Background and Aim of Study: Implementing artificial intelligence (AI) in various areas of human activity is an avalanche-like process. This situation has raised questions about the feasibility and regulation of AI use that require justification, particularly in the context of scientific research. The aim of the study: to identify the extent to which AI-based chatbots can meet ethical standards when analysing academic publications, given their current level of development. Material and Methods: The present study employed various theoretical methods, including analysis, synthesis, comparison, and generalisation of experimental studies and published data, to evaluate ChatGPT’s capacity to adhere to fundamental ethical principles when analysing academic publications. Results: The present study characterised the possibilities of using AI for academic research and publication preparation. The paper analysed a case of text generation by ChatGPT and found that the information generated by the chatbot was falsified. This fact and other similar data described in publications indicate that ChatGPT has a policy to generate information on request at any cost. This completely disregards the reliability of such information, the copyright of its owners and the basic ethical standards for analysing academic publications established within the scientific community. Conclusions: It is becoming increasingly clear that AI and the various tools based on it will evolve rapidly and have qualities more and more similar to human intelligence. We believe the main danger lies in losing control of this AI development process. The rapid development of negative qualities in AI, such as selfishness, deceitfulness and aggressiveness, which were previously thought to be unique to humans, may in the future generate in AI the idea of achieving superiority over humans. In this context, lying and violating ethical standards when analysing academic publications seem like innocent, childish pranks at the early stages of AI development. The results are important in drawing the attention of developers, scientists, and the general public to the problems of AI and developing specific standards, norms, and rules for its use in various fields.

Background and Aim of Study: Artificial intelligence (AI) has become a strategic element of the modern educational space. Adaptive platforms, automated assessment systems, intelligent tutors, generative models, and big data analytics create new opportunities for individualized and inclusive learning. However, critics point out that excessive reliance on automated solutions can lead to decreased student independence, superficial knowledge acquisition, and disruption of long-term memory formation. The impact of AI on social development and cognitive dynamics is becoming a key issue in 21st-century educational research. The aim of the study: to analyze current issues related to the impact of AI on education and identify the conditions that ensure its positive influence on the educational process. Conclusions: AI in education has the potential to significantly enhance social development, expand access to knowledge, and foster new collaborative skills. However, without thoughtful implementation and pedagogical support, AI technologies can contribute to cognitive degradation, a decline in critical thinking, and a weakening of social competencies. Therefore, a key challenge for modern science and practice is finding the optimal balance where AI acts as a tool to enhance, rather than replace, human intelligence and social interaction. To this end, educators and educational institutions must take a comprehensive approach that covers the following areas: educators acting as mentors when working with AI; using AI as a mentor in education; and promoting transparency and digital literacy in education.

Background and Aim of Study: The full-scale war in Ukraine causes the population to experience numerous stressors that are layered on top of each other (forced displacement, losses, constant threats and existing traumas). This leads to emotional confusion (a state of reduced control of one’s own emotions), fatigue, narrowing of attention and impaired self-validation, which complicates self-understanding and self-care. All this poses numerous problems for the psychotherapist. They are due to the fact that standard psychotherapeutic programs may not be effective enough when clients are overwhelmed by the intensity of their problems. At the same time, assistance has to be provided within an extremely limited time frame. The aim of the study: to propose an integrated short-term intervention strategy for psychological counseling and psychotherapy to address emotional confusion in clients who have experienced multiple crises during wartime, utilizing the strengths of trauma-sensitive mindfulness, eye movement desensitization and reprocessing (EMDR), dialectical behavioral therapy (DBT), and self-compassion-based approaches. Conclusions: The integrative approach allows therapists and clients to create a snapshot of current difficulties. It involves the sequential application of elements from different modalities: grounding techniques (EMDR/Mindfulness), internal state description (DBT), external stressor inventory, identification of key maladaptive beliefs (EMDR), and the use of stabilization or reprocessing techniques. This structured, brief intervention helps clients describe their condition, understand the sources of emotional confusion, practice self-compassion, and prioritize problems. Implemented over 1–2 sessions, this approach helps clients move beyond emotional confusion and motivates adaptive change, thereby instilling hope.

Background and Aim of Study: The war in Ukraine is having a significant impact on the mental health of all its residents. This is particularly noticeable among young university students, who are forced to seek refuge both within and outside the country during their studies. The aim of the study: to identify the characteristics of life satisfaction and psychological resilience among university students in different learning environments during wartime. Material and Methods: The study was conducted at Uzhhorod National University in Ukraine in May 2025. It used the Google Forms platform to administer two psychological tests: the Satisfaction with Life Scale (SWLS) and the Connor-Davidson Resilience Scale 10 (CD-RISC 10). Respondents aged 18–35 were divided into two groups. Group 1 consisted of 116 students, 21.6% of whom were male and 78.4% of whom were female. None of them has changed their place of residence (Ukraine). Group 2 consisted of 108 students, 19.4% of whom were male and 80.6% of whom were female. They were all forced to relocate to other regions, including Ukraine and the European Union. Results: Both groups of students showed high internal consistency in their SWLS and CD-RISC 10 scores (Cronbach’s alpha of 0.856 to 0.929). Students in Group 1 report levels of extreme and general dissatisfaction with life that are almost 1.4 times higher than those in Group 2. However, this indicator is significantly higher among men than women. This suggests that their psychological well-being is poor. On average, students in Group 1 are 25.5% more psychologically resilient than those in Group 2, who are 23.6% resilient. Furthermore, the proportion of students in Group 2 who have low psychological resilience is almost double that in Group 1. In terms of gender characteristics, women in Group 1 demonstrate higher levels of psychological resilience (29.7%), whereas men in Group 2 demonstrate lower levels (14.3%). These are the highest levels observed across all gender groups. Conclusions: A positive strong correlation (r=0.811) has been found between psychological resilience and life satisfaction among university students during wartime, confirming high convergent validity. However, there was a significant decrease in psychological resilience among students in Group 2, who had relatively low levels of life satisfaction. Therefore, it is necessary to develop programmes that actively promote psychological intervention and social support to improve the mental well-being and learning effectiveness of university students.

Background and Aim of Study: Postgraduate studies in African countries face low completion rates due to capacity issues, hindering knowledge creation and innovation. The aim of the study: to map the steps involved in conducting a systematic literature review in Information Systems (IS) research to the identified review types, thereby providing a framework based on Generative Artificial Intelligence (AI)-based design science artefact for researchers and educators in the field IS for postgraduate teaching and learning. Material and Methods: A systematic literature review was conducted to identify the review types in IS research following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. The Association of Information Systems (AIS) database was used to identify relevant articles. The initial filter produced 2775 results. When focusing only on journal articles, the record produced 221 results, resulting in five papers qualifying for inclusion in the study. These papers were augmented to eight articles using one journal article and two conference papers identified through snowballing. Results: The results indicate that there are few publications within the AIS database on the tools used to support systematic literature review processes. However, those that exist do not reflect the type of review used. Additionally, tools that were used to support systematic literature review were those assisting with data extraction. Thus, frameworks may be needed to conduct a methodical review on various review types to ensure rigour and transparency in the findings of the reviews. Conclusions: This paper proposes a framework to guide the design of tools that can holistically support systematic literature review processes, making these reviews more accurate and less tedious. Such artefacts, especially using Generative AI tools, could potentially support postgraduate students in conducting rigorous reviews, improving completion rates and promoting knowledge creation and innovation in African countries.

ackground and Aim of Study: The war in Ukraine, which is still ongoing, has a significant impact on the mental state of each individual. The same applies to university students forced to seek refuge inside or outside the country. The aim of the study: to identify the characteristics of psychological distress experienced by university students in a war context and the role of a healthy lifestyle in overcoming it. Material and Methods: The study was conducted by Uzhhorod National University (Ukraine) in February 2025, involving the administration of adapted DASS-21 and HPLP-II psychological tests via the Google Forms platform. The respondents, aged between 18 and 35, were divided into two groups: those who were forced to relocate to other regions (European Union and Ukraine) and those who did not change their place of residence (Ukraine). Psychological distress and healthy lifestyle behaviours among students in wartime were identified using the DASS and HPLP-II. These tools demonstrated high internal consistency, with values ranging from 0.916 to 0.951. There was a statistically significant negative correlation between psychological distress and health-promoting lifestyles. Results: The levels of depression and anxiety were significantly higher among students in Group 1 than among those in Group 2. The study revealed the following gender-related findings: high average scores on the Depression and Anxiety scales (13.4 and 12.3 points, respectively) among women in Group 1; high average scores on the Stress scale (12.4 and 12.1 points, respectively) among men in all groups. The following healthy lifestyle practices play an important role in helping both groups to overcome the symptoms of psychological distress: interpersonal relationships (2.8 / 3.0 points), spiritual growth (2.8 / 2.9 points), and stress management (2.6 / 2.7 points). They had high and moderate HPLP-II scores. Students in Groups 1 and 2 demonstrated poor use of practices such as taking responsibility for their health (2.15 and 2.23 points), being physically active (2.2 and 2.3 points) and eating healthily (2.3 and 2.4 points). They had low HPLP-II scores. Conclusions: The high levels of depression and anxiety experienced by university students during the war were caused by a combination of psychogenic factors and their own behaviour. It does not promote mental health. The study results indicate the need to introduce measures that increase motivation for personal health and physical activity among university students, tailored to their specific requirements and needs.

Background and Aim of Study: The use of artificial intelligence (AI) by students and teachers in higher education is becoming increasingly common. AI implementing in higher education started as a spontaneous process among all stakeholders. Strategies and models of implementation in higher education systems must now justify this. The aim of the study: to explore the benefits and challenges of using AI in academic university teaching, and to develop and justify a model for the optimal implementation of AI for the development of the higher education ecosystem. Material and Methods: The present study used several theoretical methods: analysis, synthesis, comparison, generalisation, systematisation, and classification to define the benefits and challenges of AI use by stakeholders; systems approach, modelling, and optimisation methods to develop a model for the optimal implementation of AI in a higher educational ecosystem. Results: The prospects of AI implementation for developing the higher education ecosystem are considered. The advantages and problems of using AI in academic university teaching are characterised based on the classification of directions of using AI in higher education. The model of optimal implementation of AI in the educational ecosystem of higher education, based on the systems approach, has been developed and substantiated. This model include structural (universities, faculties, departments, institutes, centres, doctoral schools, clinics, and labs) and functional (internal – content of education, forms and methods of teaching, diagnosing of learning outcomes, administering of educational service, and eternal – include academic achievement: levels of knowledge, skills, and competences) components. Conclusions: The study highlights the importance of implementing AI in higher education, as well as the need for collaboration between all university stakeholders in the digitisation of education. The results are essential for developing university strategies for developing educational ecosystem The curriculum should be relevant, meeting the interests of students and the current needs of employers. Education stakeholders are encouraged to use the available benefits of AI responsibly to address the challenges of student learning and teacher organisation in universities.