Artificial intelligence (AI) is becoming an increasingly crucial element in higher education. A recent transformative development is generative AI (GenAI), a form of AI underpinned by deep learning models that produce human-like outputs when provided with complex prompts. This development brings AI into direct engagement with core academic practices, including student learning, teaching, assessment and research (An et al., 2025; Nikolic et al., 2024).For example, students have been using available AI applications and tools as part of their learning practices (Johnston et al., 2024; Pallant et al., 2025). These tools can be used to effectively support a range of academic tasks such as language editing and ideation if appropriately used (Black and Tomlinson, 2025). However, concerns might arise as a result of inequalities in students' access to and capacity to utilise AI tools responsibly. Such disparities might widen the “AI divide” between students who are well-prepared to optimise AI for meaningful academic advantage and those who are not (Hammerschmidt et al., 2025).Furthermore, academic staff might be uncertain about whether, and to what extent, AI should be appropriately incorporated into teaching, learning and assessment (Farazouli et al., 2025). This uncertainty surrounding AI might stem from a lack of clear institutional direction and professional development support, rather than from opposition to innovation. Educators are left to determine acceptable use on their own, with limited knowledge and experience of GenAI tools, which has led to uncertainty and discomfort when making decisions about teaching and assessment (Corbin et al., 2025; Farazouli et al., 2025). In a fast-changing educational environment, uncertainty coupled with inherent tensions around adaptation might generate pedagogical, ethical and institutional consequences that somehow extend beyond matters of policy compliance. This raises fundamental questions about how teaching, learning and assessment are organised in practice and about whether current approaches can be sustained in ways that remain consistent with higher education's long-standing commitments.Alongside students and academic staff, institutions themselves have been urged to take a more active role in responding to the implications of AI by, for example, constantly revisiting earlier decisions and readdressing issues as they arise (Wilson, 2025). This makes clear that AI cannot be managed through isolated initiatives or one-off policies. Instead, it requires concerted, strategic efforts of leadership, management and governance to establish a coherent approach to AI.This special issue brings together studies that offer new perspectives on AI in higher education, examined through the question of how AI technologies are being used to support, or claimed to support, the implementation of the Sustainable Development Goals (SDGs). The focus is not on AI adoption in itself, but on what its expanding presence means for strategic management and leadership within higher education institutions. Since the adoption of the SDGs in 2015, they have become a widely used global reference point for guiding higher education institutions' responses to social, economic and environmental challenges (Abad-Segura and González-Zamar, 2021). Higher education institutions, therefore, are expected to contribute to this agenda through teaching, research, knowledge exchange and their own institutional practices (Avelar et al., 2023; Alcántara-Rubio et al., 2022). As AI has become increasingly embedded in higher education, it has also been promoted as a technological development with the potential to support sustainability objectives (Khan et al., 2025; Dubay and Richards, 2024; Leal Filho et al., 2024). However, the increasing use of AI in higher education also raises a number of concerns that are relevant to debates about sustainability. These include the potential reinforcement of racial and socio-economic inequalities (Roshanaei, 2024), the introduction of bias into decision-making (Panarese et al., 2025), as well as weaker accountability and risks to privacy (Khan et al., 2025).Therefore, this special issue did not start from the assumption that AI is either a solution or a problem for the implementation of the SDGs in higher education. We sought contributions that explore how and why AI becomes connected to sustainability agendas, the conditions for these connections and the practical consequences for how AI is introduced, governed and defended within institutions. Through these lenses, the articles in this issue also address questions of institutional priorities, values and leadership that are central to the scope and readership of the International Journal of Educational Management.In total, the current special issue comprises 17 articles, examining AI and SDGs in higher education from various settings and approaches. The contributions suggest that there is no single pattern in how AI supports sustainability goals. Differences in local and organisational capacity, leadership choices, governance arrangements, professional norms and existing inequalities account for much of the variation observed across higher education systems. Drawing on work from Brazil, India, Indonesia, Malaysia, the Netherlands, Nigeria, Vietnam and other contexts, the papers highlight shared concerns as well as differences in how AI is understood, used and debated in relation to the SDGs. The 17 articles in this special issue are organised into 3 themes that delineate distinguishable, albeit interrelated ways in which AI is approached in relation to the SDGs in higher education.Six articles in this theme treat AI adoption in higher education as a question of organisation and strategy rather than solely of technology, and in doing so, they question the assumption that technological uptake naturally leads to sustainable outcomes. Fernandes et al. (2026) discuss varied challenges in the implementation of AI-related policies in the context of Brazilian higher education, including limited AI expertise, ambiguity about ethical use of AI and constraints in funding and training. Drawing on a review of 39 studies, Toha (2026) characterises AI as a “robust tool” to support sustainable higher education by facilitating personalised learning, accessibility and equitable resource allocation. This review equally underscores major pedagogical and governance challenges in harnessing the affordances of AI to support sustainability in higher education. On this basis, Toha (2026) argues that the promise of AI in promoting sustainable higher education significantly depends more on the receptivity of policymakers and institutions to the ethical use of AI.Studies from Malaysia, India and Vietnam further illustrate how organisational capacity affects AI adoption in practice. Jayashree et al. (2026) suggest that big data analytics contributes to sustainability outcomes through its relationship with organisational capacity, leadership focus and staff capability, rather than through technical deployment alone. Thakur et al. (2026) show that leadership vision and strategic priorities play an important role in determining whether digital transformation leads to inclusive practices that are in accordance with sustainability goals. Tran et al. (2026) examine the influence of leadership of innovation in public universities. Their findings suggest that innovation is more likely to develop when leaders support creative work and cultivate conditions that enable staff to respond to AI-driven job complexity and digital change.Further insights are reported in the review by Jaboob et al. (2026) and Jogezai et al. (2026). In both articles, discussion of AI's potential is accompanied by recognition of practical limits related to infrastructure, staff expertise, ethical and regulatory considerations and policy development. The benefits of AI in higher education are presented as possible but dependent on conditions that many institutions have yet to establish. The emphasis on ethical guidance and institutional coordination shifts attention away from technological momentum and towards leadership and decision-making.This theme challenges the assumption that adopting AI will, in itself, lead to more sustainable higher education. The papers show that what does matter is also how institutions organise, resource and take responsibility for AI use. For institutional leadership and management, the implication is clear that sustainability continues to be a critical matter of strategy and governance, not a problem that technology can resolve on its own.The second theme centrally discusses how AI technologies are reshaping teaching, learning, and skills development in relation to the SDGs. Surendhranatha Reddy and Leelavathi (2026) report higher levels of student engagement in courses adopting technology-supported collaboration. The improvements identified in the study are associated with changes in learning activities and reduced administrative demands. The authors also noted that these outcomes are subject to training and institutional support, which may be inconsistently available across institutions.The mixed-methods study by Ironsi and Ironsi (2026) reports improvements in critical thinking, problem-solving and digital literacy among undergraduate students. It equally shows that these outcomes are influenced by material conditions, including digital infrastructure, technical support and Internet access. These constraints make it difficult to assume that micro-credential approaches can be adopted in similar ways across institutions, particularly where resources are limited.Discipline-specific studies add further nuances. More specifically, Rockett (2026) evidences the affordances of AI in enhancing students' confidence in literature exploration and idea generation in SDG problem-based learning within fashion management education. Yet, persistent uncertainty remains regarding the extent to which AI use is congruent with ethical principles and pedagogical appropriateness.AL-Sendy et al. (2026) suggest that AI has the potential to transform and modernise accounting education, preparing students for a technology-driven future. The use of AI in accounting education supports students' reflective learning and enhances learning outcomes, including reviewing decision-making processes, monitoring progress and adapting learning strategies. However, reflective thinking on its own does not significantly improve learning outcomes; its value lies in how it works together with AI and lecturer creativity. This finding underscores the significance of professional development support for lecturers for optimising the responsible use of AI creatively and embedding AI-assisted reflection activities, such as journaling and self-assessment, into coursework.A study conducted in the Netherlands, by den Hollander et al. (2026), shows that course coordinators recognise the inexorability of GenAI while expressing uncertainty about its implications for standards of academic conduct, the relevance of course content and core educational principles. It also suggests that AI adoption may be driven by external pressures rather than shared strategic direction, leaving educators to navigate these tensions with limited institutional guidance. Similarly, Tang (2026) conducted a study on lecturers' decisions to use AI in their teaching in Vietnamese higher education institutions. The study notes that greater openness to new pedagogical approaches among lecturers is related to increased engagement with AI, although this engagement can be limited by practical barriers such as resource constraints and institutional support. Tang (2026) suggests that AI adoption in teaching is contingent upon the interaction between individual dispositions and the conditions under which teaching takes place.Overall, this theme indicates that AI technologies in teaching could enhance student engagement, reflective learning and the development of skills directed toward the SDGs, provided that their use is meticulously tailored and institutionally supported. The mere presence of AI technologies is insufficient to improve learning outcomes without accompanying changes in classroom practices, lecturer creativity and access to appropriate professional development. However, there are practical constraints, such as the availability of digital infrastructure, staff readiness and clarity around ethical use, that continue to influence how AI-enabled teaching can be put into practice, taking the different levels of resourcing into consideration. This places the growing responsibility of higher education leaders on working towards long-term investment in staff development, transparent teaching and learning policies and equitable access. Without sustained strategic direction, the adoption of AI technologies may not lead to meaningful or inclusive change in higher education.Equity and inequality in the adoption of AI in higher education are central concerns across this theme.Oliveira-Melo et al. (2026) highlight differences across countries in access to tertiary education, participation in vocational pathways and engagement in lifelong learning. These differences reflect wider socioeconomic conditions, gender relations and institutional arrangements and provide important contexts for discussions about the use of AI in higher education. The findings suggest that national systems vary in the conditions under which educational reforms are introduced and sustained. In contexts where structural constraints persist, the introduction of AI is therefore unlikely to support more equitable or inclusive educational outcomes.Drawing on stakeholder perspectives from two public universities in Nigeria, Adebayo and Adekunle (2026) illustrate how the promise of AI in higher education is closely tied to institutional realities. While AI is likely associated with gains in access, personalised learning, research, global citizenship and institutional processes, these developments coexist with unresolved challenges related to data protection, ethical practice, digital capability and funding. Greater attention should be given to equitable allocation of resources, governance and digital literacy if the adoption of AI in higher education aims to help address questions of equity and inclusion in access to education.Mello Cavalcanti and Tavares (2026) highlight the affordances of AI in supporting learning experiences that are both inclusive and responsive to individual needs, as well as greater access to education and student retention, while emphasising how infrastructure, staff expertise and ethical acceptance might condition the enactment of these affordances. Apata et al. (2026) further these insights through a synthesis of international research between 2015 and 2025, showing that AI in higher education is being used to personalise learning, provide more flexible support for learners' needs and improve access for under-represented groups. It also draws attention to challenges around ethics, available resources and institutional preparedness that limit inclusive practice.This theme highlights that equity, inclusion, and inequality continue to be major challenges in higher education, regardless of whether AI is introduced. The collective evidence challenges the assumption that AI technologies can close gaps in access to learning. It stresses the influence of funding, institutional capacity and support on how AI is used and whose needs are prioritised. It calls for greater efforts from institutions, policymakers and leaders to address inequality directly, without relying on AI as a shortcut or one-size-fits-all solution for improving equity, inclusion and equality in higher education.The contributions to this Special Issue highlight the potential of AI technologies to support progress towards the SDGs in higher education while also exposing the limitations of approaches that equate technological adoption with sustainable impacts. The extent to which AI constructively contributes to sustainability depends on how it is governed, resourced and embedded within institutional strategies and leadership practices. Without effective governance and strategic structures and sustained leadership support, AI initiatives may be implemented in piecemeal ways, producing short-term gains that do not support long-term development goals. In addition, the pace of technological change makes robust reviews of responsible AI use more necessary. Assessing pedagogical appropriateness, ethical defensibility and alignment with institutional commitments to the SDGs therefore requires ongoing, strategic efforts for educational leaders, managers and policymakers.Equally importantly, the extant evidence base draws on evidence from a limited group of institutions and countries, which may narrow understanding of how AI and sustainability agendas are interpreted across a variety of higher education systems. Greater inclusion of varied perspectives from both the Global North and the Global South, including exchanges within and across institutions in each context, would, for example, advance an understanding of how global policy frameworks are applied in local settings.
Trinh et al. (Tue,) studied this question.