Universities need new communities of belonging for the AI era

This blog was kindly authored by Joe D. Lyons, Faculty Senate President at Saint Louis University.

Higher education increasingly speaks the language of flexibility, access and personalisation. Hybrid learning, asynchronous delivery and AI-supported education promise to meet students where they are. Yet universities may be overlooking a deeper challenge: as learning becomes more technologically mediated, student belonging is becoming more fragile.

Traditional university models were built around proximity. Students lived together, learned together and gradually developed intellectual and social identities through repeated interaction. Even institutions without formal collegiate systems relied heavily on the informal infrastructure of campus life: conversations after lectures, shared routines, faculty mentorship and participation in institutional culture.

That model no longer reflects the reality of many students’ lives.

According to Higher Education Statistics Agency data cited recently by HEPI, mature students now account for 63% of the student population in UK higher education. At the same time, universities including Imperial College London, Cardiff University and the University of Oxford are expanding lifelong learning and flexible education initiatives aimed at adult and professional learners. Yet many university structures still implicitly assume a traditional full-time residential undergraduate experience.

This distinction matters because belonging increasingly shapes student success. Research consistently links belonging to retention, engagement and wellbeing. Yet many modern university structures remain transactional in practice. Students move from module to module, platform to platform and assessment to assessment with limited sustained connection to peers, faculty or institutional identity.

Adult and hybrid learners experience this especially acutely. Many already possess careers, families and established professional identities. What they often lack is not independence or maturity but sustained intellectual community, reflective space and meaningful integration between academic learning and professional life.

Artificial intelligence may intensify this fragmentation if universities approach AI primarily as a productivity tool rather than a relational one. A future in which students interact more frequently with algorithms than academic communities risks weakening the social and developmental dimensions of higher education.

The solution is not to reject technology or nostalgically recreate older residential models that no longer fit contemporary learners. Instead, universities may need to rethink what collegiate community means in a post-digital environment.

One possibility is the development of intentional learning communities specifically designed for hybrid and adult learners.

These communities would not necessarily revolve around permanent residence halls or traditional campus immersion. Instead, they would combine small cohort identity, sustained faculty mentorship, practitioner mentorship, periodic in-person intensives and AI-supported developmental coaching into integrated communities of belonging.

Students would belong not only to a degree programme but also to a stable interdisciplinary learning community that remains intact throughout their studies. These communities could include students across multiple professions and life stages, creating intellectual diversity often absent in narrowly structured programmes.

Crucially, the model would involve dual mentorship.

Faculty mentors would support intellectual development, reflection and disciplinary inquiry. Practitioner mentors drawn from industry, government, healthcare, non-profits or civic organisations would help students connect academic learning to professional judgment, ethical decision-making and real-world complexity.

This distinction matters because higher education increasingly struggles to bridge theory and practice. Universities frequently discuss employability, workforce readiness and lifelong learning, yet many students still experience academic and professional development as largely separate worlds.

Practitioner mentors could help learners contextualise knowledge within lived environments while simultaneously helping universities remain more connected to evolving professional realities.

Artificial intelligence also takes on a different role within this model. AI should not replace faculty, mentorship or community. Instead, it can provide connective infrastructure sustaining continuity between human interactions.

AI systems could support reflective journaling, developmental coaching, career exploration, asynchronous cohort dialogue and personalised learning pathways between formal meetings and in-person gatherings. Rather than automating education, AI could help maintain continuity across distributed learning environments where adult learners often struggle to remain connected.

Periodic residential or in-person experiences would remain important, but as intentional moments of immersion rather than permanent modes of attendance. Weekend institutes, retreats, professional residencies, civic engagement experiences and interdisciplinary intensives could

provide opportunities for embodied community and institutional identity formation without requiring traditional residential structures.

In some ways, universities already possess historical models for this approach. Collegiate universities such as Oxford and Cambridge long recognised that intellectual development is inseparable from community. Jesuit education similarly emphasises reflection, accompaniment and whole-person formation alongside disciplinary learning. Modern honours colleges and living-learning communities have attempted related approaches in contemporary settings.

Yet these models have often remained tied to assumptions about full-time residential undergraduates. The next challenge is adapting relational education for learners whose educational lives are distributed across work, home and digital environments.

This challenge may become even more urgent as AI reshapes the nature of expertise itself. When information becomes instantly accessible and routine cognitive tasks increasingly automated, the distinctive value of universities may lie less in information transmission and more in cultivating judgment, discernment, ethical reasoning and human development.

Those capacities emerge relationally.

The risk for higher education is not simply technological disruption. It is the possibility that universities become increasingly efficient while becoming progressively thinner as communities.

In the AI era, the universities that matter most may not be those with the most advanced technology, but those most capable of sustaining meaningful human connection alongside it.