ZUMBLEBOT - AN UNIFIED GENERATIVE AI PLATFORM FOR EFFORTLESS MULTIMEDIA CREATION

Generative AI, a branch of artificial intelligence, is revolutionizing the creation of original content, including text, images, music, and videos. When integrated with blockchain technology, it creates a powerful synergy that combines creativity with enhanced security. Blockchain's immutable and tamper-proof properties can authenticate generative AI outputs, ensuring trust and originality. For instance, artists can timestamp their works on the blockchain, providing undeniable proof of ownership and authenticity. This fusion also addresses critical challenges faced by businesses adopting generative AI chatbots like ChatGPT, including mitigating the risks of false outputs (“hallucinations”) and safeguarding intellectual property used in AI training.

The article examines the challenges of forming marketing communications in Ukraine’s banking sector amid economic instability caused by the ongoing war. It explores the specifics of the banking business, which encompasses a broad range of financial services, and underscores its pivotal role in the national economy. Particular focus is placed on the impact of hostilities on banking infrastructure, resulting in damaged branches, ATMs, and payment systems, customer outflows, and heightened credit risks. Despite these challenges, the banking sector has shown resilience through reforms and anti-crisis measures. The study analyzes the marketing strategies of leading banks such as PrivatBank, Oschadbank, and Ukrgasbank, emphasizing their adoption of digital channels, personalization, and innovations like RCS communications and “green” banking. PrivatBank leads with a robust digital presence, achieving 195 million website visits in 2022–2023 and implementing RCS communications in 2025, while Oschadbank leverages Viber Business Calls and generative AI to enhance customer experience, earning recognition at FinAwards 2025. Ukrgasbank focuses on niche “green” banking, utilizing digital platforms like YouTube and Telegram for educational content. The article highlights current trends in banking marketing, driven by technological advancements, shifting customer expectations, and increased competition. Banks are increasingly using big data and analytics for highly personalized campaigns, improving customer engagement and loyalty. Generative AI plays a transformative role in automating processes, content creation, and customer interaction, making campaigns more dynamic and effective. Digital channels, particularly mobile banking, have become primary platforms for customer interaction, reflecting a shift toward digital solutions. Customer-centricity remains central, with banks investing in seamless experiences, user-friendly interfaces, and loyalty programs like cashback and bonuses. Collaboration with FinTech companies enables banks to offer innovative products, while growing emphasis on sustainability and corporate social responsibility aligns with customers’ increasing awareness of environmental and social issues. The article forecasts that advancements in AI and analytics will drive even more precise personalized marketing, with digital channels, especially mobile banking, remaining dominant. Generative AI will expand its role in content creation, chatbots, and analytics, enhancing efficiency and engagement. Seamless customer experiences will be critical for competitiveness, prompting banks to refine interfaces, streamline processes, and innovate. Data privacy and cybersecurity will gain prominence due to stricter regulations, with banks leveraging these aspects in marketing to emphasize reliability. Sustainability and social responsibility will become integral to branding, as customers increasingly value ethical and eco-conscious initiatives. The success of banking marketing will depend on adapting to rapidly evolving technological and societal trends while ensuring security, convenience, and value for customers in a competitive digital landscape.

Artificial Intelligence has become a focal point of interest across various sectors due to its ability to generate creative and realistic outputs. A specific subset, generative artificial intelligence, has seen significant growth, particularly in late 2022. Tools like ChatGPT, Dall-E, or Midjourney have democratized access to Large Language Models, enabling the creation of human-like content. However, the concept 'Generative Artificial Intelligence lacks a universally accepted definition, leading to potential misunderstandings. While a model that produces any output can be technically seen as generative, the Artificial Intelligent research community often reserves the term for complex models that generate high-quality, human-like material. This paper presents a literature mapping of AI-driven content generation, analyzing 631 solutions published over the last five years to better understand and characterize the Generative Artificial Intelligence landscape. Our findings suggest a dichotomy in the understanding and application of the term "Generative AI". While the broader public often interprets "Generative AI" as AI-driven creation of tangible content, the AI research community mainly discusses generative implementations with an emphasis on the models in use, without explicitly categorizing their work under the term "Generative AI".

Generative AI, through the efficient production of various forms of material, has the ability to completely transform the way that course content is developed. The use of generative AI in the creation of course content is crucial since it may further optimize the procedure, lessen the strain for teachers, and provide personalized, excellent, and captivating learning resources. The study aims to demonstrate how generative AI is included in the creation of course materials. Additionally, it suggests the use of a new framework called "Generative AI for Instructional Development and Education," or "GAIDE," which further makes use of AI's potential to improve educational outcomes. The use of generative AI in this framework demonstrated in particular how effective and useful these integrations were for creating instructional content and how applicable they were for creating, modifying, and evaluating the content. A mixed-methods strategy is being used in this project, which integrates quantitative and qualitative data collection techniques. The study's sample was primarily made up of about 75 Indian volunteers. The evaluation metrics for this study primarily consist of structured questionnaires and analysis using SPSS software for quantitative data analysis. Thematic analysis is used to evaluate secondary qualitative data in order to identify numerous recurrent themes and various patterns. As per the findings of this study, the applicability of generative AI is emergent and progressive for course content generation through stages of prompts and refinement, while the impact is held strongly through the efficiency delivered in faster and more extensive means for generating and refining quality course content through generative AI.

While industry practices evolve rapidly, marketing education in Australia and New Zealand faces challenges in keeping pace, particularly regarding the adoption of current marketing technologies (Harrigan et al., 2022). Generative AI, exemplified by systems like ChatGPT and DALL·E, has demonstrated benefits for learning (Baidoo-Anu & Ansah, 2023). However, despite its potential, there remains a dearth of practical guidance on effectively incorporating these technologies into marketing courses. This gap persists even as general frameworks for responsible and ethical AI use, such as the Australian Framework for Generative AI in Schools (2023), emerge. As the demand for graduates with generative AI skills grows in the job market, educators must explore innovative pedagogical approaches to bridge this gap. This academic poster presents an innovative application of generative artificial intelligence (GenAI) in the context of teaching digital marketing at the postgraduate level. Its purpose is to bridge the gap between academic theory and industry practice by encouraging educators to integrate AI tools into their curriculum through experiential learning pedagogy (Kolb, 2014), characterized by a learning process whereby knowledge is created through hands-on experiences. The poster exemplifies how various types of GenAI technologies — specifically text-based, image-based, and video-based — can enhance teaching content, tutorial exercises, and assessments within the digital marketing course. The poster showcases examples of how these GenAI tools are integrated in the course content, to guide students in generating innovative ideas for using AI in marketing to gain a competitive edge: Text-based GenAI: Tools like ChatGPT and Gemini can automatically generate search keywords for search engine marketing. By integrating text-based GenAI tools with established marketing technology (MarTech) tools such as Google Ads and Google Ads Keyword Planner, students engage in practical exercises that combine AI-generated initial ideas (e.g., search keywords) with further analysis (e.g., search volume, click-through rates, and bidding costs) using established MarTech tools. This hands-on approach enhances their learning experience and prepares them for real-world applications. Image-based GenAI: Platforms such as DALL·E, Midjourney, and Stable Diffusion enable the creation of custom images for display advertising, enhancing visual communication in marketing materials. Through experiential learning activities, students can explore ideas, seek unusual combinations, and inspire creativity faster with image-based GenAI tools, resulting in a greater variety of display ad materials. Video-based GenAI: Applications like Sora and Synthesia facilitate the production of short video clips suitable for social media marketing (e.g., YouTube Shorts, TikTok). By engaging in dynamic content creation exercises, students learn to streamline content creation, reduce manual work, and save both time and budget, thereby gaining practical skills in social media marketing. By incorporating these GenAI technologies through experiential learning pedagogy, educators can enrich the learning experience, foster critical thinking, and prepare students for the evolving landscape of digital marketing. Future research can study the use of GenAI in marketing education using theoretical frameworks such as the Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2016).

While large language models (LLMs) like GPTs show strong problem-solving capabilities, their application in K-12 education is limited by the hierarchical nature of school curricula, which pre-trained models often fail to capture. This study proposes a Contextualized Content Generation Framework (CCGF) that integrates Retrieval-Augmented Generation (RAG) and a Multi-Agent System (MAS) to enhance contextual relevance and reliability in English as a Foreign Language (EFL) content creation. The framework assigns three agents’ distinct roles: knowledge localization, question generation, and result verification. A CCGF-based prototype was used to generate middle school EFL exercises and evaluated by 18 teachers through a blind evaluation of 60 questions from GPTs, manual sources, and CCGF. Results indicated that CCGF outperformed GPTs and was comparable to teacher-crafted items. Further validation with 139 students using Item Response Theory showed no significant differences between CCGF-generated and human-made assessments. These findings support the framework’s validity and usability in real classroom settings and suggest its potential to enhance generative AI applications in interactive K-12 learning environments.

This paper provides a comprehensive review of the applications of Generative AI in DevOps, analyzing recent advancements, methodologies, and challenges. We examine key contributions from the literature and discuss the future trajectory of AI-driven automation in DevOps workflows. As Software development continues to evolve, and the demand for automation tools that can learn and adapt on their own grows. Generative AI has revolutionized various industries, including DevOps, by enabling the creation of new content, from text and images to music and code. In this paper, we explore the impact of generative AI in DevOps, its applications, and the future prospects. This paper reviews the emerging trends and applications of Generative AI in DevOps, examining its impact on automation, CI/CD pipelines, Kubernetes management, and overall efficiency. We explore the concept of "GenOps" and the use of containers for deploying AI applications, highlighting key research and developments in this rapidly evolving field. We explore the transformative impact of AI agents, containerization, and automation tools on software development and operations. The review covers various aspects, including AI-driven code generation, infrastructure management, and continuous delivery pipelines, highlighting the potential of Generative AI to enhance efficiency and productivity in modern DevOps environments. We review recent advancements, tools, and methodologies that leverage Generative AI to optimize DevOps pipelines and vice versa. The paper also discusses challenges and future directions for integrating Generative AI into DevOps workflows.

Generative AI (GenAI) tools are transforming health professions education, offering opportunities to enhance faculty development (FD). Faculty developers are uniquely positioned to integrate GenAI into practice to address resource constraints, improve accessibility, and foster equity across diverse educational contexts. This Applied Insights article offers a perspective on how GenAI can be leveraged as a co-developer in FD by drawing on emerging literature and discussion points from a workshop at the 8th International Faculty Development Conference in the Health Professions. The applied insights are structured around key phases of FD: planning, content creation, delivery, and evaluation. They include actionable strategies for using GenAI in needs assessment, multilingual and culturally relevant resource creation, personalized learning plans, and when providing feedback and mentorship. Each insight is rooted in pedagogical rationale, evidence, and strategies to address ethical and practical challenges, with an emphasis on human oversight, contextual relevance, and continuous evaluation of GenAI's impact. By considering these insights, faculty developers can harness GenAI to co-design educational materials, extend their reach through innovative formats, and maintain ethical and equity-driven educational practices. This article highlights the transformative potential of GenAI in FD when thoughtfully integrated. GenAI can empower faculty developers to enhance the quality and inclusivity of HPE while safeguarding educational standards.

This study aims to examine how to integrate generative AI (GenAI) into marketing education. We used the transformation mechanism within boundary crossing theory to explore how marketing professional insights can be utilized to prepare students for industry demands in the GenAI era. We analyze industry content and GenAI courses alongside 26 interviews with industry practitioners to identify essential knowledge, skillsets, and optimal strategies for implementing GenAI in marketing curricula. Findings underscore the necessity of equipping students with GenAI skills for marketing research, strategy development, content creation, creativity, and ideation across use cases. Practitioners emphasized that marketing theory and ethics should be centralized in any GenAI-related subject matter. For educators, the study highlights the importance of involving industry partners, integrating external materials, and offering master classes to ensure students develop practical skills alongside theoretical knowledge. This research contributes to the discourse on GenAI in marketing education by providing use-cases and actionable insights into subject design, ensuring alignment with industry expectations and equipping students with necessary competencies for a GenAI-driven marketing environment. We extend the application of Boundary Crossing theory into marketing education literature by theorizing how transformation deepens and operates bidirectionally in the context of disruptive technologies, such as GenAI.

Generative AI, including large language models (LLMs), has transformed the paradigm of data generation and creative content, but this progress raises critical privacy concerns, especially when models are trained on sensitive data. This review provides a comprehensive overview of privacy-preserving techniques aimed at safeguarding data privacy in generative AI, such as differential privacy (DP), federated learning (FL), homomorphic encryption (HE), and secure multi-party computation (SMPC). These techniques mitigate risks like model inversion, data leakage, and membership inference attacks, which are particularly relevant to LLMs. Additionally, the review explores emerging solutions, including privacy-enhancing technologies and post-quantum cryptography, as future directions for enhancing privacy in generative AI systems. Recognizing that achieving absolute privacy is mathematically impossible, the review emphasizes the necessity of aligning technical safeguards with legal and regulatory frameworks to ensure compliance with data protection laws. By discussing the ethical and legal implications of privacy risks in generative AI, the review underscores the need for a balanced approach that considers performance, scalability, and privacy preservation. The findings highlight the need for ongoing research and innovation to develop privacy-preserving techniques that keep pace with the scaling of generative AI, especially in large language models, while adhering to regulatory and ethical standards.

The study examines the transformative potential impact of Generative AI (GAI) on society, media, and media education, focusing on the challenges and opportunities these advancements bring. GAI technologies, particularly large language models (LLMs) like GPT-4, are revolutionizing content creation, platforms, and interaction within the media landscape. This radical shift is generating both innovative educational methodologies and challenges in maintaining academic integrity and the quality of learning. The study aims to provide a comprehensive understanding of how GAI impacts media education by reshaping the content and traditional practices of media-related higher education. The research delves into three main questions: the nature of GAI as an innovation, its effect on media research and knowledge acquisition, and its implications for media education. It introduces critical concepts such as radical uncertainty, which refers to the unpredictable outcomes and impacts of GAI, making traditional forecasting and planning challenging. The paper utilizes McLuhan’s tetrad to analyze GAI’s role in media, questioning what it enhances or obsoletes, retrieves, or reverses when pushed to extremes. This theoretical approach helps in understanding the multifaceted influence of GAI on media practices and education. Overall, the research underscores the dual-edged nature of GAI in media education, where it presents significant enhancements in learning and content creation while simultaneously posing risks related to misinformation, academic integrity, and the dilution of human-centered educational practices. The study calls for a balanced approach to integrating GAI in media education, advocating for preparedness against its potential drawbacks while leveraging its capabilities to revolutionize educational paradigms.

The significance of ongoing study and development of AI technology is emphasized as this chapter explores the part and utility of generative AI in medical education and training, examines the difficulties it encounters, and systems unborn development patterns in the medical field. We can have a better understanding of how generative AI is impacting medical education going forward and offering fresh styles for training healthcare workers by reading this thorough review. A branch of artificial intelligence called” generative AI” is concerned with creating systems that can produce original and cultural labors, including textbooks, music, plates, and more. These systems may produce content that mimics mortal-generated content on their own by exercising deep literacy ways, particularly generative models. The interesting field of generative artificial intelligence focuses on creating systems that can singly produce original, creative content. It makes it possible for machines to perform creative and imaginative tasks in addition to further conventional bones. By exercising generative models and deep literacy approaches, these systems may induce innovative labors that nearly mimic mortal-generated content, including literature, music, prints, and more. This system makes it possible to produce creative and original content, making it an effective tool for various uses. Generative models are central to the idea of generative AI. Generative AI enables machines to autonomously induce creative content, similar as images, music, textbooks, and more. This addresses the need for new and different content in colorful disciplines, including art, entertainment, design, and marketing. Generative AI opens new possibilities for creative expression and expands the boundaries of mortal imagination. The possibilities for creative expression are increased, and the limits of mortal imagination are pushed by generative AI. Medical training serves as a means of guaranteeing that the performance of the mortal pool is observed in a realistic and secure setting. Their use of generative AI to produce virtual cases to instruct medical scholars. These realistic clinical scenarios in the simulations were designed to help medical professionals and students make better diagnoses and treatment-related decisions.

The discussion of AI copyright infringement or fair use often skips over all the required steps of the infringement analysis in order to focus on the most intriguing question, “Could a visual generative AI generate a work that potentially infringes a preexisting copyrighted work?” and then the discussion skips further ahead to, “Would the AI have a fair use defense, most likely under the transformative test?” These are relevant questions, but without considering the actual steps of the copyright infringement analysis, the discussion is misleading or even irrelevant. This neglecting of topics and stages of the infringement analysis fails to direct our attention to a properly accused party or entity whose actions prompt the question. Making a sudden transition from a question of infringement in the creation of training datasets to the creation of foundation models that draw from the training data to the actual operation of the generative AI system to produce images makes a false equivalency regarding the processes themselves and the persons responsible for them. The questions ought to shift focus from the persons compiling the training dataset used to train the AI system and the designers and creators of the AI system itself to the end users of the AI system who conceive of and cause the creation of images. The analysis of infringement or fair use in the generative AI context has suffered from widespread misunderstanding concerning the generative AI processes and the control and authorship of the end-user. Claimants, commentators, and regulators have made incorrect assumptions and inaccurate simplifications concerning the process, which I refer to as the Magic File Drawer theory, the Magic Copy Machine theory, and the Magic Box Artist theory. These theories, if they were true, would be much easier to envision and understand than the actual science and technology that goes into the creation and operation of a contemporary visual generative AI system. Throughout this Article, I will attempt to clarify and correct the understanding of the science and technology of the generative AI processes and explain the different roles of the training dataset designers, the generative AI system designers, and the end-users in the rendering of visual works by a generative AI system. Part II will discuss the requirements of a claim of copyright infringement including each step from the copyrightability of the claimant’s work, the doctrines that limit copyrightability, the requirement of an act of copying, and the infringement elements. Part III will summarize the copyright fair use test paying particular attention to the purpose and character of the use analysis, 17 U.S.C. § 107(1), and the current interpretation of the “transformative” test after Andy Warhol Foundation v. Goldsmith, particularly in circumstances relating to technology and the use of copyrighted or copyrightable data sources. Part IV will analyze potential infringement or fair use by the creators of generative AI training datasets. Part V will analyze potential infringement or fair use by the creators of visual generative AI systems. Part VI will analyze potential infringement or fair use by the end-users of visual generative AI systems. For all their complexity, visual generative AI systems are tools that depend on an end-user who conceives of and designs the image and provides the system with a prompt to set the generative process in motion. The end-users are responsible for crafting the prompt or series of prompts used, for evaluating the outputs of the generative AI, for adjusting and editing the iterations of images offered by the AI system, and ultimately for selecting and adopting one of the images generated by the AI as the final image. The end-users then make further decisions about the actual use and its function and purpose for the images the end-users selected and adopted from the outputs of the AI. While working with the AI tool to try to produce a certain image, an end-user might steer the system to produce a work that could, under an infringement analysis, be regarded as potentially infringing, which would lead us again to the fair use analysis based on the end-user’s use of the image.

Context-driven ad placement with Generative AI represents a paradigm shift in advertising strategy for streaming platforms, moving beyond traditional demographic targeting to incorporate real-time content analysis, viewer circumstances, and environmental factors. This technical review examines how the integration of contextual analysis frameworks with generative capabilities creates a more organic advertising experience that benefits all stakeholders in the streaming ecosystem. The multi-dimensional approach processes content context through sophisticated computational models, integrates viewer preferences through behavioral indicators, and assesses environmental factors to optimize delivery parameters. Generative AI transforms both content creation—through multimodal generation, style transfer, and dynamic personalization—and placement optimization via predictive modeling, timing calculations, and format adaptation algorithms. Despite impressive performance improvements in attention retention, brand recall, conversion rates, and revenue metrics, implementation challenges remain, including computational resource requirements, integration complexity, and privacy compliance concerns. Emerging solutions such as federated learning, quantum computing applications, emotional response integration, and cross-platform contextual continuity offer promising directions for addressing these limitations while expanding capabilities.

In the digital age, generative AI significantly influences various industries, especially education. It merges with traditional teaching methods, promising a new era of educational possibilities. This chapter delves into generative AI's impact on content creation and curriculum design, discussing its evolution and benefits like producing diverse, scalable educational materials and adaptive curricula personalized for learners. Real-world examples and case studies underscore its practical impact. Nonetheless, the chapter addresses ethical and pedagogical challenges and the complexity of integrating this technology. It also speculates on generative AI's future interactions with emerging technologies and its broader effects on education systems. Targeting educators, policymakers, and edtech enthusiasts, the chapter serves as a guide and insight provider for navigating this evolving landscape responsibly.