For institutions focused on quality, continuous and well-resourced support for digital learning (provided by both faculty-based teams and central teaching centres) is no longer optional. It is a strategic necessity.

The Context of Technology in Teaching

We must address the obvious issue: educational technology has frequently been overhyped, and not every tool delivers on its promises. However, dismissing it entirely because of these disappointments is akin to abandoning teaching because a few lectures fall flat.

The evidence tells a sophisticated story. John Hattie’s (2023) updated synthesis shows that specific interventions—such as intelligent tutoring systems—can have a greater impact on learning than traditional policy moves like reducing class sizes. Yet, Hattie warns against treating this data as a simple league table. The impact is rarely about the tool itself; it is about the “underlying story” of how that tool is intentionally aligned with teaching strategies. As Laurillard (2013) notes, technology can amplify effective pedagogy, but it cannot replace the craft of teaching itself.

Innovation and Quality

Universities face a double challenge: they must innovate to stay relevant while rigorously maintaining educational standards. Innovation here means adapting practices to serve students in a rapidly changing world (Smith, 2012).

The rise of artificial intelligence is a prime example. The 2025 EDUCAUSE AI Landscape Study indicates that institutions are currently grappling with fundamental questions of strategy and policy. As students increasingly adopt AI tools, universities must shift their focus to how they engage with these technologies to support actual learning outcomes.

This engagement forces us to rethink the human role, for example in automated systems. Recent dialogues suggest moving away from a “Human-in-the-Loop” model—where humans merely correct technical errors—to a “Technology-in-the-Human-Loop” framework. In this view, human judgement, social contracts and personal narratives wrap around the technology, ensuring AI supports educational encounters rather than dictating them (Forsler et al., 2025). Building the capacity to do this requires dedicated expertise.

“Third Space” Professionals: Bridging the Gap

A key part of this capacity lies with staff who operate in the gap between academic teaching and digital innovation. In research, these are often called “third space professionals”: staff whose work blends academic and professional domains (Whitchurch, 2008).

These roles include learning designers, educational technologists and digital learning advisors. Whether embedded within faculties or situated in central Teaching & Learning Units, modern universities rely on both to:

  • Translate pedagogy: Turning educational theory into practical digital activities.
  • Navigate options: Helping academics choose the right tools from a crowded marketplace.
  • Ensure inclusion: Designing online experiences that are accessible to all students.
  • Build skills: Supporting staff in developing their own digital competencies.
  • Bridge the gap: Facilitating communication between IT departments and teaching staff.
  • Evaluate tools: Assessing new technologies for broader adoption.

By providing specialised expertise, these roles support teaching at scale, aiming to enhance and sustain the work of academic staff (Simpson, 2025).

Current Challenges in the Sector

Despite surging demand for digital learning support—particularly since the pandemic—research highlights several structural challenges (Mitchell et al., 2025):

  • Visibility: These roles fit awkwardly into traditional higher education hierarchies. If leadership does not fully grasp their scope, integrating this expertise into broader strategies becomes difficult.
  • Precarious Employment: A significant portion of support staff are on fixed-term or project-based contracts. When projects or resources end and staff leave, institutional knowledge walks out the door with them.
  • Career Pathways: Without clear routes for progression, retention is a struggle. Losing experienced professionals means losing the contextual knowledge of “what works” in that specific institution.
  • Confusing Terminology: Job titles vary wildly—”learning technologist”, “educational designer”, “educational developer”, “digital advisor”—often used interchangeably. This inconsistency complicates professional identity and recruitment (Simpson, 2025).
  • Investment: As AI and flexible delivery reshape the sector, investment in these support roles remains inconsistent across institutions.

Strategic Approaches to Support

What does effective support actually look like? Observations of successful practice suggest the following approaches:

  • Strategic Integration: Institutions that view digital support as core to their vision—rather than just a technical service—adapt more successfully. Kanuka (2010) argues that effective development centres must be multi-layered and enjoy long-term backing from top leadership.
  • Addressing “Pain Points”: Good support listens to the critics. It acknowledges valid concerns about digital burnout or dehumanised classrooms. Rather than blindly defending technology, strategic support identifies where these issues are symptoms of broader educational problems and addresses the root causes (Forsler et al., 2025).
  • Hybrid Models: The most effective structures often combine embedded support (close to the teaching context) with central expertise. This ensures disciplinary relevance while maintaining consistency and efficiency (Bearman et al., 2024).
  • Strategies over Styles: Support structures must move teachers away from the persistent myth of “learning styles,” which has a negligible impact on achievement (Hattie & O’Leary, 2025). Instead, capacity building should focus on teaching adaptable learning strategies. The goal is to help students adjust their approach—such as self-questioning or summarising—based on the task’s complexity, not a fixed personality preference.
  • Professional Development: Support staff need continuous development just as much as academic staff. This includes engagement with research and communities of practice (Borko, 2004).
  • Designing Differently: As technology evolves, there is a growing emphasis on ethical design. This means prioritising tools that are human-friendly and ensuring diverse voices—including students—are involved in the design process (Forsler et al., 2025).
  • Collaboration: Outcomes are strongest when academics and support professionals work as true partners, combining subject expertise with learning design knowledge (Stoltenkamp et al., 2016).

Conclusion

Higher education is currently navigating shifting demographics, tight funding and rapid technological change. As institutions respond, the quality of the educational experience remains the primary focus.

Technology, when backed by proper infrastructure and expertise, offers ways to improve flexibility, feedback and collaboration. However, these benefits are not automatic; they result from deliberate design and ongoing support. The evidence suggests that institutions which prioritise strategic investment in digital learning support are far better positioned to maintain educational quality and resilience in the years ahead.

References

Bearman, M., Chandir, H., Mahoney, P., & Partridge, H. (2024). Sustaining teaching and learning innovations: A scoping review. Higher Education Research & Development.

Borko, H. (2004). Professional development and teacher learning: Mapping the terrain. Educational Researcher, 33(8), 3–15.

EDUCAUSE. (2025, February 17). 2025 EDUCAUSE AI Landscape Study: Into the Digital AI Divide.

Forsler, I., Hayes, S., Jandrić, P., Macgilchrist, F., Selwyn, N., & Hansén, S. (2025). Hijacking the Digital Backlash in Education. Postdigital Science and Education.

Hattie, J. (2023). Visible Learning: The Sequel. A Synthesis of Over 2,100 Meta-Analyses Relating to Achievement. Routledge.

Hattie, J., & O’Leary, T. (2025). Learning Styles, Preferences, or Strategies? An Explanation for the Resurgence of Styles Across Many Meta-analyses. Educational Psychology Review, 37(2), 31.

Kanuka, H. (2010). Characteristics of effective and sustainable teaching development programmes for quality teaching in higher education. Higher Education Management and Policy, 22(2), 1–14.

Laurillard, D. (2013). Teaching as a design science: Building pedagogical patterns for learning and technology. Routledge.

Mitchell, K., Dave, K., Hinze, M., & Tsirgialos, A. (2025). A narrative account of third space technology enhanced learning and teaching roles working in Australian higher education. Journal of Learning Development in Higher Education, 33.

Simpson, C. (2025). Why can’t higher education agree on terminology for third-space professionals? Journal of Learning Development in Higher Education, 33.

Smith, K. (2012). Lessons learnt from literature on the diffusion of innovative learning and teaching practices in higher education. Innovations in Education and Teaching International, 49(2), 173–182.

Stoltenkamp, J., van de Heyde, V., & Siebrits, A. (2016). The third-space professional: A reflective case study on maintaining relationships within a complex higher education institution. Journal of Student Affairs in Africa, 4(2), 13–24.

Whitchurch, C. (2008). Shifting identities and blurring boundaries: The emergence of third space professionals in UK higher education. Higher Education Quarterly, 62(4), 377–396.