Assessment of the university emotional climate using convolutional models: A study in the Systems Engineering Degree Program – UPEA

The integration of Artificial Intelligence (AI) in higher education has sparked a transformative shift in pedagogical methodologies, student engagement, and academic integrity across the UK and Ireland (Dogru, et.al, 2023). This qualitative study delves into the multifaceted implications of utilising AI in academic assignments from the perspective of higher education students, drawing on semi-structured interviews carried out with higher education students. The findings reveal a variety of perceived benefits, including enhanced study efficiency, personalised learning support (Hanaba et al., 2020; Yang, 2021), and the potential of AI to level the academic playing field for students with diverse needs. However, these advantages are in contrast with students’ perceptions of a wide range of significant concerns and challenges, notably the ethical dilemmas surrounding academic honesty, the potential for student dependency on AI leading to diminished knowledge and skill development, as well as the issue of equity in relation to students’ access to AI resources. Moreover, the study stresses a critical gap in institutional guidance regarding AI use, with students voicing a need for clear, consistent guidance from universities. This paper highlights the complex landscape of AI in academia, advocating for a balanced approach that harnesses AI's potential while addressing ethical, educational, and equity challenges. The study emphasises a need for immediate and more in-depth research into the use of AI in higher education from the perspective of both students and university staff in Ireland/UK.

Human-centered approaches are vital to manage the rapid growth of artificial intelligence (AI) in higher education, where AI-driven applications can reshape teaching, research, and student engagement. This study presents the Human-Centered AI for Systematic Literature Reviews (HCAI-SLR) framework to guide educators and researchers in integrating AI tools effectively. The methodology combines AI augmentation with human oversight and ethical checkpoints at each review stage to balance automation and expertise. An illustrative example and experiments demonstrate how AI supports tasks such as searching, screening, extracting, and synthesizing large volumes of literature that lead to measurable gains in efficiency and comprehensiveness. Results show that HCAI-driven processes can reduce time costs while preserving rigor, transparency, and user control. By embedding human values through constant oversight, trust in AI-generated findings is bolstered and potential biases are mitigated. Overall, the framework promotes ethical, transparent, and robust approaches to AI integration in higher education without compromising academic standards. Future work will refine its adaptability across various research contexts and further validate its impact on scholarly practices.

This qualitative study investigates the opportunities and concerns regarding the integration of artificial intelligence (AI) in higher education. Through in-depth interviews with students, faculty, parents, administrators, policymakers, and employers, the research explores the complex landscape of AI adoption in colleges and universities. Thematic analysis reveals shared anxieties about equity and inclusion, the preservation of human interaction in teaching, potential job displacement, the ethical implications of AI, technical capabilities and resource requirements, the impact on educational quality and student outcomes, and the alignment of AI education with workforce demands. The findings underscore the need for a collaborative and transparent approach to AI integration that addresses stakeholder concerns and prioritizes ethical considerations, pedagogical effectiveness, and societal values.

This paper presents a bibliometric analysis of recent research on the integration of artificial intelligence (AI) in higher education, with a particular focus on the obstacles encountered, the ethical dilemmas raised, and the associated governance issues. The study is based on a corpus of 803 peer-reviewed articles extracted from the Web of Science database for the period 2020–2024. The analysis was conducted using the VOSviewer and Biblioshiny tools, which identified the most frequently co-occurring keywords, major contributors, global collaboration networks, and structuring thematic clusters. The results highlight a strong focus on technology acceptance models (particularly UTAUT and TAM) and the pedagogical integration of AI. However, several critical gaps remain, particularly regarding personal data protection, algorithmic bias, the lack of robust regulatory frameworks, and the underrepresentation of developing countries in scientific publications and academic collaboration networks. The study highlights the importance of strengthening ethical governance mechanisms, developing inclusive policies to support the growth of AI in the Global South, and promoting more balanced scientific partnerships at the international level.

The paper focuses on the negative aspects of artificial intelligence in higher education such as biases in the datasets and algorithms, plagiarism, factual incorrectness, micromanagement of students and employees, manipulation of behaviour, überveillance, overreliance on AI, lack of or low explainability and transparency of AI’s decisions, loss of skills, and privacy concerns. The paper adopts an operations management perspective and discusses the negative aspects of AI in the various processes in higher education institutions such as enrolment of students, hiring of employees, teaching/learning/assessment, administrative activities, research, socialisation and well-being of students, remuneration, appraisal and wellbeing of employees. Special attention is paid to AI’s impacts on ethics, creativity and critical thinking. Potential solutions to avoid or mitigate the negative impacts of AI, theoretical, managerial and policy implications are discussed as well.

This research aims to describe and map information and research ideas that can be developed in the application of artificial intelligence in higher education. The method applied in this research is Preferred Reporting Items For Systematic Reviews And Meta-Analyses (PRISMA) which uses scientific articles or proceedings sourced from the Scopus database which are analyzed using Vos Viewer and Publish Or Perish as well as Google Trends and Dimensions. The research results reveal that artificial intelligence research covers three aspects that can be applied in higher education: development of AI tools, application of AI in learning, and digitalization of higher education.

Scientific research on the assessment of educational outcomes using artificial intelligence technologies in higher education is still fragmented, especially in the context of Ibero-American countries and the transforming educational systems, which include Kazakhstan. Despite the active introduction of digital platforms, analytical tools, and automated assessment systems, comprehensive research revealing innovative approaches, methodological foundations, and ethical aspects of AI-based assessment remains limited. In this regard, this mini-review is aimed at filling the identified research gap by analyzing key theoretical works and modern empirical studies that determine the prospects for further development of this field. The methodological basis of the review is a conceptual model that considers assessment using artificial intelligence as a multi-level system that includes automated analysis of educational data, personalized feedback, predictive evaluation mechanisms, and algorithmic support for pedagogical decisions. Within the framework of this model, assessment is interpreted not as an isolated control tool, but as an integrated component of the educational environment that influences individual learning trajectories, academic success and student engagement. It should be noted that this mini-review is one of the first studies in which this conceptual framework is applied to a comparative analysis of assessment practices using artificial intelligence in the Ibero-American and Kazakh contexts of higher education. The review included 12 peer-reviewed scientific publications published since 2020, which addressed the issues of automated assessment, learning analytics, adaptive feedback systems and ethical aspects of the introduction of artificial intelligence in the university environment of these regions. The paper analyzes the main innovations in the field of AI-based assessment, including the use of behavioral and cognitive analytics, automated formative assessment, forecasting academic risks and personalized assessment support for students. Special attention is paid to contextual factors such as the level of digital readiness of students, the institutional features of universities, as well as differences in assessment practices between public and private educational organizations. Along with technological innovations, the mini-review examines the key ethical challenges associated with the use of artificial intelligence in assessing educational achievements. Such challenges include algorithmic bias, limited transparency of assessment decisions, issues of protecting students’ personal data, and the risks of increasing educational inequality. The analysis shows that the manifestation of these ethical issues varies in Ibero-American countries and Kazakhstan, reflecting differences in educational policies, the level of digital infrastructure, and socio-economic conditions. Based on research using a variety of methodological approaches, including quantitative, qualitative, and mixed designs, this mini-review demonstrates the multiplicity of interpretations and practices of applying artificial intelligence in higher education assessment. At the same time, the need to expand interdisciplinary research aimed at analyzing poorly studied categories of students and alternative educational trajectories in the digital environment is emphasized. In conclusion, the educational and ethical implications of further implementation of AI-based assessment in higher education are considered. Given the increasing role of automated assessment systems and the desire of universities to improve the objectivity and fairness of assessment, the development of methodologically sound and ethically responsible models for the use of artificial intelligence is of particular relevance. At the same time, taking into account the regional characteristics of educational systems makes the comparative analysis of the Ibero-American and Kazakh experience an important area of modern research in the field of digital education.

Introduction: The integration of artificial intelligence (AI) into higher education is swiftly revolutionizing pedagogical methodologies, in conjunction with learning processes and research paradigms. The interdisciplinary potential of AI within academic settings was examined in this study, employing a case study conducted at the University of Tirana. Through the utilization of bibliometric analysis and survey-based research, this study comprehensively investigates the swiftly emerging trends in AI applications, students' significant familiarity with AI technologies, as well as the substantial challenges impeding broader adoption. The bibliometric analysis highlights significant exponential growth in AI research, particularly within pivotal domains such as finance and accounting, thereby emphasizing the swiftly increasing relevance of blockchain and automation. The survey indicates a robust enthusiasm among students for AI in educational settings. More than 90 percent of students actively incorporate AI tools in their project work. Nonetheless, resource limitations and ethical considerations, including privacy, data security, and algorithmic bias, pose considerable challenges to the widespread adoption of AI, despite the prevailing enthusiasm. Objective: The aim of this research is to examine the incorporation of Artificial Intelligence (AI) within the realm of higher education. This analysis concentrates on the applications, advantages, and challenges associated with AI, with the intent of advancing interdisciplinary research and educational methodologies at the University of Tirana. Theoretical Framework: This research expands upon the principles of AI adoption within the educational sector, alongside an examination of ethical considerations and multidisciplinary collaboration. Theories pertaining to technological integration and adaptive learning systems serve as the foundational framework for comprehending the implications of AI in the realm of education. Method: The methodology employs bibliometric analysis to examine AI-related research trends utilizing data from SCOPUS and conducts a survey to assess students' familiarity with and perceptions of AI. Data collection was facilitated through bibliometric instruments and an online survey incorporating Likert-scale and open-ended questions. Results and Discussion: The results underscore an increased focus on artificial intelligence (AI) and blockchain within scholarly research, wherein students exhibit considerable engagement and interest in AI applications. Nevertheless, limitations in resources and ethical issues, including privacy and bias, persist as primary challenges. The discourse underscores the imperative for investment in infrastructure and the incorporation of ethical education. Research Implications: This research highlights the imperative for higher education institutions to integrate artificial intelligence tools, cultivate adaptive curricula, and address ethical considerations in order to adequately prepare students for a future shaped by AI. The implications of these findings also pertain to educational policy and the formulation of interdisciplinary research strategies. Originality/Value: The study contributes by delivering a comprehensive bibliometric analysis and gives insights into student engagement with artificial intelligence. Its significance resides in presenting actionable recommendations to enhance the integration of artificial intelligence in higher education.

Technology trends have changed the educational paradigm, especially at the tertiary level. Artificial Intelligence (AI) has emerged as a way to change the learning process with the form of games in education, this will make education in higher education experience increased efficiency and effectiveness in learning. However, a deep understanding of how artificial intelligence can improve higher learning experiences and their impact after graduation is still limited. This research aims to investigate how artificial intelligence helps learning in higher education and its impact on students' career preparation after graduation. The focus is on analysis of the implementation of AI in teaching, learning, and decision-making support in higher education institutions. The research method used is literature study and content analysis. Data was collected from scientific articles, books, as well as related research reports on the use of artificial intelligence in higher education. The data is then analyzed to identify trends, benefits, and challenges associated with applying AI in educational contexts. Research results show that the use of artificial intelligence in higher education has increased the personalization of learning, increased knowledge retention, and facilitated more timely and accurate feedback. Additionally, AI also plays an important role in helping students develop skills relevant to the future job market. The conclusion of this research is that by integrating artificial intelligence into higher education, institutions can improve the quality of learning and help students be better prepared to face the challenges of an ever-changing job market. However, challenges such as technology dependency and the need for proper training for educators and students must also be addressed so that the full potential of AI in education can be realized.

Generative Artificial Intelligence (GenAI) has attracted growing attention in higher education due to its potential to transform teaching, learning, assessment, and academic production. This article presents a systematic literature review focused on the use of GenAI in university settings, conducted following Kitchenham's methodological model. A total of 41 studies published between 2020 and 2025 were analyzed across major databases such as Scopus, Web of Science, Springer, and IEEE Xplore. The findings reveal a wide range of applications, including learning personalization, automated feedback, academic writing support, content generation, and optimization of teaching and administrative tasks. However, the review also identifies significant ethical, pedagogical, and epistemological challenges, such as academic integrity, algorithmic bias, and the urgent need to strengthen critical digital literacy among both instructors and students. This review systematizes 26 GenAI tools, 21 educational strategies, and 12 ethical implications, providing a comprehensive foundation for institutional policy development and future research. The study ultimately aims to promote the responsible, ethical, and pedagogically meaningful integration of emerging generative AI technologies within higher education.

The implementation of Artificial Intelligence (AI) in higher education poses significant ethical dilemmas that require a deep understanding to ensure its beneficial and equitable use. This research explored the ethical challenges associated with the use of AI, specifically ChatGPT, in the university setting. The methodology was developed in two phases: a systematic literature review and field research using surveys of university students. The results revealed a generally positive attitude toward ChatGPT, with strong recognition of its usefulness and the importance of AI literacy. However, concerns about academic integrity and the potential promotion of mental laziness were identified. A weak correlation was found between major choice and ethical perceptions, while age showed no significant influence. The study also highlighted the need to improve citation and source verification practices when using AI. These findings provide a solid foundation for the development of institutional policies and educational programs that address the ethical challenges of AI in higher education, emphasizing the importance of tailored approaches according to academic discipline.

Generative artificial intelligence (AI) has emerged as a transformative tool in various domains, including education, where it holds the potential to revolutionize learning experiences and outcomes. Despite its promising applications, the adoption and utilization of generative AI tools among students remain variable and complex. This article presents a mixed-methods approach to examine factors that influence student interest and use of generative AI in educational contexts. Using the theoretical foundations of the technology acceptance model (TAM), we explore the multifaceted determinants that shape students’ attitudes, perceptions, and behaviors toward utilizing generative AI technologies for learning purposes. Importantly, institutional influence has a positive influence on the intention to adopt ChatGPT as a learning tool. Insights into the challenges and opportunities associated with integrating generative AI into educational settings, as well as strategies for enhancing its acceptance and effectiveness among students are explored.

Aim. To formulate approaches to institutional regulation and preventive response to risks associated with the use of artificial intelligence technologies in science and higher education organizations on the basis of compliance. Objectives. To determine the elements of compliance system aimed at reducing the risks of using artificial intelligence; to identify the trends of institutional development related to the integration of generative artificial intelligence technologies in scientific and educational and administrative and management processes. Methods. The issues of adaptation of institutional mechanisms of modern higher education to the challenges of technological development are investigated on the basis of system, risk-oriented, logical and structural approaches of scientific knowledge. The methods of analysis, synthesis and classification were applied to form the elements of compliance-system of artificial intelligence. Results. The concept of artificial intelligence compliance is designed to expand the strategy of digital transformation of higher education organizations by introducing new objects into the subject area of management, in particular, the risks of artificial intelligence technologies, as well as the development of protective, regulatory and information-management functions. Methods compliance allows to create organizational mechanisms that provide protection of higher education and science organizations from unfair behavior of subjects in the field of artificial intelligence. Artificial intelligence compliance in higher education should ensure the protection of human rights and freedoms, compliance with the rules of academic honesty and scientific integrity, assessment of the reliability of artificial intelligence systems in the educational process, protection of personal data and confidential information, copyright protection, counteraction to data falsification and cybercrime. Conclusions. Proactive management on the basis of compliance methodology will reduce the risks of implementation of artificial intelligence technologies in the activities of universities and will help to effectively adapt to the challenges of technological development.

ChatGPT and Artificial Intelligence in Higher Education: Literature Review Powered by Artificial Intelligence

The increasing role of artificial intelligence in higher education has led to a growing interest in automating the generation of examination and practice tasks.Large language models have emerged as a promising solution to reduce the time and effort required for exam creation while enhancing flexibility and scalability.This systematic literature review provides a comprehensive overview of research on large language model based task generation, examining its effectiveness, methodological approaches