Generative AI in Higher Education Teaching & Learning: AI Literacy Training

This document sets out baseline requirements and design principles for AI literacy training within Irish higher education institutions. Its purpose is to ensure that staff and students develop sufficient technical understanding and ethical awareness of generative AI systems to use them responsibly, recognise their limitations, and act in accordance with institutional policy and public-sector obligations.

The guidance treats AI literacy as both risk mitigation and institutional capacity building. It argues that ethical guidance without technical understanding is ineffective, while technical fluency without ethical grounding risks normalising uncritical or harmful use. Effective AI literacy therefore integrates an explanation of how generative systems work, including why they produce fluent but unreliable outputs, with principled judgement about proportionality, transparency, accessibility, data protection, and continuous improvement in everyday academic practice.

Rather than prescribing a single curriculum, the document defines expected outcomes, governance structures, and resourcing requirements for sustainable training. It emphasises that AI literacy cannot be delivered as an unfunded mandate or a one-off compliance exercise. Meaningful training requires dedicated staffing, accessible delivery platforms, regular content review, and discipline-specific translation so that institutional principles can be applied in context.

The guidance proposes differentiated training for distinct audiences. All students should complete an orientation prior to engaging in assessed work where generative AI may be relevant. All staff should complete a core literacy programme reflecting their broader responsibilities, with additional role-specific training for those involved in assessment design, procurement, data handling, and institutional approval. Senior leaders require targeted briefings focused on oversight, risk appetite, and escalation responsibilities.

Core content areas include the classification of AI systems, the mechanics and failure modes of large language models, ethical and proportionate use, data sovereignty and vendor incentives, and the geopolitical and regulatory contexts in which AI systems operate. Particular emphasis is placed on critical vendor literacy, accessibility obligations, and the distinction between institutionally governed deployments and public-facing tools.

The document also addresses governance, quality assurance, and integration with institutional processes, including record-keeping, review cycles, and clear communication with staff and students. It positions AI literacy as a foundational component of responsible generative AI adoption in higher education, supporting both educational quality and public accountability.

This guidance is intended for institutional leaders, educators, professional services staff, learning developers, and policymakers involved in the design, delivery, and governance of AI literacy programmes. It should be read alongside the HEA’s National Policy Framework and associated resources hosted on the HEA Generative AI Resource Portal.