Factors influencing the use of generative artificial intelligence-based systems among university students: evidence from the UAE

The publication provides an overview of the noveltiesof the Ukrainian legislation «On Copyright and Related Rights» (2023) regarding the peculiarities of protection of non-original objects generated by a computer program (generative artificial intelligence system) using sui generis law, in particular through the prism of exceptions and limitations in copyright.Ukraine has introduced one of the world's first models of the legal protection of AI output through a special sui generis legal regime. The author determines that the legislator sets forth three conditions for a non-original object generated by a computer program (generative artificial intelligence system) to be protected by law: The AI output must differ from existing similar objects; the AI output must be generated as a result of the operation of a computerprogram without the direct participation of an individual in the generation of this object; and the rights of third parties must be respected when generating a non-original object.It is established that the existing exceptions and restrictions in Ukrainian copyright (free making of copies of works to search for text and data included in or related to scientific publications for research purposes and temporary reproduction) will allow limited operation of generative artificial intelligence systems. However, it is necessary to consider that AI outputs do not unreasonably restrict the legitimate interests of the relevant copyright owners.In practice, the functioning of generative artificial intelligence systems demonstrates one of the general principles of civil law - freedom of contract through self-regulation of the distribution of intellectual property rights to AI outputs. The assumption is made that it is not necessary to establish the «default» attribution of rights to such objects at the legislative level.The author suggests that in the future, depending on the requirements of sustainable economic development and the balance of interests between society and the relevant subjects of law, the terms of the legal protection of rights to AI outputs may be changed.The author provides recommendations for improving this area's legal regulation and enhancing law enforcement's efficiency.

The article discusses the changing role of communicators due to the active development of generative programmatic and artificial intelligence design systems. It notes an increasing interest in creating design solutions in collaboration with machines through digital-generative and conceptual- generative approaches that require theoretical reflection. This can be fully realised, for example, through a semiotic-interactive methodology, which is understood as the combination of two basic components: semiotic and interactive. The semiotic component implies a unified semiotic format for the representation of communicative acts using semiotic figures, signs, and sign systems. The interactive form of communication involves programmatic-instructive “request- response” reactive interaction, as well as dialogic co-creation between humans and artificial intelligence generative design systems that can take into account the individual context of the design process. The article highlights the characteristics of the formation in the context of methodology: the role of the professional designer (focused on creating semiotic invariants-concepts for training the generative design system), the synthetic role (combining the sub-roles of the client, designer-editor, user, and consumer — creating individual design solutions in collaboration with the generative design system), the digital profile analyst (studying the needs of the synthetic role representative, thereby helping to generate more personalised design solutions), and the intelligent assistant role (the designer-generator, facilitating the interaction of communicators with the generative design system). The changing roles of communicators lead to the necessity to consider the design program as the primary means of expression, and to the development of methods for analysing (deconstructing) and synthesising (constructing) design programs. It demonstrates how the ongoing changes are influenced by the communicative model that includes the machine executor, as well as the instrumental tools and characteristics of the generative design environment/design system.

The Sri Lanka Journal of Bio-Medical Informatics (SLJBMI) is the official journal publication of the Health Informatics Society of Sri Lanka. It is the only academic publication in Sri Lanka which focuses on the emerging field of health informatics. SLJBMI was launched in 2010 as an open access and peer reviewed e-journal. It was temporarily paused publishing since June 2015 and relaunched in 2019 with an improved reader friendly format .The mission of SLJBMI is to publish high-quality original research and other relevant publications that would contribute to the promotion of digital health and health informatics. It aspires to be the most prominent publishing spotlight related to health informatics in the region and to be the gathering platform of the renowned experts and enthusiasts worldwide.

The issue of artificial intelligence (‘AI’) in the context of intellectual property law, including copyright law, has attracted continued interest. Progressive innovation brings new challenges, and the advances we have seen in recent years - particularly in the development of generative artificial intelligence (‘GenAI’) systems – are attracting media and public attention. The adoption and use of generative artificial intelligence systems has sparked widespread debate about their relevance to the copyright system. In the wake of emerging questions, copyright holders have begun to file copyright infringement lawsuits against artificial intelligence companies targeting the process of training artificial intelligence with the results obtained from generative artificial intelligence systems. As a result of these questions, copyright holders have begun filing copyright infringement lawsuits against owners of programs trained on the basis of data protected by copyright and data protection law. Drawing on analysed discussions, normative proposals, consultations and recommendations from experienced practitioners, this article identifies one of the broad questions of contemporary copyright policy towards artificial intelligence, concerning the legality of using copyrighted works to train artificial intelligence models. It also poses the question of the desirability of establishing a new system of copyright exceptions and limitations dedicated to artificial intelligence systems, while analysing the impact of existing limitations under copyright exceptions and limitations on the development of artificial intelligence.

Currently, there are various artificial intelligence tools, such as natural language processors, which are used to enhance teaching and learning processes. In this context, the use of virtual assistants is becoming increasingly common in higher education institutions. This study addresses the design, implementation, and evaluation of a chatbot designed to improve inclusive learning in university students. This artificial intelligence-based system was implemented with undergraduate and postgraduate students in the field of education at Universidad Europea de Madrid. To achieve this, an exploratory and descriptive study with a mixed-methods approach was conducted. A self-perception questionnaire was used as a research instrument to evaluate usability, accuracy, interaction, utility, and satisfaction criteria, along with gathering qualitative feedback. The results revealed highly positive student evaluations of the chatbot, consistently surpassing average ratings of 4.2 out of 5. Furthermore, the correlation between satisfaction with the chatbot and other questions regarding usability, accuracy, interaction, and utility showed direct and significant correlations in all cases. In conclusion, this virtual and conversational assistant proves to be a valuable practice in future chatbot designs for enhancing inclusive learning based on Universal Design for Learning.

The possibility of neuro-linguistic textual identification of intelligent systems (IS) and artificial intelligence (AI) systems is investigated. To set the task, intelligent systems were assigned a task in two languages, and artificial intelligence systems were assigned in four languages, using AI systems of different generations. As part of the study, a specialized software package was used to evaluate information parameters, as well as an information analyzer designed for neuro-linguistic identification of texts. The results make it possible to use information characteristics as parameters of neuro-linguistic identification of artificial intelligence (AI) and intelligent systems (IS) systems. The results of the study showed that during the transition from one system to another, the parameters of neuro-linguistic text identification change both in the study of intelligent systems and in the study of artificial intelligence systems. In the study of AI systems, the parameters of neuro-linguistic text identification change when switching from one language to another in one neural network, when changing neural networks while maintaining the same language. In the study of intelligent systems, it was revealed that the parameters change during the transition from one language to another, when changing the intelligent system while maintaining one language. This study makes it possible to use information characteristics as parameters of neuro-linguistic textual identification of artificial intelligence systems and intelligent systems.

Good communication can enable better diagnosis, increases patient compliance with treatment recommendations, reduce the medical errors committed by personnel, and stirs positive mood and satisfaction in patients. However multilingual capabilities of AI can facilitate translations into local languages, making healthcare information accessible to a broader patient population. The study investigated the extent of awareness and utilization of artificial intelligence-based systems in biomedical translation amongst health professionals in medical tertiary institutions in Bayelsa State. Descriptive survey design was adopted for this study. Two research questions were raised to guide the study. The population of the study comprised all three hundred and forty-three professionals in the three medical tertiary institutions in Bayelsa State. A sample of 299 respondents were drawn from the population using systematic random sampling technique. The instrument for data collection was a “Awareness and Utilization of Translation APP Scale (AUTAS)” developed by the researcher and validated by experts. The reliability co-efficient of 0.82 was obtained using Cronbach Alpha formula which was considered appropriate for this study. The research questions were answered using mean and standard deviation. The findings revealed that the level of awareness and the extent of utilization of artificial intelligence-based systems in biomedical translation amongst health professionals in medical tertiary institutions in Bayelsa State is low. It was recommended among others that medical professionals should be effectively exposed to artificial intelligence-based systems in biomedical translation as this will enhance sustainability medical practice in Nigeria.

Many artificial intelligence technologies are used in health sciences. Artificial intelligence-based clinical decision support systems are computer-aided systems designed to help healthcare professionals make decisions on various issues. With clinical decision support systems, it provides support to physicians in many areas with diagnosis, treatment, drug prescribing, drug interactions, dose adjustment, warning systems, as well as storing existing data, recalling them when necessary, establishing new relationships among data. By analyzing patient-specific medical data with artificial intelligence-based clinical decision support systems, it supports the healthcare professionals in making rational decisions in the healthcare process. In this review, conceptual information such as the definition, structure and classification of clinical decision support systems, as well as the advantages and disadvantages of these systems, the methods used in artificial intelligence-based systems, and their types are included. The most accurate use of limited professional manpower and limited resources is promising in terms of sustainable health care with patient-oriented, user-friendly artificial intelligence systems solutions.

The current study sought to determine how well university students' EFL listening comprehension skills may be developed by artificial intelligence (AI) technologies. One hundred students participated in the study, split into two groups: the control group (N = 50), which received traditional education, and the experimental group (N = 50), which received instruction using artificial intelligence systems. The study's instruments included an EFL listening comprehension skills checklist to determine which listening skills are most important for first-year college students to acquire. a pre-post listening skills test to measure students' listening abilities before and after using the chatbot and Duoling AI applications and a correction rubric . A statistical analysis was conducted to confirm the study's hypotheses. Findings of the study revealed that the experimental group students' EFL listening skills were enhanced as a result of using the Artificial Intelligence (chatbot and Duoling).

Objective: this article aims to answer the following questions: 1. Can generative artificial intelligence be a subject of copyright law? 2. What risks the unregulated use of generative artificial intelligence systems can cause? 3. What legal gaps should be filled in to minimize such risks?Methods: comparative legal analysis, sociological method, concrete sociological method, quantitative data analysis, qualitative data analysis, statistical analysis, case study, induction, deduction.Results: the authors identified several risks of the unregulated usage of generative artificial intelligence in the creative industry, among which are: violation of copyright and labor law, violation of consumers rights and the rise of public distrust in government. They suggest that a prompt development of new legal norms can minimize these risks. In conclusion, the article constants that states have already begun to realize that the negative impact of generative artificial intelligence on the creative industry must not be ignored, hence the development of similar legal regulations in states with completely different regimes.Scientific novelty: the article provides a comprehensive study of the impact of generative artificial intelligence on the creative industry from two perspectives: the perspective of law and the perspective of the industry. The empirical basis of it consists of two international surveys and an expert opinion of a representative of the industry. This approach allowed the authors to improve the objectivity of their research and to obtain results that can be used for finding a practical solution for the identified risks. The problem of the ongoing development and popularization of generative artificial intelligence systems goes beyond the question “who is the author?” therefore, it needs to be solved by introduction of other than the already existing mechanisms and regulations - this point of view is supported not only by the results of the surveys but also by the analysis of current lawsuits against developers of generative artificial intelligence systems.Practical significance: the obtained results can be used to fasten the development of universal legal rules, regulations, instruments and standards, the current lack of which poses a threat not only to human rights, but also to several sectors within the creative industry and beyond.

Using generative artificial intelligence systems in the classroom for law case analysis teaching can enhance the efficiency and accuracy of knowledge delivery. They can create interactive learning environments that are appropriate, immersive, integrated, and evocative, guiding students to conduct case analysis from interdisciplinary and cross-cultural perspectives. This teaching method not only increases students’ interest and participation in learning but also helps cultivate their interdisciplinary thinking and global vision. However, the application of generative artificial intelligence systems in legal education also faces some challenges and issues. If students excessively rely on these systems, their ability to think independently, make judgments, and innovate may be weakened, leading to over-trust in machines and reinforcement of value biases. To address these challenges and issues, legal education should focus more on cultivating students’ questioning skills, self-analysis abilities, critical thinking, basic legal literacy, digital skills, and humanistic spirit. This will enable students to respond to the challenges brought by generative artificial intelligence and ensure their comprehensive development in the new era.

Editorial: Continuing on an Inclusive Path to Scholarly Excellence with Renewed Vigor

Substantial research over the last ten years has indicated that many generative artificial intelligence systems (“GAI”) have the potential to produce biased results, particularly with respect to gender. This potential for bias has grown progressively more important in recent years as GAI has become increasingly integrated in multiple critical sectors, such as healthcare, consumer lending, and employment. While much of the study of gender bias in popular GAI systems is focused on text-based GAI such as OpenAI’s ChatGPT and Google’s Gemini (formerly Bard), this article describes the results of a confirmatory experiment of gender bias in visual GAI systems. The authors argue that the potential for gender bias in visual GAI systems is potentially more troubling than bias in textual GAI because of the superior memorability of images and the capacity for emotional communication that images represent. They go on to offer four potential approaches to gender bias in visual GAI based on the roles visual GAI could play in modern society. The article concludes with a discussion of how dominant societal values could influence a choice between those four potential approaches to gender bias in visual GAI and some suggestions for further research.

Various advanced computational, cognitive and innovative approaches have been developed in the last three decades to advance the fields of artificial intelligence and knowledge-based design systems. This paper reviews the development of generative design systems in the literature and of those developed by the author. It presents a framework of a generative design system with several real design examples. Finally, the paper examines the future direction for the advancement of generative design. A generative design system supports the generation and exploration of a large number of alternative design solutions, using automatic transformation algorithms of AI nature. It maintains a consistent style of all the explored solutions, but with design features and variables. It can also include an evaluation mechanism in the generative process for the system to search for the potentially optimum design solutions.

The development of artificial intelligence generative systems (AIGS) in the modern world requires addressing issues related to the quality, stability, and efficiency of the generated content. In this context, resonance diagnostics become of paramount importance. The purpose of this study is to explore the possibilities of applying resonance diagnostics for detecting, analyzing, and resolving problems in artificial intelligence generative systems. To achieve the set goal, the following tasks were identified: analysis of the theoretical foundations of resonance diagnostics; investigation of the potential of using resonance signals to adjust AIGS learning parameters; studying the impact of resonance diagnostics on the stability and adaptation of AIGS to changing operating conditions. The study conducted an analysis of resonance diagnostics in the context of AIGS and revealed its powerful influence on addressing issues related to system quality and productivity. The research demonstrated that resonance diagnostics can be used to achieve realism, diversity, and quality of generated content. Additionally, it was determined that it can contribute to enhancing the stability and adaptation of systems to varying operational conditions