Generative AI in Higher Education Teaching & Learning: Roles & Responsibilities (Academic Support Units)

! Generative)AI)in)Higher)Education)Teaching)&)Learning)) Roles & Responsibili,es: Academic Support Units Contributors James O’Sullivan Colin Lowry Ross Woods Tim Conlon HEA Genera?ve AI Policy Framework hFps://hub.teachingandlearning.ie/genai/policy-framework HEA Genera?ve AI Resource Portal hFps://hub.teachingandlearning.ie/genai/ Version 1.0, December 2025 DOI: 10.82110/h445-6b89 Higher Educa?on Authority, Dublin How to cite: O’Sullivan, James, Colin Lowry, Ross Woods & Tim Conlon. Genera&ve AI in Higher Educa&on Teaching & Learning: Roles & Responsibili&es (Academic Support Units). Dublin: Higher Educa?on Authority, 2025. DOI: 10.82110/h445-6b89. This document, and all original content contained within, is licensed under the Crea?ve Commons AFribu?on-ShareAlike 4.0 Interna?onal Public License (CC BY-SA 4.0). 1 Academic support units occupy a critical intermediary position in the governance of generative AI in higher education. Libraries, teaching and learning centres, disability and access services, academic integrity offices, and careers services provide the connective infrastructure through which institutional strategy is translated into everyday academic practice. While often characterised as ‘supports’, such services are essential academic infrastructure and are uniquely positioned to enable consistency, equity, and sustainability in how generative AI is understood and used across institutions. A primary responsibility of academic support units is the provision of AI literacy and professional development at scale. Reliance on ad hoc workshops, informal peer learning, or voluntary engagement is insufficient in a context where generative AI is reshaping core academic practices. Support units must work collaboratively to embed AI literacy into staff development pathways, new staff induction, and continuing professional development programmes. Similarly, student-facing provision should be integrated into orientation, skills modules, and ongoing academic development initiatives. This provision should be coordinated, coherent, and aligned with institutional policy, ensuring that staff and students encounter consistent guidance rather than fragmented or contradictory messages. Equity of access and inclusion are central obligations for support units in an AI-enabled learning environment. The integration of generative AI must be approached with particular care in relation to students with disabilities, neurodiverse learners, and those studying through an additional language. Support units should ensure that AI tools and related institutional practices do not inadvertently reinforce existing barriers or create new forms of exclusion. This includes resisting assumptions that AI systems automatically enhance accessibility, and actively guarding against forms of techno-ableism in which technological solutions are treated as substitutes for inclusive design. Accessibility, universal design principles, and reasonable accommodations must remain foundational, with AI evaluated as one component within a broader support ecosystem. Academic support units also play a central role in curating, maintaining, and disseminating authoritative resources relating to generative AI. Teaching staff require access to concrete, discipline-sensitive exemplars of AI-resilient assessment design, model statements on permitted and prohibited AI use, and evidence-based case studies of effective practice. By curating such materials, support units reduce duplication of effort, promote coherence across programmes, and help ensure that institutional practice remains grounded in shared principles rather than individual experimentation alone. This curatorial role should be ongoing, with resources reviewed and updated in response to technological change and pedagogical feedback. In addition, support units have a responsibility to contribute to student well-being and ethical reflection 2 in relation to generative AI. Services such as writing centres, academic integrity offices, counselling services, and careers guidance are often the first points of contact for students navigating uncertainty around AI use. These services must be equipped to provide clear, non-judgemental guidance on disclosure practices, responsible engagement with AI tools, and the ethical dimensions of authorship, labour, and accountability. Careers services, in particular, have a role in helping students understand how AI-related skills and ethical awareness translate into professional contexts beyond the university. Academic support units serve as key feedback channels for institutional learning and policy development. Through their close engagement with staff and students, they are well positioned to identify emerging challenges, unintended consequences, and areas of confusion or inconsistency in AIrelated practice. By systematically feeding this intelligence back into institutional governance structures, support units help ensure that policies governing generative AI remain responsive, proportionate, and grounded in the realities of teaching and learning. Through these combined functions, academic support units sustain the institutional capacity required to engage generative AI as an educational issue rather than a purely technical one. Their work underpins the credibility, inclusivity, and coherence of the university’s response to generative AI, ensuring that policy intent is realised in practice.