Explore the vast range of titles available.

This study proposes a functional framework to enhance the efficiency and effectiveness of e-learning content creation by systematically integrating generative artificial intelligence (AI) technologies. While previous research on e-learning has primarily focused on systems and infrastructure, little attention has been given to content creation. To address this gap, we present a five-step methodology: (1) conducting a systematic literature review of existing e-learning development frameworks; (2) proposing a content-specific framework centered on instructors and technical support roles; (3) outlining a detailed task-based content creation process; (4) identifying and classifying commercial AI tools applicable to each functional unit; and (5) comparing the tools based on their strengths, limitations, and suitability. The proposed framework includes eight key functional stages, ranging from lesson planning to editing, automation, and final review. For each stage, AI tools such as ChatGPT, Synthesia, MidJourney, and Grammarly are evaluated and mapped to the corresponding workflow phase. The findings suggest that integrating AI tools into content creation can significantly reduce production time and cost, improve instructional quality, and lower e-learning sector entry barriers. This study contributes a conceptual model and practical strategies for leveraging AI in scalable, high-quality digital education environments.

Artificial Intelligence (AI) and Machine Learning (ML) technologies offer the potential to support digital content creation and media production, providing opportunities for individuals from diverse sociodemographic backgrounds to engage in creative activities and enhance their multimedia video content. However, less attention has been paid to recent research exploring any possible relationships between AI-generated video creation and the sociodemographic variables of undergraduate students. This study aims to investigate the multifaceted relationship between AI-generated video content and sociodemographics by examining its implications for inclusivity, equity, and representation in the digital media landscape. An empirical study about the use of AI in video content creation was conducted with a diverse cohort of three hundred ninety-eighth undergraduate ( n  = 398) students. Participants voluntarily took part and were tasked with conceiving and crafting their AI-generated video content. All instruments used were combined into a single web-based self-report questionnaire that was delivered to all participants via email. Key research findings demonstrate that students have a favorable disposition when it comes to incorporating AI-supported learning tasks. The factors fostering this favorable attitude among students include their age, the number of devices they use, the time they dedicate to utilizing technological resources, and their level of experience. Nevertheless, it is the student’s participation in AI training courses that exerts a direct impact on students’ ML attitudes, along with their level of contentment with the reliability of these technologies. This study contributes to a more comprehensive understanding of the transformative power of AI in video content creation and underscores the importance of considering instructional contexts and policies to ensure a fair and equitable digital media platform for students from diverse sociodemographic backgrounds.

Since late 2022, dozens of YouTube channels focusing on a diverse array of topics related to language learning with generative AI tools such as ChatGPT have rapidly emerged. This study explores the implementations and perspectives of YouTube content creators who now constitute an increasingly important segment of the ecosystem of language teaching and learning. A mixed methods netnographic approach was employed, combining qualitative and quantitative techniques. A total of 140 videos were identified and analyzed, and an in-depth content analysis was conducted to uncover underlying themes. Four main categories of creators were identified: educators, learners, technology professionals, and e-learning providers. Educators, especially English and Japanese teachers, were the majority, followed by learners and technology field professionals. This study highlights the benefits, drawbacks, and concerns associated with the integration of AI tools in language learning. By examining this rapidly evolving phenomenon, the study contributes towards an understanding of the role and impact of generative AI tools in language education.

The application of social cognitive theory has expanded to the boundaries of human-computer interaction research. However, existing research has scarcely addressed mutual cognitive facilitation between humans and personalized educational large language model (LLM) agents. This study explored how educational LLM agents influence teachers’ curriculum design and content creation, based on a sample of 464 teachers from coastal regions of China, along with semi-structured interviews with 23 participants. Quantitative analysis of the survey data revealed that the involvement of educational LLM agents positively predicts teachers’ ability to create content in curriculum design. Additionally, teachers’ self-efficacy mediated this relationship, while both school support and self-efficacy together created a chain mediation effect. Qualitative findings from the interviews supported the quantitative results and further highlighted individual differences and contextual nuances in teachers’ use of educational LLM agents. In summary, the findings indicated that educational LLM agents positively impact teachers’ curriculum design and content creation, with school support and teachers’ self-efficacy acting as a chain mediator in this process.

PurposeThe proliferation of non-fungible token (NFT)-based crypto-art platforms has transformed how creators manage, own and earn money through the creation, assets and identity of their digital works. Despite this, no studies have examined the drivers of continuous content contribution behavior (CCCB) toward NFTs. Hence, this study draws on the theory of relational bonds to examine how various relational bonds affect feelings of psychological ownership, which, in turn, affects CCCB on metaverse platforms.Design/methodology/approachUsing structural equation modeling and importance-performance matrix analysis, an online survey of 434 content creators from prominent NFT platforms empirically validated the research hypotheses.FindingsFinancial, structural, and social bonds positively affect psychological ownership, which in turn encourages CCCBs. The results of the importance-performance matrix analysis reveal that male content creators prioritized virtual reputation and social enhancement, whereas female content creators prioritized personalization and monetary gains.Originality/valueWe examine Web 3.0 and the NFT creators’ network that characterizes the governance practices of the metaverse. Consequently, the findings facilitate a better understanding of creator economy and meta-verse commerce.

This study aims to examine the effect of determinants of the quality of tax audits for content creation tax on formal tax compliance and actual tax compliance. We used the questionnaire tool to collect data for a sample of 179 tax auditors, also we used partial least squares (PLS) to test the hypotheses. The result showed each of the regulations and laws, the quality of communication between the tax authority and taxpayers and the determinants associated with organizations have a positive effect on formal tax compliance. Meanwhile, the regulations and laws have a positive effect on actual tax. Also, there is no effect for both the quality of communication between the tax authority and taxpayers and the determinants associated with organizations on actual tax compliance. This study focused on the income tax on the profits of content creators and did not address the value-added tax on content creation. This study was also limited to the opinions of tax auditors and did not address the opinions of content creators. Plain Language Summary Quality of tax audits for content creation tax and tax compliance Although there are many studies that dealt with the issue of taxes in Egypt. But studies addressing the content creator tax are still limited. The purpose of the study to examine the effect of determinants of the quality of tax audits for content creation tax on formal tax compliance and actual tax compliance.

Animation production is increasingly shaped by collaborative workflows and compute-intensive pipelines, where content creation, model inference, and high-quality rendering are often conducted in parallel by geographically distributed users. However, conventional animation tools are largely built around standalone environments, making it difficult to scale computation resources or support efficient multi-user collaboration. Cloud platforms offer elastic resources and distributed execution capabilities, yet integrating animation generation, rendering, and collaboration into a unified cloud-based system remains challenging. This paper presents a cloud-based collaborative animation generation system that integrates open-source generative models with distributed rendering and resource scheduling services. The proposed framework enables multiple users to concurrently submit animation tasks, which are decomposed into generation and rendering subtasks and executed across cloud nodes through a coordinated orchestration layer. A distributed rendering pipeline is developed to support scalable execution of Blender-based workloads, while a resource scheduling mechanism is designed to manage heterogeneous GPU resources and dynamically allocate tasks according to workload conditions. Extensive experiments are conducted to evaluate both system-level and animation-oriented performance. The results demonstrate that the proposed system can effectively support concurrent animation creation while achieving stable performance gains and improved resource efficiency in cloud environments.

Finding the right content determines success in diverse activities and in various marketing functions such as advertising, public relations, social media marketing, customer relationship management, inbound marketing or personal sales. High-quality content is needed when communicating brand purpose, responding to a social media firestorm or investing in high-reach channels such as traditional TV advertising. Top-of-funnel marketing activities such as blog or social media posts, search engine marketing, press releases, e-mail marketing, cold outreach in sales or the creation of landing pages for inbound marketing all benefit from more efficient creation of more content. According to estimates from communication giant WPP, savings can be as much as 10 to 20 times.

In the digital transformation era, the application of deep learning in image style conversion has opened new avenues for creative expression and business innovation. This article explores advanced deep learning techniques to achieve efficient and accurate image style conversion in different image datasets. The method has been validated through comprehensive experiments on the Microsoft Common Objects in Context dataset, and compared to existing methods, this article's method demonstrates outstanding performance metrics including peak signal-to-noise ratio, structural similarity, and user satisfaction. In addition, the impact of different hardware configurations on processing efficiency was analyzed, providing insights into optimizing computing resources for real-time applications. The results of this case study not only contribute to the theoretical advancement of image processing but also provide practical solutions for the visual content creation industry.

With the advancements in science and technology and the improvement of medical care, mental health problems are receiving increasing attention. Increasing numbers of children, adolescents, and young adults are susceptible to anxiety. This paper assesses young adults based on self-determination theory and the theory of planned behaviour to determine the intrinsic and extrinsic motivations and mediating variables behind young adults’ content creation behaviour within anxiety disorder online communities (ADOCs). In addition, the paper introduces empathy as a moderating variable, builds a model of the content creation behavioural motivation of young adults, studies the motivation behind young adults’ content creation behaviour in ADOCs, and determines the moderating effect of empathy on young adults’ content creation behaviour. The research data were obtained using a questionnaire survey, and the SmartPLS structural equation model was used for empirical analysis. The study found that expressing one’s anxiety was the most obvious motivation, the content creation intention of young adults significantly positively affected their content creation behaviour, perceived enjoyment motivation had a significant negative influence on young adults’ intention to create content, reward motivation had no significant influence on the content creation intention of young adults, other motivations had significant positive influences on young adults’ content creation intention, and empathy only had a significant negative moderating effect on the relationship between self-efficacy and young adults’ content creation intention. This study not only enriches and expands research on motivation theory but also has practical significance for the improvement and active development of ADOCs.