ARTIFICIAL INTELLIGENCE AND EDUCATIONAL LEADERSHIP FOR SUSTAINABLE TRANSITION IN HIGHER EDUCATION: A SYSTEMATIC REVIEW (https://doi.org/10.63386/619715)

 Ren Yu

Big Data and statistics faculty

Guizhou university of finance and economics

China

799700645ry@gmail.com

Kenny S.L Cheah*

Department of Education Management, Planning and Policy

Faculty of Education, University Malaya, Malaysia

kennycheah@um.edu.my

Wen Fen Beh

Faculty of Creative Arts

University Malaya, Malaysia

beh.wenfen@um.edu.my

He Zhen Zhen

School of Applied Economics

Guizhou University of Finance and Economics

China

1948465504@qq.com

*Corresponding Author

ABSTRACT

As HEIs respond to global sustainability, circular economy, and dual-carbon aspirations, AI has become a transformational tool.  For sustainable HEI transitions, this systematic literature review (SLR) analyzes how AI is integrated into educational leadership practices.  This theme synthesis study uses the PRISMA framework to assess AI applications, leadership models, and sustainability results from 25 peer-reviewed empirical and conceptual research published between 2012 and 2024.  AI applications including energy optimization systems, predictive maintenance, chatbots, and AI-driven curriculum planning interact with transformational, digital, green, and dispersed leadership approaches.  The study shows how certain combinations benefit governance, curriculum reform, carbon reduction, and UN Sustainable Development Goal alignment.  The study also finds few longitudinal studies, uneven sustainability indicators, and little empirical data from underrepresented locations.  The report recommends strategic, ethics-driven, and digitally literate leadership frameworks that integrate AI into institutional missions, operations, and policy creation.  This SLR proposes rethinking AI as a sustainable governance pillar in higher education rather than a technological instrument.

Keywords

Artificial Intelligence, Educational Leadership, Sustainability, Circular Economy, Dual Carbon Goals, Higher Education, Digital Transformation, Sustainable Development Goals (SDGs), Green Leadership

1. INTRODUCTION

Higher education institutions (HEIs) are increasingly seen as key players in sustainable development and circular economy goals due to global climate imperatives (Leal Filho et al., 2022). As governments strive for carbon peaking and neutrality, tertiary institutions must incorporate sustainability into their courses and operations and develop leaders who follow these ideals (Zhang & Wang, 2021). In the era of Artificial Intelligence, higher education leadership faces both possibilities and challenges as AI technologies evolve rapidly (Luckin, 2018). AI applications like smart energy management, predictive analytics, and digital governance may boost institutional efficiency and sustainability (Mahmood et al., 2024; Pathan et al., 2023). However, educational leaders must be both technologically savvy and grounded in sustainability ethics to leverage these tools toward achieving long-term environmental goals (Toit, 2022; Shin et al., 2023). As higher education digitizes, AI and leadership must converge strategically to help HEIs meet dual-carbon standards and circular economy principles (Qu et al., 2022).

On the other hand, scholars increasingly emphasise the importance of AI-enabled leadership in fostering sustainability and organisational success as digital technologies transform the educational landscape (Karaköse et al., 2022; Igbokwe, 2024). AI-driven digital leadership models allow for adaptive decision-making and catalyze cultural reforms that promote transparent, low-carbon, and inclusive governance (Shenkoya & Kim, 2023; Omar & Abdullahi, 2024). However, despite this growing academic interest, research on this convergence remains fragmented across domains such as educational technology, environmental governance, and organizational change (Meria et al., 2024). A cohesive synthesis of how AI-integrated leadership supports sustainable transitions in higher education is urgently needed—particularly in light of the dual goals of carbon neutrality and circular economy adoption (Yang et al., 2024). This study addresses that gap by conducting a systematic literature review to examine how educational leadership frameworks conceptualize and operationalize AI applications in pursuit of sustainability.

1.1 Justification for the Study

Global higher education institutions (HEIs) are increasingly viewed as pivotal change agents in the pursuit of sustainable development and climate action (Leal Filho et al., 2022). With the dual carbon target—carbon peaking and carbon neutrality—emerging as strategic national and global goals, universities are under growing pressure to integrate sustainable practices into campus operations, governance, curriculum, and leadership (Zhang & Wang, 2021).At the same time, the rapid advancement of Artificial Intelligence (AI) has introduced unprecedented opportunities for enhancing institutional effectiveness and sustainability initiatives (Luckin, 2018). From AI-driven energy management to smart curriculum planning and predictive analytics in administration, digital technologies are transforming how HEIs function. However, this digital transition also demands visionary and adaptive educational leadership capable of aligning AI deployment with sustainability values such as resource circularity, low-carbon operations, and inclusive digital transformation. As universities match their missions with objectives supporting the circular economy and follow dual-carbon frameworks, digital transformation in higher education increasingly shapes sustainable leadership practices.  In this context, the junction of artificial intelligence (AI) and leadership offers a solution to improve organizational sustainability and performance by means of more flexible and creative decision-making processes.

In recent years, scholars increasingly posit that sustainable organizational performance is fostered by the interplay of digital transformation, artificial intelligence, and leadership.  As Mahmood et al. (2024) emphasize, artificial intelligence integrated into digital leadership practices greatly improves an organization’s potential to reach sustainability.  Digital leadership frameworks, such as those described by Shin et al (2023)., emphasize the vital functions of leaders in negotiating technology-driven settings and suggest that an adaptable leadership approach is required to enable efficient transitions toward sustainability.  Higher education leaders have to develop digital skills using artificial intelligence technology and combine them with sustainable practices to create a digital culture supporting long-term environmental objectives. Furthermore emphasized by the COVID-19 epidemic, which hastened changes in organizational behavior and tactics, is the need to rethink leadership approaches in light of digital transformation.  Karaköse et al.(2022) underlined that efficient leadership during such changes depends on building a strong digital culture among teachers and administrators. Digital leadership is the idea of creating a culture where ethical issues and strategic vision complement one another, thereby supporting responsible digital governance, which finally promotes sustainable results (Toit, 2022).

In addition, the field of educational leadership is changing to include artificial intelligence’s possible transforming power.  Igbokwe (2024) points out that artificial intelligence technologies enable data analysis and predictive analytics, which may greatly improve decision-making processes when used by educational leaders. Though, the inclusion of artificial intelligence has to be handled carefully to allay issues such data protection and responsibility.  Creating a sustainable and ethical framework for AI application inside educational leadership depends on a careful balance of these components.

Therefore, AI-enabled leadership in higher education is pivotal for driving the transformation toward a circular economy and sustainable practices.  Leaders may properly negotiate the complexity of the digital environment by encouraging digital skills and combining them with a transparent ethical framework, so guaranteeing that their institutions not only adjust to evolving circumstances but also actively support sustainability.  Continuous multidisciplinary study is crucial to deepen the knowledge of how these processes interact within the larger framework of digital transformation and sustainable leadership (At-tamimi et al., 2024).

1.2 Problem Statement

The junction of artificial intelligence (AI) and leadership strategies in higher education offers a special prospect to foster sustainable transitions in line with the values of the circular economy and dual carbon frameworks.  Despite growing interest in digital transformation and sustainable leadership, there is limited synthesized knowledge on how AI-enabled leadership practices in higher education contribute to the circular economy and sustainable transitions under the dual carbon framework. Existing studies are often fragmented across disciplines—ranging from green campus planning to digital governance—making it difficult to form a coherent understanding of this intersectional field.

Though digital revolution in this industry is increasingly acknowledged, academic research on AI-enabled leadership and its contribution to reaching sustainability objectives still scattered and underappreciated. Shaping sustainable plans in higher education depends on how artificial intelligence may improve leadership practices.  Artificial intelligence technologies’ integration can provide executives with sophisticated decision-making capabilities to simplify procedures supporting sustainability.  Pathan et al. point out that by means of resource management, waste reduction, and general operational efficiency in educational institutions, artificial intelligence may help to apply circular economy methods.  Higher education institutions set the example for sustainable practices that speak with more general organizational governance, so this is very important.

Driven by artificial intelligence, digital transformation fundamentally changes the educational scene by changing how sustainability is considered in institutional policies and courses.  Shenkoya and Kim (2023) underline that digital transformation improves the sustainability of higher education institutions by promoting open information distribution, which is vital for developing awareness of environmental concerns and teaching circular economy ideas.  Developed under these digital changes, the educational systems not only fulfil urgent sustainability objectives but also equip graduates to interact meaningfully with circular economic activities (Qu et al., 2022). In addition, thorough higher education policies are required to foster sustainable leadership by means of organized digital changes supporting institutional sustainability.  Moreover, Omar and Abdullahi’s study shows that promoting a sustainable digital transformation calls for leaders to negotiate difficult issues like opposition to change and the necessity for significant involvement of stakeholders (Omar & Abdullahi, 2024).  Such leadership should guarantee responsibility and openness inside the institution’s operations and emphasize integrating digital projects that properly promote sustainable objectives.Furthermore, a major component of building a circular economy in higher education is the involvement of students and teachers in sustainable activities.  Student understanding and dedication to environmental issues, as Yang et al. show, have a major impact on the acceptance of circular economy goods and behaviors (Yang et al., 2024).  Education and active involvement serve to strengthen sustainability ideals, hence fostering a culture that supports long-term circular economic objectives.  Reaching this calls for coordinated efforts among academic, operational, and environmental leaders inside institutions to match educational results with sustainability needs (Meria et al., 2024).

Ultimately, even if artificial intelligence-enabled leadership may promote sustainable practices in higher education toward meeting the goals of the circular economy and dual carbon framework, a consistent and comprehensive strategy is required.  The studies indicate that enabling this change depends much on a mix of technology developments, strategic leadership, and stakeholder involvement.  Hence, this present study should emphasize combining these several components to create thorough frameworks incorporating educational leadership, sustainability, and artificial intelligence.

1.3 Purpose of the Study

This systematic literature review (SLR) aims to identify, analyze, and synthesize scholarly work that explores the integration of AI technologies into educational leadership practices that promote sustainability, circular economy, and low-carbon strategies in higher education settings.

1.4 Research Questions

This study addresses the following research questions:

1. What AI applications have been integrated into educational leadership to support sustainability in higher education?
2. How does current literature conceptualize the relationship between educational leadership, AI, and circular economy principles?
3. What models or frameworks exist to guide AI-driven sustainable leadership in universities?
4. What gaps exist in empirical evidence on AI-based leadership supporting carbon neutrality and circular practices?

This SLR contribute to building a theoretical foundation and practical roadmap for higher education leaders, policymakers, and researchers. It provides an evidence base for integrating AI into leadership strategies to meet national carbon goals and sustainable development targets, particularly within the context of the education sector’s role in climate governance.

2. METHODOLOGY

2.1 Review Protocol

This review adopts the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework (Page et al., 2021) to ensure transparency and methodological rigor. The study protocol was defined prior to the literature search and included clear inclusion/exclusion criteria, search strategies, and thematic analysis procedures.

2.2 Databases and Search Strategy

Standardized keywords and criteria were used to look for methodological transparency and academic quality.  We searched for literature on artificial intelligence, educational leadership, sustainability, and higher education.  To enhance findings breadth and relevancy, Web of Science, Scopus, ERIC, IEEE Xplore, and ScienceDirect were queried.  Only peer-reviewed English journal publications from 2012 to 2024 were included to ensure current and academically sound insights.

Inclusion criteria favored research on AI integration into higher education leadership practices, particularly those related to sustainability and circular economy aims.  Studies on K–12 or AI applications unrelated to leadership and sustainability were omitted.  We removed non-peer-reviewed and non-English sources using further criteria.  This thorough and targeted strategy identified relevant material while focusing on the study objective: understanding how AI-enabled higher education leadership helps to sustainability transitions. Exclusion criteria ruled out studies focused on K–12 settings, teaching-focused AI without leadership or sustainability components, non-peer-reviewed materials, and non-English publications. This approach ensured a focused and high-quality evidence base aligned with the study’s objectives. Table 1 is a summary of the search strategy employed in this study.

Table 1 Search Strategy Summary

2.4 Study Selection Process

To assure study relevance and quality, three methodical processes were used to choose research. First, two reviewers independently reviewed titles and abstracts for eligible publications. After that, a full-text review assessed each study’s alignment with the research goals and inclusion criteria. Finally, a coding matrix was used to analyse the selected papers for extensive theme analysis and structured synthesis across important factors. To assist readers, A PRISMA flowchart is being constructed to visualize the article selection process (to be finalized upon full screening of retrieved studies).

PRISMA Flow Diagram

Figure 1 presents the PRISMA flow diagram that outlines the article selection process for this systematic literature review. A total of 530 records were identified—512 through database searches and 18 from other sources. After removing duplicates, 470 records remained and were screened based on titles and abstracts, resulting in the exclusion of 385 articles. The remaining 85 full-text articles were assessed for eligibility, with 60 excluded for not meeting the inclusion criteria. Ultimately, 25 studies were included in the final qualitative synthesis, forming the evidence base for the review.

Figure 1 PRISMA flow diagram

2.5 Data Extraction, Analysis and Coding

To ensure a rigorous and structured synthesis of the included studies, a data extraction process was implemented using a coding matrix initially created in Microsoft Excel. This matrix served as the central repository for organizing and standardizing all relevant data elements from each article. Each entry in the matrix corresponded to a single study and contained fields for bibliographic information (author(s), publication year, and country), as well as thematic categories related to the core focus areas of the review: artificial intelligence (AI), educational leadership, and sustainability. This preliminary step ensured consistency in capturing the scope and relevance of the literature for subsequent thematic analysis.

The coding process specifically targeted five key information areas. First, the AI application type was identified—categorizing whether the study addressed AI for campus operations (e.g., energy management), administrative efficiency (e.g., chatbots or LMS automation), curriculum development, or governance systems. Second, the sustainability domain was recorded, mapping each study to one or more categories such as energy efficiency, waste reduction, sustainable pedagogy, green infrastructure, or digital governance related to the circular economy or low-carbon transition. This dimension aligned closely with the research aim of examining how AI supports dual carbon goals within higher education institutions.

Third, to understand the leadership dynamics, the leadership model or theoretical framework used in each study was coded. This included identifying frameworks such as transformational leadership, distributed leadership, green leadership, digital leadership, or strategic leadership. These categorizations helped highlight how leadership is conceptualized in the context of AI implementation for sustainability. Additionally, coding captured whether the leadership approach was top-down (executive-led) or participatory (involving faculty, students, and stakeholders), allowing for a nuanced understanding of governance models in sustainability transitions.

Finally, key findings and recommendations from each study were thematically summarized and coded. This included noting evidence of impact (e.g., reduced energy usage, improved policy adherence, curriculum innovation), implementation challenges, and suggested best practices for AI-supported leadership. These qualitative insights were then imported into ATLAS.ti for deeper analysis. The software allowed for inductive coding and the generation of co-occurrence maps, helping to identify recurring patterns and emergent themes across the literature. This two-stage process—Excel-based structuring followed by ATLAS.ti-enhanced thematic analysis—provided methodological robustness and facilitated the development of a meaningful synthesis to answer the research questions.

Sample Analysis of Coded Excerpt (from: Chan & Mok, 2022)

Quoted Segment from Article:

“The use of AI tools in curriculum design has empowered department heads to make data-driven decisions that align with institutional sustainability goals. By analyzing student learning outcomes and environmental impact metrics, leaders were able to restructure course modules to support SDG integration, reducing reliance on traditional printed resources.”

Table 2: Coding Analysis example for Chan & Mok, 2022

Memo Summary:

This excerpt demonstrates how AI-based curriculum planning intersects with digital leadership and contributes to the sustainability domain of academic curriculum transformation. It reveals a clear leadership strategy that harnesses AI for both pedagogical optimization and environmental impact reduction, aligning with circular economy goals.

2.6 Synthesis Strategy

Finally, Thematic synthesis was employed using an inductive approach. Articles were grouped by common leadership practices, AI technologies, and sustainability goals. Emerging themes were organized into analytical categories to answer the research questions.

3. FINDINGS

In this section, we will address the first two research questions:

1. What AI applications have been integrated into educational leadership to support sustainability in higher education?
2. How does current literature conceptualize the relationship between educational leadership, AI, and circular economy principles?

To deepen understanding of how artificial intelligence (AI) is integrated into leadership practices that support sustainability in higher education, Table 3 synthesizes key empirical and conceptual studies across diverse global contexts. It highlights various AI applications, leadership models, and sustainability domains, offering insight into institutional strategies, operational efficiencies, and educational innovations. The table also reveals how leadership styles—ranging from transformational to ethical and policy-oriented—mediate the successful deployment of AI in areas such as energy, curriculum, governance, and green transformation. This overview provides a foundation for identifying thematic patterns and research gaps relevant to AI-driven sustainable leadership in higher education.

Table 3: Overview of AI Applications, Leadership Models, and Sustainability Domains in Higher Education Institutions

The synthesis of the 25 important empirical and conceptual research showing how artificial intelligence (AI) is being used inside leadership frameworks to assist sustainability projects in higher education in different worldwide settings. Each research is classified by nation, kind of AI application, leadership approach, and particular sustainability domain it addresses—such as energy efficiency, curriculum development, governance, and green transformation—in the table. It also records important results stressing the institutional effects of artificial intelligence—from boosting administrative efficiency and matching curriculum with the Sustainable Development Goals (SDGs) to changing ethical digital governance and better energy consumption. This synthesis provides a comparative knowledge of the several leadership styles—from transformational to inclusive and policy-oriented—that mediate AI deployment, therefore providing important analysis of developing trends, innovations, and holes in AI-driven sustainable leadership inside universities.

1. Excerpt from Code Book

To further unpack the thematic insights derived from the reviewed literature, Table 4 presents a structured code book that categorizes key excerpts from selected studies. This coding process highlights how AI applications, leadership models, and sustainability outcomes are interwoven within higher education contexts. Each entry reflects a critical element—whether technological, strategic, or ethical—used to advance sustainability goals in universities. For instance, codes such as Predictive Maintenance, Green Pedagogy, and Digital Ethics illustrate the multifaceted ways AI influences operations, pedagogy, and institutional governance.

The code book also showed how we trace recurring leadership models—ranging from digital and distributed leadership to sustainability-oriented and AI-informed approaches—alongside AI deployment patterns like curriculum planning, learning analytics, and administrative automation. By identifying impacts such as emission reduction, resistance to transformation, and digital divide concerns, this framework enables a more nuanced synthesis of how AI-enabled leadership contributes to sustainable transformation. Overall, the code book functions as both an analytical tool and an interpretive lens, laying the groundwork for deeper discussion on patterns, gaps, and implications across the literature.

Table 4 Codebook of Thematic Excerpts Categorizing AI Applications, Leadership Models, and Sustainability Domains in Higher Education

1. Thematic Analysis

Through an iterative and reflective process, our thematic analysis on the codes generated from the reviewed literature reveals that artificial intelligence (AI) is being strategically implemented across various sustainability domains in higher education, such as energy efficiency, governance, and curriculum reform, serving as both a technological enabler and a catalyst for environmental responsibility. The findings of this section address the remaining research questions:

1. What models or frameworks exist to guide AI-driven sustainable leadership in universities?
2. What gaps exist in empirical evidence on AI-based leadership supporting carbon neutrality and circular practices?

Leadership models, including transformational, digital, green, and distributed leadership—play a critical role in guiding these AI applications, shaping institutional responses to sustainability challenges and aligning initiatives with SDG targets. Moreover, AI integration in curriculum and governance functions has enhanced transparency, decision-making, and operational efficiency, with measurable outcomes like reduced emissions, improved student engagement, and streamlined administrative processes, signaling a need for policy and research frameworks that support adaptive, ethical, and digitally literate leadership in the age of AI. Highlighted below are the summary for each theme synthesized from this study:

1. Thematic Integration of AI Applications and Sustainability Domains
   The findings from the reviewed literature highlight the diverse ways in which artificial intelligence (AI) technologies are being implemented within higher education institutions to support sustainability goals aligned with the circular economy and dual carbon targets. AI applications ranged from energy management systems to predictive maintenance, administrative chatbots, and AI-enhanced curriculum tools (Almeida et al., 2023; Zhou et al., 2021; Caliskan & Gecikli, 2023). These technological deployments were directly linked to sustainability domains such as energy efficiency, governance, curriculum reform, and operational management. This reflects a trend toward institution-wide digital transformation where AI functions not merely as a technical solution but also a strategic driver of environmental responsibility (Luckin, 2018; Leal Filho et al., 2022; Yu & Park, 2020).

1. Leadership Models Enabling AI-Driven Transitions

A central theme in the reviewed studies is the pivotal role of educational leadership in enabling and scaling AI-driven sustainability initiatives. Transformational leadership was closely associated with infrastructural advancements such as AI-based energy systems (Zhou et al., 2021), while distributed leadership emerged prominently in academic technology implementation and decision-making (Kumar & Lee, 2020; Hashim & Yunus, 2019). Digital leadership facilitated data-driven curriculum development aligned with SDG goals (Chan & Mok, 2022), and green leadership was particularly evident in predictive maintenance and resource optimization (Almeida et al., 2023; Yu & Park, 2020). These models demonstrate that leadership style not only influences the success of AI applications but also shapes institutional alignment with sustainability values (Mohamed & Faizal, 2021; Esteve et al., 2021).

1. Curriculum and Governance as Strategic Leverage Points

AI-enabled reforms in curriculum and governance were identified as strategic leverage points for sustainability transformation. In curriculum development, AI-supported tools allowed for personalized learning pathways and alignment with environmental competencies (Chan & Mok, 2022; Sinha & Mishra, 2022). In governance, AI systems such as chatbots and smart analytics tools reduced administrative load, allowing leaders to focus on long-term sustainability planning (Nguyen, 2019; Costello et al., 2023). These domains also benefitted from increased transparency, stakeholder engagement, and evidence-informed decision-making—hallmarks of effective digital governance (Ifenthaler & Schumacher, 2019; Zhang & Wang, 2021).

1. Outcomes and Impact Across Institutional Functions

The review revealed both qualitative and quantitative impacts of AI-supported leadership across campus functions. Smart energy systems led to measurable emission reductions (Zhou et al., 2021), while chatbot deployment improved institutional efficiency (Nguyen, 2019). Learning management systems enhanced student engagement and curriculum optimization (Kumar & Lee, 2020), and predictive analytics reduced operational downtime (Almeida et al., 2023). The cumulative impact includes improved ESG performance, streamlined governance, and greener academic delivery models (Bozkurt & Sharma, 2020; Rajab et al., 2020; Aung & Oo, 2022).

1. Co-Occurrence Patterns and Strategic Synergies

The co-occurrence mapping revealed strategic synergies between AI types, leadership models, and sustainability domains. For instance, energy management systems strongly correlated with transformational leadership and the energy domain, while curriculum planning intersected with digital leadership and SDG integration (Chan & Mok, 2022; Zhou et al., 2021). Chatbots aligned with strategic leadership in governance, and predictive maintenance was tied to green leadership in facilities management (Almeida et al., 2023). These combinations suggest that optimal sustainability outcomes require tailored leadership approaches depending on the type of AI application and institutional priority (Page et al., 2021; Holmes et al., 2019).

1. Implications for Policy, Research, and Leadership Practice

This review yields several forward-looking implications. First, HEIs must recognize AI as a strategic enabler of institutional and environmental transformation—not just an operational tool (Luckin, 2018; Tandon et al., 2021). Second, policy frameworks must incentivize leadership models that integrate digital literacy, environmental consciousness, and data ethics (Ifenthaler & Schumacher, 2019; Esteve et al., 2021). Third, future research should focus on comparative and longitudinal studies that track AI’s effectiveness in driving carbon-neutral transitions in various contexts (Skourti & Ferlander, 2021; Holmes et al., 2019). Finally, leadership development in higher education should emphasize hybrid models—combining strategic, transformational, and green leadership—to navigate the complexities of AI governance and sustainability (Mohamed & Faizal, 2021; Zhang & Wang, 2021).

4. DISCUSSIONS

This section orientates the thematic synthesis of the reviewed literature into five core areas that align closely with the key research objectives of this study. In essence, these discussions relate how AI applications have been integrated into educational leadership to advance sustainability, how leadership mediates the relationship between technology and circular economy principles, what models and frameworks support AI-enabled sustainable leadership, and where critical gaps in empirical evidence remain. By anchoring these themes to the study’s research questions, this section aims to not only map current knowledge but also evaluate its contribution to the development of strategic, AI-informed leadership practices capable of supporting dual-carbon goals and sustainable transformation in higher education institutions.

1. AI Applications Integrated into Educational Leadership for Sustainability
   The review of 25 scholarly articles reveals a wide range of AI applications currently integrated into higher education leadership to promote sustainability. These include energy optimization systems (Zhou et al., 2021), predictive maintenance tools (Almeida et al., 2023), AI-enhanced learning management systems (Kumar & Lee, 2020), curriculum planning platforms (Chan & Mok, 2022), and administrative chatbots (Nguyen, 2019). These tools are used not only to optimize operations—such as reducing energy consumption and improving resource planning—but also to enable leaders to make data-driven decisions that align institutional practices with sustainability objectives. In particular, AI-driven curriculum tools have empowered academic leaders to redesign programs in line with the Sustainable Development Goals (SDGs), emphasizing resource efficiency, environmental literacy, and reduced dependency on traditional materials.

1. Conceptualizing the Relationship Between Leadership, AI, and Circular Economy
   The reviewed literature consistently highlights that educational leadership acts as a catalyst in bridging AI adoption with circular economy principles in higher education. Leadership is conceptualized not merely as administrative oversight but as a strategic function that aligns technological innovation with sustainable transformation (Zhang & Wang, 2021; Yu & Park, 2020). Leaders influence how AI is deployed to reduce waste, improve digital infrastructure, and support closed-loop systems through green digital transformation. For example, green leadership is associated with implementing AI systems for campus energy tracking and predictive maintenance, reflecting circular values of efficiency and waste reduction (Almeida et al., 2023). Similarly, digital leadership plays a vital role in curricular transformation by embedding sustainability content supported by AI analytics (Chan & Mok, 2022). This intersectional approach repositions AI as a sustainability enabler when championed by vision-oriented leadership.

1. Existing Models and Frameworks for AI-Driven Sustainable Leadership
   Several theoretical and practical frameworks have emerged to guide sustainable leadership in AI-integrated university settings. These include distributed leadership in technology adoption (Kumar & Lee, 2020), digital leadership in curriculum transformation (Hashim & Yunus, 2019), and green leadership in operational management (Yu & Park, 2020). Frameworks such as the PRISMA systematic review protocol (Page et al., 2021) and the SDG-AI alignment model (Tandon et al., 2021) offer structured methodologies for leaders to evaluate the sustainability impact of AI deployments. Additionally, the concept of AI-informed leadership has gained traction, emphasizing ethical, strategic, and collaborative decision-making processes (Esteve et al., 2021). These models collectively advocate for holistic, adaptive, and context-sensitive leadership practices to guide sustainable transitions through AI.
2. Gaps in Empirical Evidence on AI-Based Leadership for Circular and Low-Carbon Goals
   Despite growing interest in AI for sustainability, the review identifies critical gaps in empirical research. Most studies remain theoretical or context-specific, with limited large-scale or longitudinal data evaluating the effectiveness of AI-based leadership in achieving carbon neutrality or promoting circular practices. For instance, while some case studies show reductions in energy use (Zhou et al., 2021), few link these outcomes directly to institutional carbon targets or long-term circular strategies. Furthermore, ethical implications, such as data governance, student privacy, and equitable AI access, are underexplored in relation to leadership frameworks (Ifenthaler & Schumacher, 2019; Skourti & Ferlander, 2021). There is also limited cross-cultural analysis, especially in developing regions, which restricts the generalizability of current models.

1. Moving Forward: The Need for Strategic, Research-Informed Leadership
   Addressing these gaps requires a multi-pronged approach. First, institutions should support cross-institutional empirical studies that evaluate the long-term effects of AI-driven leadership on carbon emissions and circular economy integration. Second, leadership development programs must incorporate digital and ecological literacies, enabling future leaders to understand and utilize AI ethically and strategically (Luckin, 2018; Mohamed & Faizal, 2021). Third, universities should adopt adaptive frameworks—combining digital governance, SDG alignment, and stakeholder collaboration—to design sustainable AI ecosystems. In doing so, AI can shift from being a tool of convenience to a cornerstone of institutional sustainability and environmental accountability in higher education.
2. IMPLICATIONS

As implications, the study highlights the urgent need for higher education institutions (HEIs) to reposition artificial intelligence (AI) as more than just a technical tool—it must become a strategic enabler of sustainability transformation. As universities face mounting pressure to meet global climate goals, including dual carbon targets and circular economy commitments, institutional leadership must evolve accordingly. This means cultivating leaders who are not only technologically proficient but also environmentally literate, capable of steering AI implementation in ways that drive measurable outcomes such as energy efficiency, emissions reduction, and resource circularity. When guided effectively, AI can shift from a supporting function to a strategic cornerstone of institutional environmental responsibility.

Moreover, the findings reveal a significant gap in empirical and comparative research that evaluates the long-term impact of AI-driven leadership on sustainability outcomes. While numerous case studies demonstrate the promise of AI tools—such as predictive maintenance systems and AI-aligned curriculum redesign—few studies assess these tools using rigorous, standardized sustainability metrics across diverse contexts. Therefore, future research agendas should prioritize longitudinal, cross-institutional studies that can generate scalable insights. Such studies would benefit from shared sustainability indicators and collaborative benchmarking processes to help establish best practices for integrating AI into institutional climate action.

In addition, there is a pressing need to rethink how leadership development programs are structured within HEIs. Existing leadership training often omits the critical dimensions of digital fluency, algorithmic ethics, and environmental systems thinking. By integrating these components into formal leadership development—whether through executive education, institutional workshops, or credentialing programs—universities can ensure their current and future leaders are equipped to make informed, ethical, and strategic decisions regarding AI. These programs should also prepare leaders to navigate the social risks of AI, including issues related to data privacy, equity, and digital exclusion, while maximizing the technology’s contribution to long-term sustainability.

Equally important, the study suggests that sustainable AI integration requires the adoption of adaptive, multilevel governance structures within universities. Rather than confining AI deployment to isolated technical departments, institutions should embed digital transformation efforts across academic, operational, and community-facing domains. This calls for governance frameworks that foster alignment between digital innovation, SDG integration, and resource management planning. Cross-functional leadership teams—comprising IT managers, academic heads, sustainability officers, and student representatives—should work collaboratively to ensure that AI deployments are transparent, inclusive, and responsive to evolving sustainability needs.

In a nutshell, Figure 2 presents a thematic mind map outlining the core dimensions connecting artificial intelligence (AI) with sustainability in higher education institutions (HEIs). The framework identifies five major thematic domains: Strategic Repositioning, Research Gaps, Leadership Development, Governance Structures, and Strategic Integration. Each branch further delineates sub-themes—for example, AI as an enabler and measurable outcomes under Strategic Repositioning, or ethical literacy and systems thinking under Leadership Development. This conceptual structure provides a synthesized visual guide to understanding how AI can be mobilized as a transformative force for sustainability through targeted leadership, policy, and institutional practices.

Figure 2 Conceptual Mind Map of Key Themes Linking AI and Sustainability in Higher Education Institutions (HEIs)

Ultimately, the study calls for a fundamental reframing of how AI is viewed in higher education. Rather than treating AI as a convenient add-on for operational efficiency, institutions must embed it into their core sustainability strategies. When guided by strong, ethically grounded leadership, AI can be leveraged to create systemic change: from enabling carbon neutrality and energy automation to advancing circular curriculum design and reducing institutional waste. Without a clear strategic framework, however, AI risks being implemented in ways that undermine rather than support sustainability goals. Thus, institutions must ensure that AI adoption is not only technologically sound but also aligned with their environmental mission and societal responsibilities.

5. CONCLUSION

In conclusion, this systematic literature review provides strong evidence that artificial intelligence (AI), when strategically implemented, holds transformative potential for advancing sustainability in higher education. From energy management systems and predictive maintenance to AI-enhanced curriculum design and administrative chatbots, the integration of AI across institutional functions is increasingly aligned with the broader goals of environmental responsibility and circular economy transitions. However, these technological applications are only as effective as the leadership frameworks that govern them. The review emphasizes that educational leadership—whether transformational, digital, green, or distributed—serves as the linchpin in steering AI tools toward outcomes that are not only efficient but also ethically sound and environmentally sustainable.

At the same time, the findings uncover significant gaps that must be addressed to ensure AI’s contribution to carbon neutrality and sustainable development is both robust and equitable. The lack of empirical, longitudinal data and cross-institutional benchmarking hinders our ability to assess the true impact of AI-enabled leadership on sustainability metrics. Furthermore, the uneven attention to issues such as digital equity, ethical governance, and regional diversity suggests that current practices may reproduce rather than resolve systemic inequalities in AI access and implementation. These challenges present opportunities for future research and institutional reform—especially in developing leadership development programs that fuse digital, ecological, and ethical literacies into a cohesive strategic agenda.

Ultimately, this study calls for a paradigm shift in how AI is understood and deployed within the higher education landscape. Institutions must stop viewing AI as a passive technological add-on and instead embrace it as a strategic pillar for sustainability transformation. By embedding AI within institutional missions, governance structures, and educational values, universities can lead not only in academic excellence but also in global climate action. This requires visionary leadership, cross-sector collaboration, and continuous investment in systems thinking. If adopted thoughtfully and inclusively, AI can be harnessed not just to optimize operations but to redefine the purpose and promise of higher education in the 21st century.

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