Educational institutions worldwide face mounting challenges in effectively integrating artificial intelligence (AI) technologies into their operations, largely due to the absence of comprehensive frameworks for evaluation and implementation. This systemic gap has led to fragmented adoption practices, missed opportunities for innovation, and potential risks in deployment of AI solutions. The rapid proliferation of AI technologies in higher education has created significant challenges for institutional leaders who must balance technological advancement with educational outcomes, ethical considerations, and resource constraints. This study addresses these critical challenges by developing a comprehensive taxonomy of AI applications in higher education leadership. Through a systematic literature review and inductive analysis of publications from 2017 to 2024, the research synthesizes diverse AI applications into ten distinct domains: Administrative Efficiency, Personalized Learning, Enhancing Teaching Practices, Decision-Making and Policy Formulation, Student Support Services, Organizational Leadership and Strategic Planning, Governance and Compliance, Community Engagement and Communication, Ethical AI Leadership, and Diversity, Equity, and Inclusion Initiatives. The resulting taxonomy, validated across various higher education contexts, provides educational leaders with a structured framework for understanding, evaluating, and implementing AI solutions in their institutions. This study contributes to the field by offering a common language and conceptual framework for researchers, policymakers, and practitioners, while also identifying critical areas for future research. The findings underscore the transformative potential of AI in higher education and the need for a balanced approach that leverages technological advancements while addressing ethical considerations and equity issues.

Purpose: – Presents organizations with issues to consider regarding inclusion and its importance as well as a well-established index that companies can join in order to make sure that equality is not a mere policy but that it counts in reality in every organization. Design/methodology/approach: – Describes the power that an organization can derive from deliberately nurturing and integrating heterogeneous groups of people so that they fit together. Findings: – Explains that the best way for an organization to succeed is to combine employee knowledge, skills, judgment and attitudes, alongside fair treatment. Practical implications: – Provides details of an index that aims to measure the efforts of organizations to tackle discrimination and create an inclusive workplace for lesbian, gay and bisexual employees. Social implications: – Reveals that the method of the index should allow organizations already successfully implementing diversity strategies acknowledgement while, at the same time, providing inspiration to those beginning to tackle diversity. Originality/value: – Demonstrates that diversifying the workforce must be accompanied by inclusivity and equality in order to triumph.

Purpose: This paper examines the integration of AI-enhanced coaching systems into traditional organizational development practices and develops a comprehensive theoretical framework to understand these complex interactions. Design/methodology/approach: A narrative literature review synthesizes theoretical perspectives and empirical studies at the intersection of digital transformation and coaching practices. This review systematically examines multiple theoretical frameworks related to human–technology collaboration in organizational contexts. Findings: The research establishes a theoretical framework comprising four interconnected domains: cognitive amplification, collaborative learning, ethical governance and adaptive evolution. Three hypothetical case studies demonstrate the practical application of this framework across diverse organizational settings, revealing both challenges and opportunities in implementing AI-enhanced coaching initiatives. Practical implications: Organizations implementing AI-enhanced coaching must develop comprehensive integration strategies, establish robust ethical governance frameworks, transform coaching roles, assess organizational readiness, design balanced coaching programs, develop new evaluation metrics and establish appropriate supervision mechanisms. Originality/value: This paper offers the first comprehensive theoretical framework specifically addressing the integration of AI technologies in coaching practices, synthesizing distributed cognition theory, complex systems thinking, and social cognitive theory to provide a foundation for future research and practice in this rapidly evolving field.

This article aims to inspire international human resource management (IHRM) scholarship that incorporates postcolonial feminist theory, using the under-researched topic of paternalistic leadership and gender to illustrate the opportunities and challenges such an endeavour can present. Paternalistic leadership is an important and widely used indigenous framework for examining leadership in Chinese contexts, while postcolonial feminism holds the capacity to problematise the representation of non-western women in feminist theory as a universal, transhistorical category. As this article demonstrates, postcolonial feminist theory centralises cultural difference in theorising gender, strives toward shattering binaries reproduced by colonialism and imperialism (West/East, Western/Third World Woman) and generates indigenous, localised knowledge on non-western women. Three sites of enquiry are discussed: 1) Chinese feminisms and genders; 2) Chinese cultures and gender norms; 3) Voice, agency and the subaltern woman. This article provides research propositions for IHRM scholars to translate postcolonial femininst theory into research on paternalistic leadership and gender. Concluding, the article outlines implications for practice and sifts the discussion for the future avenues of research it signals.

This chapter explores the development of digital leadership skills essential for navigating AI-driven research environments. It examines the changing landscape of research and education in the context of AI integration, identifying key competencies required for effective digital leadership. These include visionary thinking, technological proficiency, data literacy, and innovation management. The chapter provides strategies for cultivating these capabilities through training programs, mentorship, and active participation in AI-related events. It also addresses ethical considerations in AI leadership, exploring issues of bias, privacy, and inclusivity. Theoretical implications of digital leadership in AI are discussed, drawing on established leadership theories. Practical strategies for implementing AI leadership are presented, along with case studies of successful AI integration. The chapter concludes by identifying future research avenues and emphasizing the critical role of digital leadership in shaping the future of AI-driven research.