Universities need more than AI tools to become future-ready

Higher education institutions are investing in artificial intelligence (AI), learning analytics and sustainability, but many still treat them as separate agendas rather than linked pillars of institutional transformation, claims new research that suggests that universities preparing for Industry 5.0 need stronger leadership, clearer governance, ethical AI practices and better alignment between digital transformation and sustainability goals.

The study, titled "The Role of Artificial Intelligence, Learning Analytics, and Sustainability for Future-Ready Universities," was published in Sustainability. Based on semi-structured interviews with 29 leaders from public, private, research-focused and teaching-oriented higher education institutions, the research uses the Technology-Organization-Environment framework and the Triple Bottom Line model to examine how university leaders view AI, learning analytics and sustainability in strategic planning.

AI is improving efficiency, but ethics and governance remain weak

Universities are adopting AI most visibly in administrative operations, where automation can reduce repetitive work, improve reporting, support scheduling and streamline communication. Leaders interviewed in the research described AI as most advanced in operational settings, while its use in teaching and learning remains more limited and experimental. That pattern suggests that many institutions are turning first to low-friction efficiency gains before attempting deeper pedagogical change.

Higher education institutions face rising pressure to cut costs, respond faster to students, manage complex data systems and compete in increasingly digital education markets. AI tools can help process institutional data, answer routine student questions, support admissions workflows and improve internal decision-making. For leaders dealing with constrained budgets and growing service expectations, those gains are immediate and measurable.

However, the same leaders also identified some serious risks. Bias, privacy, data misuse and unclear accountability emerged as major barriers to AI adoption. These concerns are especially important in universities because AI systems can affect student assessment, academic advising, admissions, hiring, research support and institutional planning. If algorithms reproduce bias or operate without clear oversight, the consequences can reach students, faculty and staff across the institution.

The interviews show that AI adoption depends less on technology availability alone and more on institutional readiness. Staff training was repeatedly identified as a critical condition. Universities may buy AI tools, but employees need the confidence and skill to use them responsibly. Without AI literacy, adoption can become uneven, mistrusted or limited to small pilot projects.

Governance gaps are another major concern. Many institutions lack clear rules for how AI should be used, who is accountable for its outputs, how privacy should be protected and how ethical risks should be reviewed. This is a major weakness because AI can move faster than policy. When institutions deploy tools before building governance systems, they risk creating fragmented practices across departments.

The findings also show differences between types of universities. Research-focused institutions were more likely to view AI as a tool for data analysis, research support and competitive advantage. Teaching-oriented institutions were more likely to emphasize chatbots, student services and operational support. Public institutions often relied on grants and government-backed infrastructure, while private institutions were more likely to partner with technology firms.

These gaps are concerning because there is no single path to AI integration. A large research university may have the technical capacity to experiment with advanced analytics, while a teaching-oriented institution may focus on tools that improve student support. Still, the strategic challenge is common: AI must be connected to institutional mission, ethical oversight and human capacity.

The results suggest that AI adoption will fall short if it is limited to productivity gains; institutions need to connect it with governance, staff training, academic strategy and sustainability planning to build real readiness.

Learning analytics can improve student success but faces cultural resistance

Learning analytics is widely seen as a critical tool for student success and institutional planning. By tracking patterns in student engagement, performance and progression, universities can identify students at risk of dropping out, improve advising, adjust curricula and support retention. Leaders across the interviews recognized its potential for early intervention and evidence-based decision-making.

In many institutions, student support remains reactive. Students may receive help only after grades fall, attendance drops or financial pressure becomes visible. Learning analytics can shift universities toward earlier and more targeted support by detecting risk signals before failure becomes difficult to reverse. This is important as universities face demands to improve completion rates and support diverse student populations.

Dropout analytics also emerged as a significant use case. Predictive tools can help institutions identify patterns associated with withdrawal or non-completion. Private institutions appeared more likely to use these tools for retention, while research-focused universities tended to integrate analytics into broader strategic planning. Teaching-oriented institutions often used analytics for operational efficiency and student services.

However, cultural and technical barriers limit adoption, with faculty resistance emerging as one of the strongest themes. Some instructors distrust data-driven recommendations, worry about surveillance or fear that analytics will undermine academic autonomy. Others lack the training needed to interpret dashboards or act on predictive alerts. This implies that learning analytics is not only a technical project, it is a cultural change project.

Legacy systems add another layer of difficulty. Older institutional platforms often do not communicate well with one another, making it hard to build real-time dashboards or connect student records, learning management systems and advising tools. Without integrated infrastructure, universities may generate data but fail to convert it into useful action.

Privacy concerns also remain central. Learning analytics depends on student data, and institutions must balance the benefits of early intervention with the risks of excessive monitoring. Students and faculty may resist analytics if they do not know what data are collected, how they are used, who can access them or how long they are retained. Trust becomes a core condition for effective implementation.

The research points to a gap between identifying risk and acting on it. Universities may flag struggling students, but academic advisors and faculty may lack the time, resources or coordination needed to intervene. This creates a practical limitation: analytics can reveal problems, but it cannot solve them without human support systems.

The strongest institutions are likely to be those that use learning analytics as part of a broader student success strategy. That means combining predictive tools with advising capacity, faculty engagement, transparent data policies and student-centered communication. Analytics should not become a surveillance mechanism or a substitute for human judgment. It should help universities make better, faster and fairer decisions.

This is where the Technology-Organization-Environment framework is useful. The technology may exist, but adoption depends on organizational culture, leadership commitment, infrastructure and external pressures such as accreditation, competition and policy demands. The findings show that these conditions remain uneven across higher education.

Sustainability is valued, but fragmented implementation slows transformation

Leaders viewed sustainability as important for reputation, compliance, environmental responsibility and institutional resilience. Still, many institutions struggle to embed sustainability into core academic and operational strategies.

Universities are adding sustainability concepts to courses, programs and learning outcomes. This reflects a wider recognition that graduates need the skills to address climate change, resource use, social equity and responsible innovation. However, interdisciplinary sustainability education remains limited in many institutions. Sustainability is often added to selected courses rather than embedded across the university.

Operational initiatives are also visible. Institutions are using smart technologies, energy-efficiency programs, waste reduction policies and green campus strategies to improve environmental performance. Public universities were more likely to report infrastructure-based initiatives such as solar systems and smart grids, while private institutions sometimes relied more on carbon offsets or partnership-based approaches.

Sustainability officers reported that institutional mission statements often support sustainability, but daily operations and departmental accountability do not always follow. Leadership endorsements may not translate into budgets, metrics or cross-campus coordination. This creates a gap between commitment and implementation.

Scope tracking is another weak area. Many institutions can measure direct energy use or campus-level initiatives, but they struggle to track indirect emissions linked to travel, procurement, commuting, supply chains and outsourced services. Without stronger measurement, universities cannot fully assess their environmental footprint or demonstrate progress toward sustainability targets.

The Triple Bottom Line model helps explain why the issue is broader than environmental policy. Future-ready universities must balance economic viability, environmental stewardship and social responsibility. Sustainability is not only about green buildings or emissions. It is also about equity, community engagement, digital justice, responsible AI and institutional resilience.

The research also highlights a major missed opportunity: AI, learning analytics and sustainability are rarely integrated in one strategic framework. Digital tools could help universities track emissions, optimize resource use, improve energy management, monitor sustainability learning outcomes and guide institutional planning. Yet many institutions manage these domains in silos.

Breaking those silos will require stronger leadership. Leaders need to connect academic affairs, IT, sustainability offices, finance teams and student support systems. Future-ready universities cannot be built through isolated projects. They need governance structures that link technology adoption with ethical values and sustainability outcomes.

The analysis also has limits that must be considered. The research is qualitative and based on interviews with 29 higher education leaders, so it captures leadership perceptions rather than broad statistical patterns across the entire sector. The sample includes diverse institutions, but the results cannot be generalized to all universities without further quantitative testing.

Future research using correlation and regression analysis could test measurable relationships among AI adoption, learning analytics use, sustainability integration and institutional performance.