gaw da. Ngai da kashu kasha lu tim mung da kashu kasha ni hpe a tsawm sha n lu tam jaw ai da, ya ngai hkrai sha nga timmung da ngai hpe mung tsawra myit n madun ai da, dai hku nga myit la na she shi gaw lani mi na ten hta si wa sa na re ai da. Si wa maw re shaloi she da dai shanhte nta dai makau kaw na mi yet na matsan la wa hpe dai hkan nu ni hpe shaga la ai da, shaga la na shi gaw shi na gumra leng dap 2 hpe mung dai manang wa hpe ap ai da. Ap kau re tsun na shi na dum nta hpe mung ap kau ai da, ap kau re shaloi i nang gaw da grai ket ai da kashu kasha ni madu jan ni hpe mung a tsawm sha chye tam jaw ai da. Nang zawn re nye manang gaw da nang sha nga ai da. Ngai gaw da madu jan ni kasha ni lu timmung da a tsawm re n chye tam jaw ai majaw da nye madu jan nye kasha ni mung n nga sai da, dai re majaw nang ngai lu ang ai sut gan ni yawng hpe nang hpe ap da sai yaw da, ngai na matu i kaja ai hku sha kyu hpyi ya mu yaw da. Ngai na ndai sutgan ni hpe nang la nna na madu jan ni na kasha ni hpe a tsawm sha bau maka u yaw ngu na htet kau da ai da. . Language as given: Jinghpaw

The Artificial Intelligence (AI) Chip Market was valued at USD 52.92 Bn in 2024, and it is projected to reach USD 295.56 Bn by 2030 with the CAGR of 33.2%.

This dataset and accompanying materials are part of a study investigating the user acceptance and perceived value of AI-powered virtual singers in multilingual music production. The research, conducted with 378 global music practitioners and educators, explores key factors influencing the adoption of AI virtual singers, including technical familiarity, simulation fidelity, cost efficiency, and the balance between functional and cultural utility. Through a mixed-methods approach combining quantitative surveys and qualitative sentiment analysis, the study provides insights into how AI virtual singers are perceived by music producers and educators, highlighting both opportunities and challenges for their integration into the music production process.The dataset includes responses from a multilingual questionnaire measuring user perceptions, as well as the Python analysis code used for sentiment analysis of open-ended responses. Key findings suggest that technical familiarity significantly correlates with acceptance, and while AI virtual singers offer cost and workflow advantages, limitations in emotional expressiveness remain a major barrier to wider adoption. The research also identifies the importance of software optimization for different occupational groups and recommends further development in affective synthesis models for improved emotional authenticity in virtual singers.