ПОРІВНЯННЯ РІЗНИХ ВЕРСІЙ ДОДАТКУ Midjourney ТА ЙОГО ВИКОРИСТАННЯ В РІЗНИХ СФЕРАХ СТВОРЕННЯ ВІЗУАЛЬНОГО КОНТЕНТУ

In the context of the modern information war, the development of effective methods to counter disinformation and hostile propaganda has become critically important. The amount of false information and the speed of its dissemination necessitated the implementation of automated systems involving artificial intelligence to optimise the processes of creating visual counter-propaganda content. This research aimed to develop a methodology for the integration of artificial intelligence technologies into the processes of creating effective visual tools for countering disinformation, taking into account the principles of graphic design and the psychology of visual information perception. The research was based on a comprehensive approach that combined theoretical analysis of scientific literature, comparative analysis of the neural networks Midjourney, Stable Diffusion, and DALL-E, semiotic analysis of visual materials, as well as experimental implementation of the developed system for the automated creation of counter-propaganda visual content. A comprehensive approach has been developed for the creation of visual tools to counter disinformation, which combines the capabilities of automated information collection systems, artificial intelligence algorithms for generating graphic content, and the principles of effective graphic design. It has been found that the use of artificial intelligence in graphic design has optimised up to 20% of routine tasks in the creation of visual content, allowing designers to focus on the strategic and creative aspects of development. The developed recommendations for the use of artificial intelligence in graphic design may be implemented by state institutions, media, and public organisations to respond promptly to information threats

This article is part of a larger study of design as a cultural phenomenon. In this part of the study, the author examines the process that is currently taking place in the semiotic structure of design, associated with the active introduction of neural networks into the creation of visual content. Artificial intelligence products stylistically take the design away from the fourth-order simulacrum (from the flat design style) and return the design to using the third-order simulacrum as the main iconic form.The object of the research is the transformation of the semiotic design system. The subject of the study is a return to the third–order simulacrum in modern design. The purpose of the study is to show and try to explain how in modern design there is a return to the use of a third-order simulacrum. The research method is a semiotic analysis of modern design based on the methodology of R. Barth's semiotic analysis. The study is also based on J. Baudrillard's theory of semiosis in hyperreality and three orders of simulacra. The author sees the philosophical justification of the art of neural networks in the concept of flat ontologies. The study of the semiotic structure of design allows us to see that the logic of design development carries this phenomenon through successive stages of semiosis associated with a decrease in meaning and a diminution of being. In its development, the design consistently uses first index signs, a second-order simulacrum, then a third-order simulacrum as the main sign form. Nowadays, the main iconic form in design has become a fourth-order simulacrum. Next, design had to either end as a profession and phenomenon, or move into a new cultural paradigm that was not related to simulation. However, unexpected transformations have begun to occur in design, due to the active involvement of non–human agents - neural networks – in the creation of visual content. Neural network products are a typical example of a third-order simulacrum. Thanks to the use of neural networks, modern design finally acquires the vector of transhumanism and closes in simulation.

ABSTRACTWith the advancement of artificial intelligence (AI) content generation tools, companies are increasingly utilizing AI to create visual marketing content. However, research on how consumer responses to AI‐generated content differ from those to human‐generated content remains limited. Based on the dual‐process theory, this research examines the impact of three types of visual content creators—AI creators, human‐AI co‐creators, and human creators—on consumer responses. An online experiment involving 450 consumers evaluated their reactions to visual marketing content labeled with different creator information. The findings indicate that content generated by AI creators elicits greater emotional arousal among consumers compared to content generated by human‐AI co‐creators or exclusively by human creators. Moreover, content generated by AI or co‐created by humans and AI is perceived as requiring less effort from companies, yet it conveys a greater perception of company innovation ability than content created solely by humans. These types of content creators indirectly influence consumer attitudes toward the content and their perceptions of product premiumness. This effect is mediated by emotional arousal, perceived company effort, and perceived company innovation ability. As one of the early studies exploring the effects of AI‐generated visual content in marketing, this research offers novel insights into the psychological mechanisms underlying consumer responses—based on evaluations of both the content and the company's strategic approach.

The study explored the educational potential of the application of student-generated digital visual content for learning English as a second language (ESL) by undergraduate students enrolled in the course Foreign Language which is actually Introduction to Legal English. This study used a mixed-methods approach. The researchers designed a quasi-experimental design to examine whether the students’ creation of visual content, supported by structured use of artificial intelligence (AI), could improve second language learning outcomes, increase motivation, and promote critical engagement with digital tools. The experimental group was tasked with creating personalized visual learning materials. The applied approach was structured in several steps, from creating simple forms including infographics and comparative charts to poster presentations and digital video passion projects. The algorithm for collaboration with AI and the work with specific features of AI-generated materials was applied aimed at making a student a critical consumer of this content and mitigating potential drawbacks of using AI. To assess the learning outcomes after the intervention, the post-test was administered, which revealed that the studied instructional design had a positive impact on language development across all aspects checked. The questionnaire, which included both open-ended and closed-ended questions, investigated students’ perceptions of the applied methodology and faced challenges. The findings showed that students perceived integrating visual creation and structured AI-supported activities into English language learning as beneficial for language skills development, boosting motivation and interest, and the advancement of digital literacy.

The study explored the educational potential of the application of student-generated digital visual content for learning English as a second language (ESL) by undergraduate students enrolled in the course Foreign Language which is actually Introduction to Legal English. This study used a mixed-methods approach. The researchers designed a quasi-experimental design to examine whether the students’ creation of visual content, supported by structured use of artificial intelligence (AI), could improve second language learning outcomes, increase motivation, and promote critical engagement with digital tools. The experimental group was tasked with creating personalized visual learning materials. The applied approach was structured in several steps, from creating simple forms including infographics and comparative charts to poster presentations and digital video passion projects. The algorithm for collaboration with AI and the work with specific features of AI-generated materials was applied aimed at making a student a critical consumer of this content and mitigating potential drawbacks of using AI. To assess the learning outcomes after the intervention, the post-test was administered, which revealed that the studied instructional design had a positive impact on language development across all aspects checked. The questionnaire, which included both open-ended and closed-ended questions, investigated students’ perceptions of the applied methodology and faced challenges. The findings showed that students perceived integrating visual creation and structured AI-supported activities into English language learning as beneficial for language skills development, boosting motivation and interest, and the advancement of digital literacy.

This study investigates how visual content influences marketing effectiveness on LinkedIn, focusing on its role in enhancing engagement, improving brand perception, and amplifying message clarity. As LinkedIn evolves from a professional networking site to a comprehensive platform for B2B communication and digital branding, organizations are increasingly leveraging it to share updates, build authority, and foster professional relationships. Visual content—such as infographics, short videos, graphics, carousels, and animations—has emerged as a key tool in capturing user attention and improving the delivery of complex information. The research combines both primary and secondary data sources. A survey was conducted among professionals from sectors including IT, marketing, education, and human resources, aiming to understand how visual elements affect content preference, engagement behaviour, and memory retention. Findings suggest that visual content significantly outperforms text-based posts in driving interactions such as likes, comments, shares, and click-through rates. Infographics are particularly effective for simplifying data, while short-form videos are favored for their ability to communicate brand value quickly and persuasively. Supporting secondary literature and LinkedIn’s algorithmic patterns confirm that posts enriched with visual media tend to receive broader organic reach. Studies also highlight that visual storytelling increases brand recall and strengthens trust and credibility. Furthermore, neuroscience supports the idea that visuals are processed faster than text, making them a powerful asset for marketers aiming to deliver impactful messages in a short time. The paper also explores strategic applications of visual content on LinkedIn, recommending the use of consistent branding, high-quality design, and goal-oriented visuals tailored to specific marketing objectives—such as employer branding, lead generation, or thought leadership. It encourages investment in design resources and training to maximize the effectiveness of visual marketing. However, the study recognizes certain limitations, including a geographically narrow sample (primarily Indian professionals), a relatively small data pool, and reliance on self-reported user behaviour. The absence of experimental methods like A/B testing also limits the ability to establish direct causation. In conclusion, the research calls for future studies on the integration of emerging technologies like AI in visual content creation, the development of platform-specific visual strategies, and the importance of accessibility and inclusivity in design. As digital engagement continues to shift towards visual-first experiences, LinkedIn marketers must adapt to stay competitive and relevant in the professional content ecosystem. Keywords: Visual Content, LinkedIn Marketing, User Engagement, Brand Visibility, Content Strategy, Infographics, Short-form Videos, B2B Communication, Professional Networking, Social Media Marketing, Brand Recall, Visual Storytelling, Digital Branding, Click-through Rate, Organic Reach, Marketing Analytics, Content Performance, Thought Leadership, Employer Branding, Visual Design

Despite the ubiquity of direct manipulation techniques available in computer-aided design applications, creating digital content remains a tedious and indirect task. This is because applications require users to perform numerous low-level editing operations rather than allowing them to directly indicate high-level design goals. Yet, the creation of graphic content, such as videos, animations, and presentations often begins with a description of design goals in natural language, such as screenplays, scripts, outlines. Therefore, there is an opportunity for language-oriented authoring, i.e., leveraging the information found in the structure of a language to facilitate the creation of graphic content. We present a systematic exploration of the identification, graphic description, and interaction with various linguistic structures to assist in the creation of visual content. The prototype system, Crosspower, and its proposed interaction techniques, enables content creators to indicate and customize their desired visual content in a flexible and direct manner.

In recent years, the field of artificial intelligence has witnessed significant advancements in generating and manipulating visual content. One prominent area of development within this domain is AI image generators, These generators hold immense potential across diverse applications, revolutionizing industries such as advertising, entertainment, and digital art creation. As artificial intelligence continues to evolve, its ability to generate and manipulate images has reached remarkable levels of sophistication. However, alongside their impressive capabilities, AI image generators are also prone to making intriguing and sometimes puzzling mistakes. These errors can range from minor imperfections to profound distortions, raising important questions about the limitations and ethical implications of AI in visual content creation. Understanding these mistakes not only sheds light on the underlying algorithms and processes but also prompts critical reflections on the broader implications of relying on AI for creative and practical applications. This research aims to explore and analyze the nature, causes, and consequences of mistakes made by AI image generators, offering insights into their development, usability, societal impact, and how can ideas be proposed to support artificial intelligence thinking to extract historically correct images through an open source of information for experts everywhere, regardless of their specializations.

The purpose of the article is to determine the possibilities and limitations of managing the global financial system on the basis of artificial intelligence. For determining the reasons of the recent global financial crisis and for determining the opportunities and limitations of overcoming the crisis with the help of modernization of this system’s management with the help of artificial intelligence, the authors use the method of SWOT analysis. For describing the essence of the process of managing the global financial system on the basis of artificial intelligence, the method of modeling of economic systems and the method of formalization are used. As a result, the authors come to the conclusion that the working hypothesis is correct – artificial intelligence does possess large potential in the sphere of stimulation of anti-crisis development of the global financial system. Allowing overcoming the influence of “human factor” on development of this system, which was one of the main reasons of its recent crisis, artificial intelligence can ensure stable functioning, high effectiveness, and sustainable growth of the global financial system. Despite the wide possibilities for optimization of management of the global financial system on the basis of artificial intellect, including rationalization of decision making, programming of social responsibility of economic subjects, expansion of opportunities in the sphere of analysis and forecasting of long-term consequences of their actions and market events, and elimination of dependence of financial markets on the influence of social tendencies, this process is peculiar for certain limitations. In order to overcome them, the authors offer a conceptual model of managing the global financial system on the basis of artificial intelligence.

There is a widening gap between the creation of visual content and its analysis and interpretation by machine, an increasingly essential require-ment for correct indexing and filtering. In the case of the WWW, for instance, although there are efficient methods to process the encoded (e.g. ASCII) text, there are no such methods for the (significant) visual content. This paper focuses on the methods developed by the authors to address the problem of extracting the characters from WWW images containing text.

Abstract. The article is devoted to considering modern neural network tools that allow for speeding up the development of web interfaces and simplifying the work of UI/UX designers. One of the main problems of modern design is quick access to general information and possible structuring of a site with specialized content, as well as obtaining its visual content. Currently, neural networks cannot replace designers, but to a large extent help them solve tasks. All neural networks that can be used in the design of web interfaces can be divided into four main types: convolutional, recurrent, forward propagation, and generative adversarial networks. In his work, the designer can mainly use generative networks, they can be classified according to the principle of "information at the input - information at the output". When working on a project, the designer can create a request to the neural network and get several options, generate different ideas, and create mood boards based on them, selecting colors, gradients, texture, typography, etc. The neural network can create various graphic elements: icons, buttons, illustrations, and photos with the right perspective, style, and colors. Using neural networks to improve images and refine or remove necessary elements is also promising. The process of speeding up the creation of the landing page interface using the Midjourney application is considered. Examples of writing prompts (prompts) that will affect the final quality of the generated image are given. The results are high-quality visual content that can either be placed in a project or used as an idea, element placement, composition, color scheme, photos, icons, etc. After creating the graphic design elements using Chat GPT 3.5, the landing page's content was created. You can use the FIG GPT plugin directly in the Figma environment to quickly generate the required content. Existing shortcomings and generation inaccuracies that arise in the work can be corrected by quickly updating and creating new versions of neural networks.

Artificial Intelligence (AI) has significantly transformed the landscape of digital media by enhancing the processes of visual content creation and media management. AI-driven technologies such as machine learning, computer vision, and deep learning enable the automated generation, editing, and optimization of visual media including images, videos, and graphics. Digital media platforms increasingly rely on AI tools to improve production efficiency, personalize content, and enhance audience engagement. AI-based systems can analyse user preferences, generate creative visuals, and automate repetitive production tasks, allowing media professionals to focus on strategic and creative aspects of content development. Despite these advantages, the integration of AI in visual content creation also raises concerns related to ethical issues, originality, algorithmic bias, and the potential impact on creative employment. This study explores the role of AI-driven technologies in visual content creation across digital media platforms and examines their influence on media production, content management, and audience engagement. The findings highlight that AI has the potential to revolutionize digital media workflows by improving productivity, creativity, and datadriven decision-making in modern media organizations.

The problem and the aim of the study. Digital literacy of the teacher implies that a specialist has a formed system of basic knowledge, skills and attitudes in the field of using digital technologies to search for information, prepare didactic materials, design electronic resources, and personalize learning. Interactive services, gamification tools, neural networks, etc. are actively used to ensure appropriate training and develop the necessary personal qualities of teaching staff in additional professional education. The aim of the study is to identify the features of the development of digital literacy of teachers when designing plots for text games using neural network tools in the context of additional professional education. Research methods. Indicators of digital literacy of the teacher: information, computer, communication, media literacy and attitude to technology. The experiment was conducted in the Institute for Education Development of the Kirov Region (Russian Federation). The study involved 350 participants. Software: dialogue simulators, neural networks for text generation (NiceBot, NeuroTexter); web applications (Porfiryevich, GamioAI, Twine). The digital literacy level was assessed using the questionnaire materials of the National Agency for Financial Research and the questions of the author's test. In statistical data processing, the Pearson's chi-squared test was used. KEYWORDS Results. The article describes the system of work with students of the experimental group on designing plots of text games using neural network tools within the framework of the course "Digital educational environment: new competences of the teacher". The peculiarity of the proposed interaction option is the transition from practical activities with dialogue simulators which simulate automatic text generation to writing plots using game applications based on artificial intelligence. Statistically significant differences in the qualitative changes that occurred in the pedagogical system were revealed (χ2 = 8.204, p < 0.05). Conclusion. The features of the activity that provide additional conditions for the development of digital literacy of teachers are formulated: new experience in the use of neural networks and gamification services, the use of digital tools for modeling an original game space, changing the roles of participants in information interaction, an adaptive development process, etc.

The article examines the possibilities of various neural networks as tools for the implementation of projects in graphic design, evaluates their ability to ensure quality and efficiency in the creation of visual content for various types of products. The advantages and disadvantages of each neural network are also analyzed. The work presents the opinions of scientists and practitioners about the variety of neural networks that can be used to perform graphic design tasks. In addition, the results of own practical experience of working with neural networks are given. The study confirmed the effectiveness of neural networks in creating concepts of characters and locations for computer games, illustrations for printed and electronic publications, as well as in the development of trademarks and logos, corporate style and graphic design of packaging. However, their functionality does not yet provide the necessary quality level for such products as posters created on the basis of figurative language tropes; fonts; engineering graphics in axonometric projections showing the internal structure of devices or equipment; layout for print publications, websites and mobile applications, as well as infographics based on stylized images and design solutions for packaging. Maze Guru, Midjourney, and Leonardo AI are best for graphic design content. The ChatGPT neural network is an effective tool for matching peers and gathering feedback from scientists. The advantage of using neural networks is a significant acceleration of the process of creating visual content, as well as the possibility of combining different programs to supplement and improve the results obtained. Disadvantages include mainly English-language communication between the user and the network, as well as discrepancies between the images that exist in the user's mind and those generated by the network. Creations created by neural networks are easily recognizable, and for similar text queries, they can give very similar results.

With the advent of the era of big data, artificial intelligence has attracted continuous attention from all walks of life, and has been widely used in medical image analysis, molecular and material science, language recognition and other fields. As the basis of artificial intelligence, the research results of neural network are remarkable. However, due to the inherent defect that electrical signal is easily interfered and the processing speed is proportional to the energy loss, researchers have turned their attention to light, trying to build neural networks in the field of optics, making full use of the parallel processing ability of light to solve the problems of electronic neural networks. After continuous research and development, optical neural network has become the forefront of the world. Here, we mainly introduce the development of this field, summarize and compare some classical researches and algorithm theories, and look forward to the future of optical neural network.