AI-Driven Decision-Making Applications in Higher Education

Decision-making in higher education institutions faces complex challenges due to increasing data, regulatory demands, resource management, and changing educational needs. Effective decisions require accurate, timely insights into institutional processes. This study introduces an integrated decision-support approach consisting of Learning Analytics, Digital Twins, and Generative Artificial Intelligence (AI) with Retrieval-Augmented Generation (RAG). The objective is to enhance decision accuracy and operational efficiency in higher education. Learning Analytics provide insights into student performance, teaching effectiveness, and curriculum efficiency. Generative AI with RAG facilitates rapid retrieval and synthesis of institutional documentation and regulatory framework, enhancing decision accuracy. Digital Twins simulate institutional operations, enabling predictive resource planning and infrastructure modeling. Preliminary results indicate significant improvements in strategic planning and resource management by using an integrated decision support approach. Collectively, these technologies transform strategic decision-making and efficiency in higher education, enabling an agile and smart approach, offering a robust solution to contemporary challenges.

Art education has undergone significant transformation in the era of digitalization. With the advancement of Artificial Intelligence (AI) technology, there is tremendous potential to enhance the art learning experience. This research aims to elucidate how AI technology can be effectively utilized to improve art education in the context of digitalization. The study aims to introduce AI technology and explore its potential applications in enhancing the art learning experience. In the context of art learning experience, AI technology can provide broader access to digital art collections, online galleries, and virtual museums. Students can explore various artworks and cultures through digital platforms, thereby enriching their understanding and appreciation of art. The research findings reveal that AI technology can offer personalized guidance to students in developing their artistic skills. Intelligent AI systems can analyze students' artworks, provide tailored suggestions, and assist in enhancing their technical and creative abilities. In conclusion, the integration of AI technology in art education holds immense potential to elevate students' learning experiences. By expanding access, providing personalized guidance, and creating interactive experiences, AI technology enriches art learning in the era of digitalization. This is expected to provide greater insights and understanding of how AI technology can be harnessed to enhance the art learning experience in the context of digitalization. By optimizing the potential of AI, art education can become more inclusive, personalized, and engaging.

he rapid progress in implementing Artificial Intelligence (AI) across various domains such as healthcare decision-making, medical diagnosis, and others has raised significant concerns regarding the fairness and bias embedded within AI systems. This is particularly crucial in sectors like healthcare, employment, criminal justice, credit scoring, and the emerging field of generative AI models (GenAI) producing synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including biases ingrained in the synthetic data representation of individuals.This survey paper provides a concise yet comprehensive examination of fairness and bias in AI, encompassing their origins, ramifications, and potential mitigation strategies. We scrutinize sources of bias, including data, algorithmic, and human decision biases, shedding light on the emergent issue of generative AI bias where models may replicate and amplify societal stereotypes. Assessing the societal impact of biased AI systems, we spotlight the perpetuation of inequalities and the reinforcement of harmful stereotypes, especially as generative AI gains traction in shaping public perception through generated content.Various proposed mitigation strategies are explored, with an emphasis on the ethical considerations surrounding their implementation. We stress the necessity of interdisciplinary collaboration to ensure the effectiveness of these strategies. Through a systematic literature review spanning multiple academic disciplines, we define AI bias and its various types, delving into the nuances of generative AI bias. We discuss the adverse effects of AI bias on individuals and society, providing an overview of current approaches to mitigate bias, including data preprocessing, model selection, and post-processing. Unique challenges posed by generative AI models are highlighted, underscoring the importance of tailored strategies to address them effectively.Addressing bias in AI necessitates a holistic approach, involving diverse and representative datasets, enhanced transparency, and accountability in AI systems, and exploration of alternative AI paradigms prioritizing fairness and ethical considerations. This survey contributes to the ongoing discourse on developing fair and unbiased AI systems by outlining the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the burgeoning field of generative AI.

Universities worldwide are rapidly advancing artificial intelligence (AI) to reconcile the perennial tension between educational quality, scalability, and personalization.This paper analyzes 13 studies and case analyses, focusing on AI applications in higher education: (1) adaptive learning systems, (2) real-time instructional tools, and (3) automated assessment feedback. These technologies improve course completion, reduce instructor preparation time, and enhance student writing.Nonetheless, they also introduce fresh risks related to academic integrity, data privacy, shifting teacherstudent roles, and equity in resource distribution. This paper proposes a two-tier, triadic governance framework to address the intertwined challenges of educational ethics and quality, user adaptation, and the digital divide posed by generative AI. Universities should adopt honor codes, AI evaluation guidelines, secure data sandboxes, and AI-literacy curricula. Governments and industry must provide regulatory standards, resources, and multilingual large-language-model infrastructure. AI can significantly drive equity and excellence in higher education when internal self-regulation aligns with external policies.

The rise in integration of Artificial Intelligence (AI) tools in different sectors has generated a substantial impact in their prospective applications in the academic sector. This study analyzes the interest in adopting AI technologies in post-secondary education, mainly focusing on developing countries. It assesses the benefits, complications, and ethical considerations linked with the usage of AI in academic practices, including teaching, training, learning, and research. By conducting a thorough review of existing literature, the work highlights key benefits of AI adoption, for instance enhanced educational experiences, possibilities for personalized learning, and optimized administrative efficiency. Moreover, the study responds to potential challenges, such as biases in AI systems, threats to social engagement and critical thinking, and the negative impact on creativity within academic environments. To efficiently leverage AI’s advantages while maintaining fundamental educational values, the study proposes strategic ways for successful AI integration. Also, the paper underscores the significance of considering the distinctive context of developing nations, especially the issues related to limited resources and the necessity to make assure equal availability to AI technologies. By addressing these challenges, the study aims to give a roadmap for effectively and responsibly adopting AI technologies into higher education systems.

Aim/Purpose In a post-pandemic learning environment, artificial intelligence (AI) may become the new standard. More and more college students are utilizing AI tools like ChatGPT and Bard to enhance their learning. This study compares the use of web-search and AI-based systems to assess how much students' knowledge has improved in an Internet Marketing course. Background While authorship and academic integrity have received a lot of attention in higher education research thus far, using AI effectively provides several benefits for both teaching and learning. In other words, by utilizing these capabilities, AI-based systems may boost students' motivation, interest, and level of knowledge. Methodology The course will teach the students how to use the application of AI (i.e. ChatGPT). To evaluate the efficacy of AI technology, this study will conduct empirical research to compare the knowledge improvement of students who would obtain knowledge about buying sustainable production products in Taiwan between using the web-search systems and the AI-based systems. The assessments of students' knowledge growth are evaluated by using a dependent samples t-test. Contribution This finding expands on prior research examining the possibility of using AI-powered learning aids to inspire students (Al Shamsi et al., 2022; Lund et al., 2023). The ability of ChatGPT to deliver cutting-edge content, personalized feedback, and interactive learning sessions may all contribute to generating curiosity, a sense of accomplishment, and ultimately a greater desire to learn more about subjects. Findings It is anticipated that the AI chatbots created for information delivery might have something to do with students' increased interest in, motivation for, and understanding of their studies. This study examines the potential benefits to students' understanding of sustainable development goals with an AI-specific method of information provision relevant to purchasing items produced sustainably. Recommendations for Practitioners In the near run, using AI systems has been proven to boost university students' sentiments. University students may feel more supported by AI when they use it more regularly. Long-term AI use may cause reliance, especially in the absence of human companionship. Recommendations for Researchers Not all students and instructors have a lot of experiences in teaching and learning with generative AI, despite the fact that it is a relatively new technology. After generative AI is completely incorporated into classrooms, the roles of AI in teaching and learning could reconsider. Impact on Society In order to prepare students for work in a world driven by generative AI, higher education in the future needs change. With an emphasis on in-class and hands-on activities for assessment, new learning outcomes—skills in learning and teaching with AI, AI literacy—and the importance of interdisciplinarity should be highlighted. Future Research Future studies should concentrate on the following topics: interdisciplinary education, creative pedagogies and their evaluation, new assessment and its acceptability.

Artificial Intelligence (AI) is rapidly transforming higher education and vocational training, presenting both unprecedented opportunities and significant challenges. This integrative literature review examines the impact of AI-driven innovations on education, focusing on their implications for educators, students, and employability. The study explores key themes, including AI-driven educational tools, institutional policies, ethical considerations, and the role of educators in ensuring equitable access to AI-powered learning resources. While AI enhances adaptive learning, assessment automation, and personalized educational experiences, its implementation risks exacerbating inequalities due to biased algorithms and unequal infrastructure distribution. Furthermore, the study highlights the necessity of professional development and institutional strategies to empower educators in leveraging AI effectively. It also introduces specialized roles such as Vocational AI Curriculum Developer and Vocational AI Data Protection Specialist to support AI integration in vocational training. By synthesizing research on AI applications in higher education and vocational training, this review provides critical insights into strategic investments, policy recommendations, and sustainable AI-driven teaching models. The findings underscore the importance of a balanced approach that fosters innovation while ensuring ethical AI deployment, ultimately preparing students for an evolving job market. The findings show that AI can improve teaching, research, and administration, but its success depends on continuous research, collaboration, and careful planning. The paper emphasizes the importance of a fair and wellstructured approach to AI adoption in higher education and vocational training, ensuring that technological advancements lead to accessible, ethical, and meaningful learning experiences.

HRMARS - Purpose: This study aims to examine the development trajectory, research hotspots, and thematic evolution of artificial intelligence (AI) applications in higher education over the past decade. By mapping global scholarly output, the study seeks to provide a comprehensive understanding of how AI is reshaping pedagogical practices, learning environments, and instructional strategies. Design/methodology/approach: A bibliometric analysis was conducted using literature published between 2012 and 2023 retrieved from Scopus, Web of Science, and CNKI. Citation analysis, keyword co-occurrence, and Latent Dirichlet Allocation (LDA) topic modeling were employed, supported by visualization tools such as VOSviewer and CiteSpace. The analysis identified research trends, collaboration patterns, and emerging thematic clusters in the field. Findings: The results indicate a sustained growth in the number of publications, reflecting the increasing academic interest in AI-enhanced higher education. Key research themes include personalized learning, intelligent tutoring systems, learning analytics, and data-driven decision-making. The findings also reveal strong international research contributions, particularly from the United States, China, and the United Kingdom, highlighting the global relevance and collaborative nature of this field. Research limitations/implications: This study is limited by its reliance on published literature from major databases, which may exclude relevant grey literature. Differences in indexing and data formats across databases may also introduce noise in data integration. Future research should consider broader data sources and incorporate cross-validation approaches to improve reliability. Practical implications: The insights generated offer valuable guidance for universities, policymakers, and educational technologists seeking to implement AI-driven innovations in teaching and learning. The findings underscore the importance of interdisciplinary collaboration and informed decision-making in developing effective AI applications in higher education. Originality/value: This study provides one of the most comprehensive bibliometric overviews of AI research in higher education from 2012 to 2023. By integrating citation metrics, co-occurrence analysis, and advanced topic modeling, it offers a nuanced understanding of the field’s evolution and proposes new directions for ethical, interdisciplinary, and sustainable AI development in university contexts.

The advent of artificial intelligence (AI) marks a pivotal shift in the landscape of corporate governance, catalyzing a reevaluation of traditional frameworks and necessitating a forward-looking approach to decision-making, risk management, and ethical considerations. This study explores the multifaceted impact of AI on corporate governance, offering a nuanced analysis of how AI technologies are transforming the operational, strategic, and ethical dimensions of organizations. The research underscores the potential of AI to enhance decision-making processes, optimize operational efficiencies, and foster innovation by providing advanced analytical capabilities and predictive insights. However, it concurrently highlights the emergence of unprecedented challenges, including data privacy concerns, algorithmic bias, and the need for robust regulatory frameworks to mitigate risks associated with AI deployment. The article advocates for a proactive stance in redefining corporate governance models to accommodate the disruptive nature of AI, emphasizing the integration of ethical considerations and transparency in AI applications. It calls for a collaborative effort among corporate leaders, policymakers, and stakeholders to develop governance structures that not only leverage AI’s potential but also safeguard against its inherent risks. The study’s recommendations include the establishment of ethical AI guidelines, the adoption of transparent AI practices, and the continuous monitoring of AI systems to ensure their alignment with corporate governance objectives and societal values. However, it is important to note that the approach and methods used in this study are based on a qualitativeliterature review and, therefore, the generalization of the findings across different sectors and corporate governance frameworks may be limited. Additionally, the rapidly evolving nature of AI technologies poses inherent challenges to keeping up with emerging trends and potential risks.

The rapid emergence and integration of generative artificial intelligence (GenAI) in higher education present both challenges and opportunities. There is a critical need to consolidate empirical evidence, existing practices, assessments, and normative discussions to reflect on the recent advancement and inform adaptation in higher education policies. Asia presents a compelling context in which to explore the impact of GenAI on higher education due to its diverse educational landscape, rapid technological advancements, and unique socio-cultural dynamics. This Special Issue examines the inclusion of GenAI in higher education in Asia and reflects on the challenges and opportunities brought about by this sweeping revolution. Ten articles in this special issue examine GenAI and its application in higher education from diverse perspectives. Synthesizing these fresh findings based in Asia, this introductory article proposes a framework of actionable strategies for integrating GenAI into higher education that may inform education policymaking and administration in the years to come.

Background: Generative Artificial Intelligence (AI), particularly large language models like ChatGPT, has rapidly entered medical education. In pharmacology instruction within MBBS programs, these AI tools offer novel educational benefitsfrom ondemand tutoring to personalized learningbut also pose risks due to "hallucinations, " i. e., AI -generated misinformation. Objective: This review explores how generative AI is being integrated into pharmacology education, the educational advantages it provides, the nature and causes of AI hallucinations, and the potential risks of relying on inaccurate AI -generated content in medical learning. Mitigation strategies, best practices, case examples, ethical considerations, and future directions are discussed to inform balanced and safe use. Methods: A literature review was conducted focusing on recent (2020-2025) peer -reviewed sources and authoritative reports on generative AI in medical and pharmacology education. Results: Generative AI can enhance pharmacology education through adaptive learning, rapid information access, and interactive simulations, which students have found useful for studying and clinical preparation. However, AI hallucinationsplausible -sounding yet incorrect answersare common, with studies reporting a substantial fraction of AI -generated medical responses containing errors. These inaccuracies can mislead learners and threaten patient safety if not recognized. Strategies such as careful validation of AI -provided information, AI literacy training, and technical approaches (e. g. retrieval -augmented generation) are identified to mitigate risks. Conclusions: Generative AI holds significant promise in medical pharmacology education by augmenting learning and engagement. Yet, educators and students must remain vigilant about AI hallucinations. A balanced approachleveraging AI's benefits while implementing safeguards and ethical guidelinesis essential to ensure that AI becomes a reliable adjunct in pharmacology education rather than a source of misinformation. Ongoing research, policy development, and training are needed to fully realize generative AI's potential in medical education while minimizing its risks.

Purpose Generative Artificial Intelligence (GenAI) is rapidly transforming business strategy, innovation processes and governance practices. While prior research has focused primarily on technological implementation and performance outcomes, limited attention has been paid to how senior executives interpret, frame and adapt to GenAI as a strategic and ethical phenomenon. This study aims to explore executives’ sensemaking and adaptive responses to GenAI and to develop a conceptual model that captures this process. Design/methodology/approach The study adopts a qualitative exploratory design based on semi-structured interviews with 15 senior executives from diverse industries in Greece. Data were analyzed using thematic analysis to identify recurring patterns in executives’ perceptions, decision rationales and ethical considerations related to GenAI adoption and integration. Findings The analysis reveals four interrelated dimensions shaping executive adaptation: strategic integration and decision-making, business value and innovation, human–AI collaboration and workforce transformation and ethics, governance and adoption barriers. Cross-thematic synthesis indicates that executives perceive GenAI as a decision-support and innovation amplifier rather than an autonomous decision-maker. These findings inform an Emergent Integrative Model of Executive Adaptation to GenAI, conceptualized as a dynamic cycle comprising strategic sensemaking (Curate), operational experimentation (Create) and ethical consolidation (Consolidate). Research limitations/implications Several executives cited technical and resource limitations as obstacles to effective GenAI implementation. Challenges include legacy systems, lack of skilled artificial intelligence (AI) engineers and limited integration between AI platforms and enterprise software. Practical implications This model advances understanding of executive cognition and adaptive intelligence in the AI era, positioning leadership as a process of continuous learning, sensemaking and ethical stewardship. Practically, the research offers a roadmap for organizations and policymakers for aligning GenAI-driven innovation with responsible governance and leadership development. Social implications By highlighting the role of executive stewardship, the study underscores how ethical leadership in GenAI adoption influences public trust, workforce well-being and organizational legitimacy. Originality/value Existing research on AI in business has predominantly focused on technological implementation, efficiency gains and economic outcomes. Studies emphasize measurable benefits such as productivity enhancement, improved decision-making speed and customer experience optimization. However, fewer studies explore how executive cognition and strategic reasoning shape the trajectory of AI adoption – particularly regarding GenAI technologies that introduce new forms of creative automation. The study advances leadership and sensemaking research by shifting the focus from GenAI adoption outcomes to executive cognition and ethical stewardship. It offers a novel integrative model that explains how strategy, innovation and governance co-evolve in the GenAI era.

The article is devoted to the study of the application of artificial intelligence technologies in law enforcement activities in Ukraine and the world. The purpose of the article is to analyze the international and domestic experience of using digital technologies in law enforcement activities, to identify the shortcomings of the legal regulation of the use of artificial intelligence in this area, to provide proposals for its improvement in Ukraine. It was found that one of the countries that is actively expanding the use of artificial intelligence and successfully introducing relevant technologies into public administration is China. The artificial intelligence system which is used in China called «Zero Trust». The purpose of using this system is to prevent the commission of corruption offenses. It is emphasized, that the disadvantage of the «Zero Trust» system is that artificial intelligence, identifying the cause of corruption, does not indicate the algorithms and principles of this process. At the same time, the experience of using artificial intelligence in the field of preventing and combating corruption in Ukraine is considered when carrying out automated verification of persons declarations of authorized to perform state and local government functions. Ways of improving legal regulation when conducting financial control of declarations using artificial intelligence technologies are proposed. It has been found that artificial intelligence is widely used in the USA to predict the prevalence and state of crime. It was emphasized that Ukraine also has experience in the application of artificial intelligence technologies in law enforcement activities. Artificial intelligence is used in the development and implementation of departmental specialized intelligent software, the implementation of operative and investigative actions (for the recognition of video objects observation by comparing images in social networks, etc). It was concluded that artificial intelligence and digital technologies help to quickly process large volumes of information in the investigation and prevention of criminal activity. Modern developments in the field of artificial intelligence and advanced digital technologies, which are implemented in foreign countries to prevent and fight crime, are of great importance for the improvement of legal regulation in this field in Ukraine.

Teacher education students play double role as present learners and future educators. Hence, they need targeted training to navigate the growing influence of Generative Artificial Intelligence (GenAI) on teaching, learning and professional identity. However, existing artificial intelligence (AI) literacy programmes predominantly emphasize technical AI knowledge and pre‐GenAI tools or are offered by GenAI platforms that focus on their own technologies' features, thereby lacking pedagogically structured frameworks that address non‐technical dimensions such as ethics, contextual understanding and the development of human‐centered, critical and lifelong learning mindsets to adapt to rapid technology changes. Furthermore, limitation of time in teacher education syllabus and faculty's lack of AI literacy require targeted intervention. This design‐based research (DBR) aims to fill this gap by developing and evaluating a set of design principles for GenAI literacy training in teacher education. Integrating contemporary AI competency frameworks for learners and teachers, the study implements these principles in a workshop that serves as the research prototype. The prototype was first piloted with 14 master's students and then evaluated with 29 teacher education (TE) students. Results show significant gains in participants' AI competence self‐efficacy, attitudes towards GenAI and commitment to critical, ethical and pedagogical engagement with GenAI tools. The findings highlight the need for teacher education programmes to integrate GenAI literacy that supports teachers' evolving roles as reflective practitioners, co‐creators and lifelong learners in an AI‐driven world. Practitioner notes What is already known about this topic Students' self‐efficacy and mindset influence their adoption of GenAI in learning and teaching. GenAI competency is essential for teacher education students, encompassing technological, ethical and sociocultural dimensions. Implementing AI literacy training faces challenges such as educators' limited AI literacy, the lack of pedagogical frameworks and the need to integrate technical knowledge with ethical reasoning, self‐regulated learning and metacognitive skills. Transformative learning is crucial in preparing teacher education students as co‐creators and facilitators in AI‐enhanced education. What this paper adds This study presents the design and evaluation of an AI competency training programme to enhance teacher education students' self‐efficacy, critical and human‐centered AI mindsets and human–AI interaction skills. Using design‐based research, it identifies key principles for AI literacy training, demonstrating how transformative, active and experiential learning approaches can prepare teacher education students for evolving roles in AI‐enhanced education. Implications for practice and/or policy Educational technologists can apply the proposed design principles to develop GenAI tools and AI systems that enhance AI competency and self‐regulated learning (SRL). Educators can integrate these AI training principles into their teaching strategies and enhance their task assignments and assessments to foster deep learning, self‐regulated learning and metacognitive skills in the age of GenAI. Policymakers should update teacher education curriculum to include AI competency, strengthen content and pedagogical knowledge, especially in social emotional learning, formative and authentic assessment methods and establish guidelines for selecting GenAI tools that support human intelligence and mitigate over‐reliance.

This study investigates the key factors influencing the acceptance of artificial intelligence (AI) technologies among university students in Oman. Despite global advancements in AI integration within education, limited research has explored how students in the Middle East, particularly in Oman, perceive and adopt AI applications in higher education. Grounded in the Technology Acceptance Model (TAM), the study examines the effects of perceived usefulness, perceived ease of use, perceived trust, perceived attitude, and perceived social influence on actual AI usage. A quantitative research design was employed, utilising a structured questionnaire distributed to 310 students from Sohar University, from which 200 valid responses were collected. Data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results demonstrate that the model explains 81% of the variance in actual AI use (R² = 0.810), confirming the strong predictive power of the TAM constructs. Perceived trust emerged as the most significant predictor, positively influencing both perceived usefulness (β = 0.450) and perceived ease of use (β = 0.613). Perceived ease of use also positively impacted usefulness (β = 0.343) and showed a medium effect size (f² = 0.293). The findings underscore the importance of fostering trust, designing intuitive AI tools, and leveraging peer influence to support AI adoption in Omani universities. The study offers practical implications for educators, developers, and policymakers and contributes to the growing body of literature on AI acceptance in non-Western educational contexts.