Artificial Intelligence Maturity Assessment in Leadership at Higher Education: A Case Study

This chapter provides a comprehensive overview of a study investigating the integration of artificial intelligence (AI) in higher education. The chapter examines various applications, strategies, and ethical considerations associated with this integration. It explores how AI technologies can automate tasks such as teaching, learning, and grading, enhancing the efficiency and accessibility of education. Additionally, the chapter investigates the potential benefits of AI in delivering personalised learning experiences, catering to diverse learning needs, and enhancing student engagement through intelligent tutoring systems and virtual environments. Furthermore, the chapter addresses ethical concerns related to AI, including issues concerning data privacy and algorithmic bias. It emphasises the importance of aligning AI applications with educational objectives. It highlights the necessity of providing teacher training and continuously improving AI implementation to ensure that these technologies complement, rather than replace, the human aspects of 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.

Artificial Intelligence (AI) is permeating many facets of our daily lives, appearing in household appliances, cell phones and popular online apps. AI has the capacity to revolutionize teaching and learning processes in higher education institutions. The integration of AI technologies in education can lead to personalized learning experiences, improved educational quality and enhanced learning outcomes. However, the adoption of AI in higher education comes with challenges such as ethical considerations and the need to address equity and inclusion issues to ensure that all students benefit from AI advancements. This chapter considers how AI can be utilized in education, while acknowledging the challenges and finding ways to mitigate them. Useful tools include: Bespoke Learning, Intelligent Tutoring Systems, Grading, Collaborative, Learning Assistance, Research Support and Adaptive Learning. The challenges addressed are: ethical considerations, resistance to change and data security and privacy. In navigating the complexities of integrating AI in higher education, institutions must strike a balance between leveraging the transformative potential of AI technologies and addressing the ethical, social and technical challenges that accompany their implementation. By prioritizing ethical considerations, addressing resistance to change and safeguarding data security and privacy, higher education institutions can harness the benefits of AI to enhance teaching and learning practices, foster innovation and prepare students for success in the digital age.

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.

In recent years, the European Union has advanced towards responsible and sustainable Artificial Intelligence (AI) research, development and innovation. While the Ethics Guidelines for Trustworthy AI released in 2019 and the AI Act in 2021 set the starting point for a European Ethical AI, there are still several challenges to translate such advances into the public debate, education and practical learning. This paper contributes towards closing this gap by reviewing the approaches that can be found in the existing literature and by interviewing 11 experts across five countries to help define educational strategies, competencies and resources needed for the successful implementation of Trustworthy AI in Higher Education (HE) and to reach students from all disciplines. The findings are presented in the form of recommendations both for educators and policy incentives, translating the guidelines into HE teaching and practice, so that the next generation of young people can contribute to an ethical, safe and cutting-edge AI made in Europe.

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.

This systematic literature review explores the integration of artificial intelligence (AI) technologies such as intelligent tutoring systems (ITS), machine learning, natural language processing, and adaptive learning platforms in university education. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we evaluated peer-reviewed articles, case studies, and government reports from 2015 onwards. The results demonstrate that AI technologies not only enhance personalized learning and educational outcomes but also streamline administrative functions, transforming educational practices. However, challenges such as ethical issues, data privacy, and algorithmic bias remain. The review underscores the importance of theoretical frameworks like constructivist learning theory and the TPACK framework for effective AI integration. Recommendations are provided for educators, administrators, and policymakers to ensure responsible AI use in university settings. This paper offers insights into the current capabilities and future prospects of AI in higher education, promoting ongoing research and strategic implementation.

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.

n the context of total digitalization of education, problems connected with the implementation of artificial intelligence (AI) technologies, as well as issues related to the role of a person as a carrier of human intelligence (HI) in the educational process, become especially topical for universities. The increasing penetration of AI into many areas of higher education requires a balanced approach, considering all the pros and cons, assessing the risks and consequences for humans as subjects of education. The goal of the article is to compare AI and HI capabilities in the educational process of a university. The authors set and solved the following tasks: 1) to study the theoretical material and practical experience of Russian universities in the aspect of applying AI and HI in higher education; 2) to study the concepts of “artificial intelligence” and “human intelligence”; 3) to identify the advantages and disadvantages of AI and HI for the educational process at a university; 4) to present the comparative description of AI and HI in the educational process of a university; 5) to define the perspectives for the development of AI and HI in higher education. To solve the tasks the methods of analysis of scientific literature, analysis of regulatory documents, study and generalization of advanced pedagogical experience, and comparative analysis are used. The results indicate that the issues of introducing AI technologies into the educational process of universities are topical for the state and society. In theory, there is no consensus on the definition of the AI phenomenon, while HI is interpreted as the sum of a person’s cognitive abilities. The review of practice shows a wide range of applicability of AI technologies in universities: from automating routine tasks to creating an immersive educational environment. The comparative analysis of the capabilities of AI and HI has been carried out according to the following parameters: processing and analysis of big data, adaptive learning, interactivity, feedback, creative thinking and critical thinking, complexity of tasks, ethics and morality, emotional communication, objectivity of assessment. The analysis shows that AI and HI in relation to higher education have advantages and disadvantages. A promising scenario for the future development of the system of higher education under the influence of AI technologies will be the integration of AI and HI, which opens up new opportunities for humans in Education 4.0.

AI (Artificial intelligence) has permeated the world and currently has an undeniable footprint in most areas of society. In relation to the HE (higher education) sector, AI is becoming a prominent feature. AI in HE is readily accessible through the use of ‘intelligent tutoring systems, teaching robots, learning analytics dashboards, adaptive learning systems and human-computer interfaces’ (Ishak & Jiang 2022:70). Pelletier, Robert, Muscanell, McCormack, Reeves, Arbino, and Grajek (2023:4) aptly highlight the impact that AI currently has on the globe and the need to consider it as a current trend in the HE sector. Huang, Saleh, and Liu (2021:206) similarly argue that it is indisputable that AI has infiltrated the education space. As modern science and technology progress, the advancement in AI has also progressed. Research indicates that AI, when applied to education, has resulted in positive effects which have aided toward teaching reform (Huang et al. 2021:206).

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.

This chapter examines the increasing role of Artificial Intelligence (AI) in higher education, focusing on its integration, implementation strategies, and the ethical challenges that arise. It provides insights into how generative AI (GenAI) tools and large language models (LLMs) can be used effectively in classrooms, offering educators and administrators practical guidance for navigating this evolving landscape. The chapter also highlights potential future developments of AI in higher education and explores professional development opportunities to help institutions adapt. By addressing both the benefits and challenges of AI, the chapter aims to empower higher education professionals with the knowledge and tools needed to use AI to enhance teaching, learning, and overall educational experiences. Ultimately, it provides a guide for effectively incorporating AI to support and transform educational practices.

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.

Artificial Intelligence (AI) developments challenge higher education institutions’ teaching, learning, assessment, and research practices. To contribute timely and evidence-based recommendations for upholding academic integrity, we conducted a rapid scoping review focusing on what is known about academic integrity and AI in higher education. We followed the Updated Reviewer Manual for Scoping Reviews from the Joanna Briggs Institute (JBI) and the Preferred Reporting Items for Systematic reviews Meta-Analysis for Scoping Reviews (PRISMA-ScR) reporting standards. Five databases were searched, and the eligibility criteria included higher education stakeholders of any age and gender engaged with AI in the context of academic integrity from 2007 through November 2022 and available in English. The search retrieved 2223 records, of which 14 publications with mixed methods, qualitative, quantitative, randomized controlled trials, and text and opinion studies met the inclusion criteria. The results showed bounded and unbounded ethical implications of AI. Perspectives included: AI for cheating; AI as legitimate support; an equity, diversity, and inclusion lens into AI; and emerging recommendations to tackle AI implications in higher education. The evidence from the sources provides guidance that can inform educational stakeholders in decision-making processes for AI integration, in the analysis of misconduct cases involving AI, and in the exploration of AI as legitimate assistance. Likewise, this rapid scoping review signals key questions for future research, which we explore in our discussion.

This study explores academics’ perspectives on integrating artificial intelligence (AI) into Moroccan higher education (HE). A questionnaire examining perceived benefits, challenges, and influence of demographic factors was distributed to 103 faculty members at the college of Arts and Humanities, Sidi Mohamed Ben Abdellah University in Fez, Morocco. The results reveal that AI offers promising opportunities to enhance educational practices by improving efficiency and providing valuable learning resources. However, participants also expressed concerns that increased use of AI may negatively impact student-instructor interactions and academic integrity. Preliminary analyses suggested age and academic discipline may shape views on AI, but robust statistical testing was needed. Overall, findings indicate AI adoption presents a dual-edged situation for HE. If properly planned and regulated and equity concerns are addressed, AI technologies can transform teaching and learning positively, but unaddressed challenges may undermine educational quality and student experiences. This study provides timely insights into how best to maximize AI’s benefits while addressing its risks as digital transformation increasingly impacts Moroccan universities.