Higher Education Leadership

Leadership style is the general way a leader behaves towards his subordinates for attaining objectives. There are two major dimensions of leadership i.e. transformational leadership and transactional leadership.The objective of the present study was to find out the relationship between leadership styles of principals and job satisfaction of college teachers. In this connection, survey research method was used.The population for this study comprised all teachers of public degree colleges of Punjab (One of the provinces of Pakistan). By using random sampling technique 10 % of the colleges were taken as sample. There were 43 colleges excluding the mixed colleges and from sample colleges five teachers from each college were selected as sample. For collecting data from teachers two questionnaires were used.One questionnaire was used by the researchers for teachers to rate leadership styles of their heads. This questionnaire was based upon seven indicators. The first four variables dealt ...

Higher education needs leadership models that help faculty and academic units adopt artificial intelligence in ways that are ethical, collaborative, and centered on learning. This conceptual article proposes an integrative leadership model that combines four well-established traditionstransformational, servant, inclusive, and ethical leadership-to guide AI use in teaching and learning. The model links leadership mindsets and behaviors to two outcomes that matter for teaching and learning: ethically aligned decision-making and authentic collaboration among faculty, students, and staff. Leadership approaches are synthesized and developed into design principles for curriculum, assessment, and classroom practice. Theoretical propositions connect leadership behaviors to the promotion of ethical teaching and learning and to authentic collaboration, while practical implementation tools for AI use are outlined for course teams and academic leaders. This framework emphasizes the role of departments, teaching and learning centers, and program leaders in balancing innovation with responsibility. The article contributes clear, practical steps for leading AI adoption that protect academic integrity, support inclusion, and foster a culture of shared teaching and learning responsibility (

The integration of artificial intelligence (AI) into higher education has reshaped teaching and learning through adaptive platforms, predictive analytics, automated grading, and administrative decision-making. While these technologies offer efficiency and personalization, they also present risks of systemic bias that threaten equity, fairness, and institutional integrity. Addressing these risks requires leadership strategies that extend beyond technical solutions to include ethical governance and accountability. This qualitative archival research examines how higher education leadership guided by Path-Goal Theory addresses systemic bias in AI-powered teaching and learning. Path-Goal Theory, developed by House (1971), emphasizes directive, supportive, participative, and achievement-oriented leadership behaviors that clarify goals, remove barriers, and align institutional practices with follower needs. Archival analysis of institutional documents from Harvard University, Maricopa Community Colleges, and the University of Southern Maine demonstrates that higher education leadership employs these behaviors to varying degrees in establishing AI governance. Findings indicate that directive leadership provides clear policies and accountability, supportive leadership aids faculty adaptation, participative leadership engages diverse stakeholders, and achievement-oriented leadership promotes ethical innovation. However, gaps remain in the balanced application of these behaviors, particularly in sustaining supportive and achievement-oriented practices. This study contributes to higher education leadership by linking Path-Goal Theory to the ethical governance of AI, offering a framework for leaders to mitigate systemic bias while advancing institutional missions. Implications highlight the need for professional development, inclusive stakeholder engagement, continuous review, and goal-setting practices that ensure AI adoption in higher education remains equitable, responsible, and mission-driven. Chapter 1: Introduction The integration of artificial intelligence (AI) into higher education has transformed how teaching and learning are designed, delivered, and assessed. Universities and colleges are increasingly

When colleges and universities are asked to embrace AI, they must answer a critical question: how can leaders balance innovation while upholding ethical standards, fostering equity, and maintaining faculty trust? Leadership plays a decisive role in shaping how AI adoption occurs, requiring approaches that align technological innovation with institutional values. This study explores how higher education leaders can design leadership frameworks that ethically and inclusively guide AI integration. Based on transformational, servant, inclusive, and Leader-Member Exchange (LMX) leadership theories, the study examines how leaders implement vision, engage faculty, promote equitable access, and sustain ethical practices in AI-enhanced learning environments. A multi-component framework is proposed, encompassing strategic vision, stakeholder engagement, capacity building, policy and ethics, change management, and evaluation and feedback. Each component is aligned with leadership theories to provide actionable strategies that address faculty preparedness, institutional governance, and equitable student outcomes. The framework emphasizes continuous improvement through feedback cycles, ensuring that AI adoption evolves responsibly while maintaining alignment with higher education's mission. By combining theoretical perspectives with practical applications, this study offers higher education leaders evidence-based strategies for balancing innovation with equity and ethical responsibility.

This framework explores respect as both a universal entitlement and a relational practice. It introduces a dual approach: one perspective affirms that all individuals deserve respect by virtue of their humanity, while the other recognizes that respect can also be earned through ethical behavior, empathy, and accountability. Additionally, these perspectives offer a practical model for leadership, education, and organizational culture-where respect must be both guaranteed and cultivated. Based on ethical theory, organizational psychology, and real-world case studies, the framework provides tools for understanding how respect operates across roles, relationships, and institutional settings. It invites leaders and educators to build environments where dignity is protected, trust is deepened, and respect becomes a shared responsibility.

Emotional intelligence should be regarded as a fundamental component of effective leadership because it enables leaders to foster trust, understand and respond to the needs of their team members, and make ethical decisions that respect human dignity. This discussion examines the moral shortcomings that occur when leaders neglect empathy, overlook the suffering of others, and manipulate performance data to serve organizational interests. Ethical leadership involves a sense of moral responsibility, the cultivation of inclusive environments, and a deep respect for human dignity. In the absence of emotional awareness and sensitivity, leadership risks compromising ethical principles, thereby transforming into a moral challenge rather than a moral authority. A friend of mine lives with chronic pain. Some days, she can't concentrate. Her body is in constant conflict with itself. And still, she shows up.

This conceptual study examines the long-term integration of artificial intelligence (AI) in higher education teaching and learning through the perspectives of Transformational and Servant Leadership. It emphasizes how visionary and ethical leadership practices shape sustainable, inclusive, and faculty-centered AI adoption over time. By synthesizing contemporary empirical research and leadership theory, the study highlights strategies that support equitable student engagement, faculty autonomy, and adaptive institutional practices. The findings show the importance of combining transformational vision with servant-oriented ethical care to foster long-term innovation while maintaining humancentered values. Recommendations for higher education leaders include implementing leadership frameworks that balance technological advancement with ethical responsibility, promoting continuous faculty development, and ensuring inclusive and emotionally responsive learning environments. This study contributes to higher education leadership by providing a conceptual blueprint for guiding sustainable and ethically grounded AI integration.

Background: The integration of artificial intelligence (AI) into higher education has created new challenges for leadership, particularly in areas of governance, ethical responsibility, and institutional identity. Universities are expected to balance innovation with accountability while ensuring that leadership decisions reflect both organizational values and public trust.

This review examines Olson and Presley's Future of Higher Education, which explores contemporary leadership challenges faced by American colleges and universities through a collection of essays by experienced academic leaders. The book emphasizes the importance of ethical governance, democratic values, and strategic foresight in responding to organizational change amid demographic shifts, technological advancements, and increasing demands for accountability. It presents diverse leadership perspectives based on practice, emphasizing mission-driven decision-making and inclusive values. The review also identifies areas for further development, including greater attention to underrepresented student populations and systemic barriers. Additionally, it discusses the relevance of the book's insights to current leadership practices, particularly in managing technological innovations such as artificial intelligence, exemplified by the UC AI Council's initiatives.

During the recent years Jordan has witnessed an increasing awareness of women"s issues. Most the successive Jordanian governments were publicly committed to integrating women into the development process, and eliminating all types of discrimination against women and could succeeded in much aspect of women"s life, but not on the case of higher education management and top leading posts. therefore this came to critically investigate the women status of women in higher education, to reach this end the study used a descriptive research methodology ,depending in on published data as we as data collected by the researchers from the universities itself. The study found that there is paradoxical situation in Jordanian higher education system, when female students forming more than 50% of the total students enrolled in higher education institutions, we find very few women holding managerial and leadership posts. Contribution/ Originality: This study is one of the few studies which have critically investigated the situation of women in higher education management and leadership positions. The primary contribution of this study is to draw policy makers' attention to the fact that women are underrepresented in higher education governing bodies.

During the recent years Jordan has witnessed an increasing awareness of women"s issues. Most the successive Jordanian governments were publicly committed to integrating women into the development process, and eliminating all types of discrimination against women and could succeeded in much aspect of women"s life, but not on the case of higher education management and top leading posts. therefore this came to critically investigate the women status of women in higher education, to reach this end the study used a descriptive research methodology ,depending in on published data as we as data collected by the researchers from the universities itself. The study found that there is paradoxical situation in Jordanian higher education system, when female students forming more than 50% of the total students enrolled in higher education institutions, we find very few women holding managerial and leadership posts. Contribution/ Originality: This study is one of the few studies which have critically investigated the situation of women in higher education management and leadership positions. The primary contribution of this study is to draw policy makers' attention to the fact that women are underrepresented in higher education governing bodies.

This conceptual study examines Grand Canyon University's (GCU) institutional approach to artificial intelligence (AI) integration from the perspective of higher education leadership. The study focuses on faculty support, ethical practice, and inclusive curriculum development as responsibilities of leadership. Current guidelines provide instructions for faculty but do not establish a strategic framework to ensure consistent, ethical, and inclusive implementation. The absence of structured leadership practices has led to variability in faculty approaches, ethical concerns, uneven adoption, and inconsistent student outcomes. Based on transformational, servant, and adaptive leadership theories, the study proposes a conceptual framework that recognizes provosts, deans, and faculty developers as leaders guiding AI adoption across disciplines. The framework explores faculty empowerment through professional development, ethical guidance, shared decision-making, and curriculum design that incorporates AI into inclusive teaching and learning practices. By examining current gaps and building on established leadership theories, the study proposes a model for responsible AI integration that protects academic integrity and supports equitable learning outcomes.

Background: Artificial intelligence (AI) adoption in higher education, particularly in defensefocused institutions, offers potential for streamlining decision-making and resource management but also raises challenges related to inclusivity, transparency, and accountability. Effective governance requires leadership that articulates vision, engages stakeholders, and upholds ethical standards. In military-focused academic environments, these challenges are intensified by regulatory requirements, the mission, and public trust. Purpose: This study examines how leadership practices at the Defense Acquisition University (DAU) influence the design, implementation, and effectiveness of inclusive AI governance frameworks. It explores how transformational leadership behaviors-such as demonstrating ethical standards, setting a clear vision, and actively responding to stakeholder needs-shape stakeholder experience and institutional credibility. Methods: A qualitative archival approach was employed. Documents-including DAU's AI statutes, policies, and guidance-provided insights into ethical AI use, leadership accountability, and institutional compliance. Narrative analysis identified themes related to leadership influence, stakeholder engagement, transparency, benchmarking, and governance effectiveness. This method revealed how leaders communicate institutional values, interpret policy in a military-academic setting, and implement AI-related guidance in ways that support inclusive and practical governance. These aspects of leadership are rarely captured in technical reports or survey-based studies. Findings: DAU leadership demonstrates practices that build trust and support fairness in AI governance. Structured stakeholder engagement and benchmarking improve policy legitimacy. The study also reveals the dual accountability leaders face, which shapes how AI is introduced, regulated, and sustained. Conclusions: Inclusive, accountable leadership is essential for ethical AI adoption in defensefocused institutions.

This article examines the effectiveness of private versus public feedback in professional, educational, and interpersonal settings. It argues that private feedback is often more effective because it protects dignity, reduces defensiveness, and strengthens trust, thereby encouraging openness to improvement. Public feedback, while potentially beneficial for reinforcing group norms or addressing urgent issues, carries risks if it diminishes respect or causes embarrassment. This article helps readers understand that leaders should consider the situation, the relationship, and the individual traits of the person receiving feedback when deciding how to deliver feedback. The article contributes to the ongoing conversation on effective feedback practices by integrating insights from communication, organizational behavior, and leadership.

This presentation explores how the Chief Diversity Officer (CDO) actively shapes institutional decision-making in higher education in the context of artificial intelligence, emphasizing ethical leadership, algorithmic equity, and the advancement of diversity and inclusion. It explores how AI-driven systems shape key institutional processes—including admissions, faculty evaluation, financial aid, housing, library resource allocation, and alumni engagement—and the associated risks to diversity, equity, and inclusion (DEI). The discussion highlights the dual challenge of algorithmic bias and human bias in AI interactions, emphasizing the need for CDOs to implement equity-centered governance, inclusive algorithm design, and campus-wide AI literacy initiatives. By presenting practical strategies and guiding principles, this presentation contributes to the development of ethical leadership frameworks that embed DEI values into AI deployment, ensuring that technological innovation supports both institutional goals and social justice.
Keywords: Chief Diversity Officer, artificial intelligence, higher education leadership, diversity, equity, inclusion, algorithmic governance, ethical leadership

This framework presents a leadership approach, structured as a model, built on seven principles for integrating artificial intelligence into higher education. It defines integrity as the primary guiding principle, with AI serving as a tool and leadership providing direction. The framework emphasizes ethical use, transparency, equity, and leadership accountability, ensuring that technological adoption supports institutional missions, strengthens academic trust, and promotes student success. It can be applied in policy formulation, administrative operations, academic planning, and classroom practice. The framework offers a structure for guiding research and practice in ways that safeguard institutional values while enabling informed and responsible innovation.

Graduate Teaching Assistants (GTAs) are key partners in the education of undergraduates. Given the potentially large impact GTAs can have on undergraduate student learning, it is important to provide them with appropriate preparation for teaching. But GTAs are students themselves, and not all of them desire to pursue an academic career. Fully integrating GTA preparation into the professional development of graduate students lowers the barrier to engagement so that all graduate students may benefit from the opportunity to explore teaching and its applications to many potential career paths. In this paper we describe the design and implementation of a GTA Preparation course for first-year Ph.D. students at the Georgia Tech School of Physics. Through a yearly cycle of implementation and revision, guided by the 3P Framework we developed (Pedagogy, Physics, Professional Development), the course has evolved into a robust and comprehensive professional development program that is well-received by physics graduate students.

Graduate teaching assistants (GTAs) are key partners in the education of undergraduates. Given the potentially large impact GTAs can have on undergraduate student learning, it is important to provide them with appropriate preparation for teaching. But GTAs are students themselves, and not all of them desire to pursue an academic career. Fully integrating GTA preparation into the professional development of graduate students lowers the barrier to engagement so that all graduate students may benefit from the opportunity to explore teaching and its applications to many potential career paths. In this paper we describe the design and implementation of a GTA preparation course for first-year Ph.D. students at the Georgia Tech School of Physics. Through a yearly cycle of implementation and revision, guided by the 3P framework we developed (pedagogy, physics, professional development), the course has evolved into a robust and comprehensive professional development program that is well received by physics graduate students.

This article explores how emotionally intelligent Black women in higher education leadership manage challenges related to algorithmic bias within institutional data systems. It highlights the subtle ways algorithm-driven decision tools-often seen as neutral-can reinforce exclusion and retaliation. The discussion focuses on the emotional labor involved in interpreting and responding to hidden biases, emphasizing the need for greater transparency, accountability, and inclusive system design to address systemic inequities. This paper contributes to ongoing conversations about race, gender, and technology in academic leadership by shedding light on often invisible experiences and advocating for institutional change.

In the United States, higher education leaders are increasingly responsible for ensuring that the adoption of artificial intelligence (AI) in admissions and other institutional processes is conducted ethically. While AI has the potential to improve efficiency and decision-making, it also introduces risks, such as algorithmic bias, which can threaten fairness, transparency, and student trust. This qualitative study explores how higher education leadership responds to these challenges by analyzing publicly available institutional documents. Using an archival research approach, the study examines policies from California Institute of Technology, Brown University, and Georgetown University. Guided by theories of transformational leadership (Bass & Riggio, 2006) and frameworks of algorithmic governance (Koene et al., 2019), the findings show that higher education leaders focus on making ethical decisions through standards of integrity, transparency requirements, and proactive measures to prevent bias. Rather than depending only on technological methods to identify bias, leaders focus on human-centered approaches like verifying authorship and creating accountability systems. This research contributes to the understanding of higher education leadership by identifying practical approaches to managing AI adoption, offering insights for leadership training, policy development, and future research on governing AI in higher education institutions.

In Thailand, a nation often lauded for its progressive stance on LGBTQ+ issues, university students who identify as LGBTQ+ still navigate a landscape marked by insufficient institutional support. This study explores their social experiences through a dual approach, combining narrative analysis and critical discourse analysis (CDA) based on the framework established by Preston and Hoffman (2015), to uncover the underlying social-psychological, political, and cultural dynamics at play. Semi-structured interviews were conducted with seven LGBTQ+ students, recruited through snowball sampling, and 26 articles from Thai media and university publications were analyzed using CDA. The findings reveal that, despite Thailand's surface-level reputation for tolerance, deeper prejudices persist. These prejudices are often masked by cultural practices like เกรงใจ (kreng jai), which prioritize social harmony over confronting sensitive issues surrounding individual differences and are facilitated by adherence to the concept of เพศ (phet), which contributes to increasingly complicated understandings of gender and sexual identities. As a result, LGBTQ+ students often find themselves marginalized and misunderstood, with their needs overlooked in both academic and social contexts. This research not only sheds light on the complexities of LGBTQ+ student life in Thai universities but also calls for a reevaluation of what true inclusivity means in educational settings. By addressing these challenges, there is potential to transform Thai universities into environments where LGBTQ+ identities are fully supported and embraced.

Levine and Van Pelt (2021) argue that U.S. higher education remains tied to an outdated industrial-era framework characterized by standardized curricula, hierarchical governance, and credentials as the central measure of success. They suggest that these structures no longer meet the needs of a digital and globally connected society. Their critique draws on systems theory and long-range educational analysis, portraying higher education as resistant to needed change. Although The Great Upheaval is not a leadership theory book, its discussion of institutional change can be considered alongside Northouse's (2021) models of adaptive and transformational leadership. These models offer useful perspectives for academic leaders seeking to respond to uncertainty, encourage responsible innovation, and ensure integrity in decision-making during a period of rapid institutional change.

This review examines, "The Five Principles of Collaboration" by Dr. J. Ibeh Agbanyim, focusing on its contributions to collaborative practice. Agbanyim's (2015) book on Five Principles of Collaboration-trust, respect, willingness, empowerment, and effective communication-are presented as fundamental elements for effective collaboration in general (professional and personal environments). However, the book does not explicitly connect these principles to the unique challenges and complexities of higher education leadership. As a scholarly practitioner in the field of higher education, I will review the book by interpreting and analyzing how these principles might be applied or adapted within academic institutions. This approach is necessary because higher education contexts involve stakeholder diversity, shared governance, and systemic hierarchies that shape collaborative processes. The review highlights both the clarity and usefulness of Agbanyim's Five Principles of Collaboration while also considering how they may be adapted to address contemporary challenges in academic institutions.

This work reflects original scholarship supported by AI as a structured tool under my direction, as stated in my author profile. The presentation has been reuploaded to correct formatting and ensure citation integrity. Earlier uploads have been reported; if encountered, they should be disregarded in favor of this updated version. This presentation examines the strategic role of higher education leadership in integrating artificial intelligence (AI) technologies to reinforce shared governance through distributed leadership models. While U.S. colleges and universities increasingly adopt AI in academic and operational contexts, few institutional frameworks account for governance alignment and participatory decision-making. Based upon distributed leadership theory (Gronn, 2002; Spillane, 2006), strategic planning principles (Rowley & Sherman, 2001), and shared governance standards (AAUP, 1966), this inquiry highlights a critical gap in empirical literature: the absence of leadership models that ethically embed AI within collaborative institutional structures. Through analysis of guiding documents from Valencia College, Elon University, and Stephen F. Austin State University, this presentation advances strategic recommendations for academic leaders to develop governance-driven approaches that preserve institutional integrity and accountability. The goal is to position AI as a catalyst for collaborative reform—not as a tool for administrative isolation.
Keywords: Higher education leadership; Distributed leadership models; Shared governance; Artificial intelligence technologies; Strategic planning; Ethical decision-making; Institutional accountability; Organizational leadership; Academic integrity; Leadership frameworks

This work reflects original scholarship supported by AI as a structured tool under my direction, as stated in my author profile. The document has been reuploaded to correct formatting and ensure citation integrity. Earlier uploads have been reported; if encountered, they should be disregarded in favor of this updated version. Artificial intelligence (AI) is no longer a niche innovation—it is a strategic driver in higher education. This presentation highlights why AI integration is a leadership responsibility, not just a technological upgrade. As digital transformation reshapes institutional priorities, AI offers tools for efficiency, personalization, and strategic insight. The purpose of this session is to explore how higher education leadership can adopt ethical, inclusive, and mission-aligned strategies for integrating AI across key areas: strategic planning, enrollment management, instruction, student support, and administrative operations. Using real-world institutional examples, the presentation outlines practical actions leaders can take to build governance structures, promote faculty development, and ensure AI serves the academic mission and student success.

Keywords: Higher Education Leadership, Artificial Intelligence, Strategic Planning, Institutional Effectiveness, Student Support Services, Instructional Assessment, Enrollment Management, Data Governance, Ethical Leadership, Digital Transformation

Artificial intelligence (AI) is no longer a niche innovation—it is a strategic driver in higher education. This presentation highlights why AI integration is a leadership responsibility, not just a technological upgrade. As digital transformation reshapes institutional priorities, AI offers tools for efficiency, personalization, and strategic insight. The purpose of this session is to explore how higher education leadership can adopt ethical, inclusive, and mission-aligned strategies for integrating AI across key areas: strategic planning, enrollment management, instruction, student support, and administrative operations. Using real-world institutional examples, the presentation outlines practical actions leaders can take to build governance structures, promote faculty development, and ensure AI serves the academic mission and student success.
 
Keywords: Higher Education Leadership, Artificial Intelligence, Strategic Planning, Institutional Effectiveness, Student Support Services, Instructional Assessment, Enrollment Management, Data Governance, Ethical Leadership, Digital Transformation

Artificial intelligence (AI) is no longer a niche innovation—it is a strategic driver in higher education. This presentation highlights why AI integration is a leadership responsibility, not just a technological upgrade. As digital transformation reshapes institutional priorities, AI offers tools for efficiency, personalization, and strategic insight. The purpose of this session is to explore how higher education leadership can adopt ethical, inclusive, and mission-aligned strategies for integrating AI across key areas: strategic planning, enrollment management, instruction, student support, and administrative operations. Using real-world institutional examples, the presentation outlines practical actions leaders can take to build governance structures, promote faculty development, and ensure AI serves the academic mission and student success.
 
Keywords: Higher Education Leadership, Artificial Intelligence, Strategic Planning, Institutional Effectiveness, Student Support Services, Instructional Assessment, Enrollment Management, Data Governance, Ethical Leadership, Digital Transformation

Purpose The purpose of this qualitative archival research study was to examine how higher education leadership utilizes analytics and artificial intelligence (AI) to support data-informed strategic planning, institutional policy, and decision-making. The study focused on how institutional leaders position AI and analytics as components of long-term planning efforts within the governance structures of U.S. colleges and universities. Grounded in Adaptive Leadership Theory and the Technology Acceptance Model (TAM), the study aimed to address gaps in literature regarding leadership-driven integration of emerging technologies in higher education. Methodology A qualitative archival research design was employed to analyze publicly available institutional documents published between 2023 and 2025 by three regionally accredited U.S. institutions: Texas A&M University-San Antonio, North Country Community College, and an EDUCAUSEprofiled university recognized for AI governance leadership. Data sources included strategic planning documents, AI policy frameworks, and institutional reports. Thematic analysis was used to identify patterns in how leadership framed AI and analytics as strategic priorities. The theoretical frameworks informed the coding structure and interpretation of institutional strategies. Findings Five key themes emerged from the analysis: (1) strategic prioritization of AI by leadership, (2) messaging of usefulness and accessibility to support institutional adoption, (3) collaborative governance of AI initiatives, (4) integration of analytics into institutional culture, and (5) ethical framing and risk awareness. These findings suggest that higher education leadership is actively shaping institutional readiness for AI through strategic planning, ethical oversight, and crossfunctional collaboration. Adaptive Leadership explained how institutions manage organizational change, while TAM clarified how leaders influence perceptions of AI's value and usability.

This qualitative archival study examined how higher education leadership interprets and responds to ethical dilemmas arising from the use of artificial intelligence (AI) in teaching. It also explored how stewardship informs leadership strategies for promoting academic integrity and fairness, and how institutional accountability is embedded within AI teaching policies and governance. Methodology This research employed a qualitative archival design focused on institutional documents published between 2023 and 2025 by Columbia University, the

This presentation examines the strategic role of higher education leadership in integrating artificial intelligence (AI) technologies to reinforce shared governance through distributed leadership models. While U.S. colleges and universities increasingly adopt AI in academic and operational contexts, few institutional frameworks account for governance alignment and participatory decision-making. Drawing upon distributed leadership theory (Gronn, 2002; Spillane, 2006), strategic planning principles (Rowley & Sherman, 2001), and shared governance standards (AAUP, 1966), this inquiry highlights a critical gap in empirical literature: the absence of leadership models that ethically embed AI within collaborative institutional structures. Through analysis of guiding documents from Valencia College, Elon University, and Stephen F. Austin State University, this presentation advances strategic recommendations for academic leaders to develop governance-driven approaches that preserve institutional integrity and accountability. The goal is to position AI as a catalyst for collaborative reform—not as a tool for administrative isolation.
Keywords: Higher education leadership; Distributed leadership models; Shared governance; Artificial intelligence technologies; Strategic planning; Ethical decision-making; Institutional accountability; Organizational leadership; Academic integrity; Leadership frameworks

This presentation examines the strategic role of higher education leadership in integrating artificial intelligence (AI) technologies to reinforce shared governance through distributed leadership models. While U.S. colleges and universities increasingly adopt AI in academic and operational contexts, few institutional frameworks account for governance alignment and participatory decision-making. Drawing upon distributed leadership theory (Gronn, 2002; Spillane, 2006), strategic planning principles (Rowley & Sherman, 2001), and shared governance standards (AAUP, 1966), this inquiry highlights a critical gap in empirical literature: the absence of leadership models that ethically embed AI within collaborative institutional structures. Through analysis of guiding documents from Valencia College, Elon University, and Stephen F. Austin State University, this presentation advances strategic recommendations for academic leaders to develop governance-driven approaches that preserve institutional integrity and accountability. The goal is to position AI as a catalyst for collaborative reform—not as a tool for administrative isolation.
Keywords: Higher education leadership; Distributed leadership models; Shared governance; Artificial intelligence technologies; Strategic planning; Ethical decision-making; Institutional accountability; Organizational leadership; Academic integrity; Leadership frameworks

This presentation examines the role of Higher Education Leadership in ethically integrating AI-Generated Feedback Systems into instructional environments. As institutions increasingly adopt algorithmic assessment tools, the need for comprehensive Strategic Planning and Institutional Accountability becomes essential to safeguard Instructional Integrity. Anchored in Kotter’s 8-Step Change Model, Ethical Leadership Theory, and Adaptive Leadership, this study highlights leadership perspectives on aligning organizational readiness, governance structures, and ethical oversight in technology-mediated pedagogy. Supported on recent scholarship, the presentation underscores how leadership-informed strategies help mitigate algorithmic bias, ensure transparency, and protect academic standards. Ultimately, further research is required for a leadership approach that centers ethics, responsiveness, and strategic direction in the implementation of AI-driven instructional tools.
Keywords: Higher Education Leadership, Strategic Planning, Institutional Accountability, Instructional Integrity, AI-Generated Feedback Systems, Ethical Leadership Theory, Adaptive Leadership, Kotter’s 8-Step Change Model, Algorithmic Bias, Academic Technology Governance

AI systems designed to support student learning are no longer peripheral-they are integrated components of modern higher education classrooms. Leadership structures have struggled to keep pace with the ethical demands of algorithm-mediated instruction. This article examines the widening gap between technological adoption and ethical oversight, arguing that the implementation of AI-driven learning systems necessitates strategic leadership-not merely technical deployment. Grounded in Servant Leadership Theory, the study positions higher education faculty as ethical stewards tasked with ensuring instructional integrity, correcting algorithmic outputs, and sustaining relational engagement with students. Through a synthesis of recent empirical literature and leadership frameworks, the manuscript outlines actionable pathways for empowering instructors, embedding moral accountability, and cultivating a leadership culture that integrates human judgment alongside intelligent systems. The findings suggest that in environments where algorithms shape teaching and learning, servant leadership offers a vital governance model to preserve institutional care, transparency, and pedagogical values. AI tutors may provide efficiency, but only human educators-and the leaders who empower them-can safeguard the ethics of learning.

Higher education leadership is increasingly called upon to define strategic planning and change initiatives in response to emerging technologies such as AI-enabled grading tools in academic assessment. This qualitative archival study investigates how higher education leadership strategies are reflected in three public higher education institutional policy documents published between 2023 and 2025. The study explores how leadership approaches to strategic planning, change, ethical decision-making, and institutional policy development are conveyed in these documents. Using qualitative content analysis, the research was guided by Institutional Theory, Kotter's 8-Step Change Model, and Ethical Leadership Theory to examine how higher education leadership addresses policy development, strategic planning and change and ethical decisionmaking in the use of AI-enabled grading tools. Findings reveal higher education leadershipcentered strategies for guiding institutional change, articulating policy direction, and upholding responsibility for ethical oversight. This study contributes to the academic literature by offering a higher education leadership-centered interpretation of strategic planning, change, and ethical responsibility in the adoption of AI-enabled grading tools within higher education institutions.

Purpose and Problem Statement This study explores how higher education leadership in U.S. universities documents institutional decision-making and communication regarding artificial intelligence in teaching and learning. It examines how leadership balances humancentered values and AI-supported instructional roles through written policies and governance frameworks. Despite AI's growing influence, limited research has addressed how leadership reflects priorities such as clarity, accuracy, scholarly rigor, strategic planning and change, and academic integrity in formal institutional texts. Research Design Using qualitative document analysis, the study reviewed leadership-authored texts published between 2023 and 2025, including AI policy statements, strategic planning reports, faculty senate communications, and academic integrity guidelines. Documents were selected for their explicit focus on instructional decision-making, ethical governance, and technology integration. The design emphasized how higher education leaders formalize institutional direction and core values through written policy. Theoretical Frameworks The study applied Ethical Leadership and Adaptive Leadership frameworks to interpret how leaders guide institutional strategy and ethical oversight in response to AI integration. Findings Documents reflected leadership priorities across five constructs: clarity, accuracy, scholarly rigor, strategic planning, and academic integrity. While some institutions articulated comprehensive AI policy frameworks, others lacked specificity. Leadership communication demonstrated varied depth and alignment with responsible innovation and instructional governance. Implications Findings affirm the central role of higher education leadership in shaping ethical and strategic responses to artificial intelligence in teaching and learning. Leaders must enhance policy clarity, uphold academic standards, and apply leadership principles to guide AI integration across instructional contexts.

In an era defined by constant disruption and the decentralization of work, traditional hierarchical models of leadership are proving insufficient for navigating complex strategic and organizational change. This review article examines the increasingly critical role of informal leadership and the concept of "silent authority"-a form of influence derived not from formal position but from trust, expertise, and social capital. We argue that this subtle yet potent authority is a decisive factor in the success or failure of strategic initiatives and organizational transformations. The article begins by defining informal leadership and silent authority, grounding them in theories of social capital, social networks, and emergent leadership. It then explores the dual role of informal leaders as both crucial sense-makers and implementers of strategy, and as powerful catalysts or inhibitors of organizational change. The analysis reveals how these individuals translate executive vision into frontline action, mobilize discretionary effort, and provide critical upward feedback, yet can also orchestrate formidable under-the-radar resistance when misaligned with organizational goals. Furthermore, the article synthesizes recent literature on how formal leaders can ethically and effectively identify and engage with these influential figures without undermining the authenticity that is the source of their power. Finally, we discuss the challenges and risks associated with informal power structures and propose a forward-looking research agenda focused on the dynamics of silent authority in remote/hybrid work environments and the impact of digital collaboration platforms. This review concludes that for organizations to achieve true agility and resilience, they must move beyond a focus on the formal chain of command and learn to cultivate and listen to the resonant power of their silent authorities.

From a philosophical viewpoint and a technical perspective, this paper claims that learning organizations are the most effective organizations which 1) demonstrate transformative leadership practices, 2) work to instigate a learning-based organizational environment, and 3) continually promote learning behavior at all levels, to name a few of its key characteristics. Like other organizations, educational organizations would exhibit individual behavior, collective actions and interactions which define their norms and values. The purpose of this paper is to explore: what it means to be a learning organization; what are and should be its key characteristics; and how to create a learning organization. The findings of this study show that learning organizations are characterized by a collective struggle to achieve organizational goals, where  culture and climate concerns become paramount, and learning is viewed as part of everyone’s business regardless of individual differences, roles, an...

Over the last decade, Zimbabwe has faced a severe economic quagmire which impacted on all spheres of life for both individuals and organizations. Universities were not spared either. Resource accumulation and availability from the environment at the University of Zimbabwe became a major challenge leading to a decline of quality of teaching and learning. Resource Dependence Theory advocates that organizations (universities included) are dependent on the environment for resources for survival and achievement of their set objectives which includes the quality in teaching and learning. This study investigated the critical resource related factors that influenced quality of teaching and learning at the University of Zimbabwe. A case study approach with University of Zimbabwe as the case was used to study the factors that influenced the quality of teaching and learning in all the ten faculties at the institution. Ten department chairpersons and ten academics participated through interview...

She is responsible for programs at the pre-college, undergraduate, and graduate levels to facilitate the recruitment, retention, and overall success of students from traditionally underrepresented groups in engineering and information and computer sciences. Dr. Artis has 18 years of experience working with education and outreach programs in engineering and over 35 publications in STEM education and outreach. Prior to joining UC Irvine, she was the Education and Outreach Director for the Center for Energy Efficient Electronics Science at the University of California, Berkeley. Previously, Dr. Artis spent nine years at Virginia Tech providing program and student support for the Center for the Enhancement of Engineering Diversity and has four years of industry and government experience as a Human Factors Engineer. Dr. Artis holds a B.S., M.S., and Ph.D. in Industrial and Systems Engineering from Virginia Tech.

The rise of generative artificial intelligence (AI) in higher education has unsettled established norms of authorship, ownership, and institutional responsibility. As AI tools become embedded in academic research and teaching, higher education leaders face an urgent mandate to define intellectual labor in this new context. This qualitative study explores how leadership authority is expressed-or obscured-through institutional authorship policies related to AI-generated academic work. Guided by the theoretical anchors of Distributed Leadership and Ethical Governance, the research examines 30 publicly available policy documents, including faculty handbooks, AI guidelines, and academic integrity statements from U.S. and international institutions. Using qualitative document analysis and AI-assisted natural language processing, the study uncovers patterns of procedural silence, rhetorical ethics, and fragmented governance. Key findings reveal that institutions often distribute responsibility without accountability, invoke ethical values without enforcement mechanisms, and avoid clear definitions of authorship in AImediated contexts. This research contributes to the literature on higher education leadership by framing policy texts as sites of leadership performance and omission, offering an interpretive model of institutional silence as a leadership decision. It calls for institutions to reclaim authorship policy as a matter of legitimacy, labor protection, and ethical governance in the age of AI.

The accelerating integration of generative artificial intelligence (AI) into higher education has exposed unresolved tensions around intellectual property ownership, authorship attribution, and leadership responsibility. This qualitative study explores how academic leaders interpret their roles in navigating the legal, ethical, and organizational implications of AI-generated scholarly output. The research addresses two central questions: (1) How do higher education leaders define their authority in governing AI-generated academic content? and (2) What institutional policies and values shape their decision-making regarding intellectual property? Grounded in the Rational Decision-Making Model and Bounded Rationality Theory, the study analyzes perspectives from deans, provosts, and faculty chairs at research-intensive universities. Semistructured interviews and policy document analysis were employed to capture leadership reasoning in institutional contexts where AI tools are increasingly embedded in teaching and research. The findings reveal a spectrum of uncertainty, with leaders acknowledging the legal ambiguity of ownership, expressing concern over diminished human oversight, and calling for clearer frameworks to define institutional versus individual rights. Many participants viewed AI as a disruptive force requiring new forms of leadership legitimacy and accountability. This research contributes to the organizational leadership literature by advancing a theory of "Distributed Intellectual Accountability" in the context of algorithmic authorship, offering practical insights for policy reform, ethical governance, and leadership development in AImediated academic environments.

This qualitative meta-synthesis explores the institutional and human readiness gaps that undermine efforts to embed artificial intelligence (AI) tools into higher education curriculum. Drawing on peer-reviewed studies and expert reports from 2021 to 2025, the study analyzes faculty confidence, curriculum redesign efforts, and leadership responses using Bolman and Deal's (2017) Four-Frame Model of Leadership. The findings reveal a persistent disconnect: while AI tools proliferate in classrooms, faculty and leaders alike lack the training, structural support, and cultural alignment to use them meaningfully. Symbolic adoption often masks deeper issues of exclusion, unpreparedness, and ethical ambiguity. This research offers a reframed understanding of AI integration-not as a technical implementation challenge, but as a leadership crisis requiring emotional intelligence, ethical clarity, and collaborative infrastructure. The study closes by calling for leadership practices that prioritize people, not platforms, and align technological ambition with pedagogical care.

This monograph explores a growing leadership crisis in the age of algorithmic decision-making. As artificial intelligence increasingly shapes high-stakes outcomes—from hiring to healthcare—organizational leaders face a legitimacy gap they are unprepared to fill. Traditional authority frameworks no longer hold when invisible systems dictate decisions that leaders cannot explain or justify. This work introduces The Boss No One Sees as a concept capturing the hidden power of algorithms and the erosion of visible leadership. Blending original insights with current research, it examines how leadership legitimacy is compromised, especially when DEI principles are missing from algorithmic governance. It offers new language, frameworks, and leadership actions rooted in ethical reasoning, inclusive foresight, and shared oversight. Case examples and practical tools illustrate how to restore trust, challenge machine outputs, and design human-centered governance models. Rather than dismiss algorithms, this monograph repositions leaders as active agents of accountability who must reclaim their voice—not after failure, but through deliberate design.

Keywords:
Algorithmic authority, leadership legitimacy, DEI, human-centered AI, ethical governance, organizational leadership, transparency, AI oversight, decision-making, accountability.

This book, The Boss No One Sees: Rethinking Leadership Legitimacy and Psychological Retaliation in Higher Education, confronts a critical yet often ignored reality in academic institutions: the misuse of leadership power through covert forms of retaliation. Rather than addressing dissent through dialogue and adaptive learning, some leaders respond with exclusion, workload manipulation, and silent sanctions that erode psychological safety. These actions rarely violate formal policy, yet they undermine trust, suppress innovation, and create emotionally unsafe environments.

Grounded in Complexity Leadership Theory, Ambidextrous Leadership, and the concept of psychological safety, this work argues that retaliation is not merely a personnel issue—it is a structural failure rooted in hierarchical rigidity and outdated leadership assumptions. Drawing from real-world patterns, theoretical models, and practical interventions, the book offers higher education leaders a new lens to assess legitimacy: not by position, but by responsiveness, ethical behavior, and the capacity to engage dissent without punishment. It proposes concrete strategies—such as dissent-safe feedback rituals, leadership coaching, and safety climate audits—to help institutions move from cultures of fear to systems of resilience and trust.

This monograph is intended for leadership scholars, academic administrators, and policy thinkers seeking sustainable, human-centered reform in higher education.

Keywords: psychological retaliation, leadership legitimacy, higher education, organizational silence, complexity leadership, ambidextrous leadership, psychological safety, dissent, toxic leadership, academic governance

Cognitive Rigidity and Related Constructs as Facets of Narrow-Mindedness What is commonly referred to as "narrow-mindedness" in everyday language is understood in academic and leadership research through more precise constructs-cognitive rigidity, intellectual arrogance, and low cognitive complexity. These terms describe the same core tendency: resistance to alternative viewpoints and difficulty adapting one's thinking when presented with new evidence. This phenomenon is not rooted in ignorance or ill intent; rather, it often arises from ingrained psychological patterns and social influences (Berthet et al., 2024; Castellón-Flores et al., 2025). Even individuals with advanced degrees or professional influence may unknowingly fall into these patterns-especially when their worldview has been shaped by cultural pressure, fear of failure, or a lifetime of associating education with legitimacy. 1. Confirmation Bias Confirmation bias refers to a pervasive cognitive tendency to seek, interpret, or recall information that confirms existing beliefs, while ignoring disconfirming evidence. For example, a scientist may unconsciously favor data supporting their established theories, failing to recognize promising alternative models. Recent research in Scientific Reports (Berthet et al., 2024) confirmed that different experimental tasks share a common underlying factor driving confirmation bias. This bias often feels like conviction-but in reality, it can blind people to new truths, even when those truths come from loved ones, colleagues, or unexpected sources. For instance, a manager who believes older employees are resistant to change may only notice examples that support that belief-ignoring situations where older staff have adapted quickly. A parent who believes their child's friends are a bad influence might misinterpret neutral behavior as rebellious, while overlooking positive interactions. Or a person in a heated discussion might.

Note: The abstract was generated with the assistance of OpenAI’s ChatGPT (2025), and reviewed for accuracy and alignment with the author’s intent.

This paper explores the persistence of narrow-minded thinking among individuals with advanced academic credentials and professional success, reframing the concept through established theoretical lenses such as cognitive rigidity, intellectual arrogance, and low cognitive complexity. Drawing on recent empirical research from 2022 to 2025, the paper examines how psychological and social mechanisms—including confirmation bias, echo chambers, and limited perspective-taking—contribute to restricted cognitive flexibility, even among highly educated individuals. Through integrative analysis, the study highlights how well-intentioned behaviors, such as encouraging others to pursue doctoral degrees, may unintentionally reflect limited recognition of alternative pathways to impact. The findings underscore the importance of intellectual humility, diverse epistemological engagement, and ongoing self-reflection in leadership, education, and organizational development. This work contributes to a deeper understanding of how success and education do not necessarily protect against the cognitive traps that hinder adaptive thinking and inclusive leadership.

In this paper we compare the institutional evolution of the higher education systems in France and England from 1980 onwards. These two systems are interesting case-studies as they correspond to two topical opposite positions in terms of their elaborative structures: the French system was originally centralised whereas the English model was decentralised. The optimal way to reform higher education systems is different in both cases (and easier in a decentralised setting due to the noncooperative behaviour of higher education institutions). We show that England faced a centralising movement (especially since 1988) whereas in France a decentralising movement was a precondition for effective reforms. In a long run perspective there seems to be some convergence between the 2 models as well as institutional change.