Generative AI in Higher Education Teaching & Learning Roles & Responsibilities (Teaching Staff)

! Generative)AI)in)Higher)Education)Teaching)&)Learning)) Roles & Responsibili,es: Teaching Staff 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/bsb6-kd92 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 (Teaching Staff). Dublin: Higher Educa?on Authority, 2025. DOI: 10.82110/bsb6-kd92 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 staff occupy a central governance role in an AI-enabled higher education system. As the primary designers of curricula, assessments, and learning environments, they act as the first line of institutional responsibility for ensuring that the integration of generative AI aligns with disciplinary standards, academic integrity, and agreed learning outcomes. Their responsibility is neither to normalise the uncritical adoption of AI tools nor to impose blanket prohibitions that are pedagogically unsustainable, but to exercise informed academic judgement about when, how, and whether AI should be integrated into teaching and assessment practices, with reference to institutional policy and disciplinary norms. A core responsibility of teaching staff lies in assessment design. In an environment where generative AI systems can produce fluent text, code, and problem-solving outputs on demand, traditional assessment formats that rely on easily replicable outputs are no longer sufficient as sole indicators of student learning. Academic staff must ensure that assessments are deliberately constructed to remain valid and meaningful in the presence of AI. This entails a shift towards assessment practices that emphasise process, interpretation, contextual reasoning, and reflective judgement, rather than the production of artefacts. Where conventional assessment formats are retained, staff should be able to justify their continued use in light of AI capabilities and demonstrate how academic integrity is safeguarded. Teaching staff are also responsible for making expectations around generative AI use explicit and intelligible to students. Each assessment brief should clearly articulate what forms of AI use are permitted, what uses are conditional or require acknowledgement, and what uses are prohibited. These statements should be framed as part of the pedagogical work of helping students to understand ethical authorship and responsible tool use. Consistency in such statements across modules and programmes is essential to ensure fairness, but this consistency must be achieved through shared principles rather than rigid uniformity, allowing for legitimate disciplinary variation and protecting academic freedom as it relates to teaching. Professional competence in relation to generative AI is now an integral component of academic practice. While institutions bear responsibility for providing access to training, guidance, and support, teaching staff have a professional obligation to engage with these opportunities and to maintain a baseline level of AI literacy relevant to their teaching role. This includes an understanding of what generative AI systems can and cannot do, the risks they pose to assessment validity and student learning, and the broader ethical, legal, and social implications of their use in educational contexts. Staff should not be expected to become technical experts, but they are expected to exercise informed 2 judgement grounded in ongoing professional development, provided adequate supports are put in place to reasonably allow them to do so. Academic staff must also model responsible and transparent use of AI in their own professional practice. Where generative AI tools are used to support the preparation of teaching materials, formative feedback, or other pedagogical resources, this use should be disclosed to students in an appropriate and proportionate manner. Such disclosure reinforces norms of academic integrity and attribution, and it positions AI as a supplementary tool that operates under human oversight rather than as a substitute for scholarly expertise or pedagogical responsibility. By making their own practices visible, staff contribute to a learning culture in which AI is approached critically. Teaching staff have a collective responsibility to contribute to institutional learning about generative AI, which includes sharing effective practices, reporting emerging challenges, and participating in departmental or institutional discussions about policy refinement. Generative AI is not a static technology, and policies governing its use must evolve in response to pedagogical experience as well as technical change. Academic staff, as practitioners embedded in disciplines and classrooms, are uniquely positioned to inform this evolution and to ensure that institutional responses remain grounded in educational values rather than reactive compliance. These responsibilities position teaching staff as active stewards of academic standards in a period of rapid technological change which threatens the principles of higher education. Their role is essential to ensuring that higher education continues to serve its core educational and public purposes in an AImediated environment.