Generative AI in Higher Education Teaching & Learning: Roles & Responsibilities (Institutional Leadership Teams)

! Generative)AI)in)Higher)Education)Teaching)&)Learning)) Roles & Responsibili,es: Ins,tu,onal Leadership Teams 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/jy6x-q456 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 (Ins&tu&onal Leadership Teams). Dublin: Higher Educa?on Authority, 2025. DOI: 10.82110/jy6x-q456. 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 Institutional leaders hold ultimate accountability for ensuring that generative AI adoption strengthens, rather than undermines, the integrity and mission of higher education. Presidents, registrars, vicepresidents, and governing authorities must not only ensure compliance with legal requirements such as GDPR and the EU AI Act, but also provide visible commitment to values-led adoption. Their first responsibility is governance. Leaders must establish institutional AI governance structures with clear lines of accountability, ensuring that these are embedded within existing academic governance rather than relegated to peripheral committees. These structures should be chaired at senior level and report directly to governing bodies, ensuring that decisions about AI adoption are treated as matters of academic integrity and institutional strategy. AI adoption must not be devolved to individual staff members or local units operating without adequate time, funding, or technical support, nor can responsibility be allowed to fall on precariously employed staff whose contracts, workloads, and institutional standing leave them least able to absorb additional risk. To do so is to formalise uneven practice and to externalise institutional responsibility onto those with the least capacity to carry it. It is critical that AI adoption is not left to the initiative of individual staff or departments without adequate investment and resource provision, and senior leadership therefore bears a direct obligation to provide sustained investment in staff development, AI literacy, and the secure, institution-wide provision of tools, policies, and guidance. Absent such commitment, appeals to a values-based approach to generative AI function less as governance than as displacement, masking a withdrawal of responsibility behind the language of principle. Leadership should ensure policy coherence through an institution-wide framework on AI use in teaching and assessment, developed through consultation, reviewed regularly, and communicated clearly to staff and students. This framework should establish shared principles, minimum safeguards, and baseline expectations, while explicitly protecting academic freedom by affirming the authority of individual teaching staff and disciplines to determine how AI is incorporated, restricted, or excluded within their own pedagogical and assessment contexts. Institutional guidance should enable informed professional judgement, not prescribe uniform practice, and should recognise disciplinary difference as a legitimate and necessary feature of responsible AI governance. As public representatives of the sector, presidents and provosts should advocate for coherent national practice, participate in European and global discussions, and ensure transparently reporting to the public on their institution’s approach. In this way, leadership ensures that AI adoption is strategic, transparent, and aligned with Ireland’s obligations as a European higher education system.