This analysis presents findings from the HEA’s ongoing survey of how generative artificial intelligence is being used in teaching and learning across Irish higher education institutions. The survey is open to contributions and will be periodically updated as new submissions are received and reviewed.
Based on voluntary submissions, this analysis highlights the work of those currently engaging with GenAI and does not necessarily reflect the full diversity of views or experiences across the sector.
Updated June 2025
Institutional landscape
To date, the database has captured 24 responses from 12 institutions, providing a limited snapshot of the Irish higher education sector.
Currently, University College Cork accounts for 24% of participation, followed by Dublin City University (20%), Trinity College Dublin (12%), and Mary Immaculate College and the University of Limerick (8%). There was one submission each from Atlantic Technological University, Munster Technological University, Technological University Dublin, University College Dublin, University of Galway, South East Technological University, and the National College of Art & Design.
This distribution suggests varying levels of institutional engagement with the mapping exercise, but also, it potentially reflects differences in strategic priorities, resources, or cultures across institutions.
As the database expands with further submissions, a more comprehensive picture of the institutional landscape is expected to emerge.
Primary focus areas
As expected in a survey focused on teaching and learning, 76% of responses identify teaching practice or innovation as a primary focus, establishing pedagogy as the central driver of AI adoption.
With 56% of initiatives focusing on student support or engagement, there is clear evidence of learner-centric design principles guiding AI integration.
The data reveals balanced attention to technical implementation (40% focusing on digital tools/platforms) and ethical considerations (36% addressing ethical/policy matters). This suggests mature institutional approaches that recognise both opportunities and responsibilities in AI adoption, which aligns with the HEA’s Ten Considerations for Generative Artificial Intelligence Adoption in Irish Higher Education.
The distribution of AI initiatives across academic disciplines reveals unexpected patterns that challenge assumptions about technology adoption in higher education. While Information and Communication Technologies shows expected engagement (32%), the highest participation comes from Teaching and Learning (60%) and Education (40%). Notably, Arts and Humanities demonstrates significant engagement at 28%, equal to Business disciplines, suggesting humanities scholars are actively embracing AI possibilities.
Every major disciplinary area shows some level of AI engagement, from Natural Sciences (20%) to Agriculture and Veterinary studies (8%). This comprehensive coverage indicates AI is being recognised as a general-purpose technology with applications across all knowledge domains.
Implementation
With 60% of initiatives operating at module level, individual educators emerge as key drivers of AI experimentation. This bottom-up innovation suggests organic adoption patterns driven by pedagogical need rather than top-down mandate.
Nearly half (48%) of initiatives operate at institutional level, indicating significant strategic commitment.
The presence of both module-level and institutional-level activity suggests healthy innovation ecosystems combining individual creativity with organisational support.
Particularly significant is the 24% of initiatives operating across multiple institutions. This inter-institutional collaboration suggests recognition that AI challenges transcend individual organisational boundaries and require collective response.
Thematic Analysis
Rapid Automatic Keyword Extraction (RAKE) is an algorithm designed to pull salient key phrases from a single piece of text without any prior training data. It offers researchers a quick way to surface the main conceptual hooks in textual data. Typical uses include rapid tagging of open-ended survey answers, generating descriptive metadata for document repositories, or highlighting emerging themes in policy consultations.
The top-ranking keyphrases—such as “formative feedback” (18.7), “assessment design” (16.9), and “creative writing tasks” (15.6)—signal a commitment to using AI not for automation alone, but to support more nuanced and student-centred forms of teaching.
This is reinforced by the notable presence of “student agency” (14.8), a term that underlines the sector’s focus on empowering learners within AI-enabled environments, and suggests an emphasis on more responsive and personalised pedagogical models.
Ethical considerations occupy a prominent position in the keyphrase ranking. “Academic integrity” (17.4) and “ethical considerations” (16.3) are among the highest-scoring items, indicating that institutions are not treating AI’s ethical challenges as peripheral concerns.
The analysis also highlights the human infrastructure necessary for sustainable AI adoption. Phrases such as “staff professional development” (15.4) and “staff confidence building” (11.8) point to an awareness that technological integration is not simply about tools or systems, but about people. Supporting educators through targeted professional development efforts reflects an understanding that confidence and competence must go hand-in-hand. Without such support structures, the risk is that AI initiatives falter due to gaps in staff preparedness or resistance rooted in uncertainty and unease.
Several other keyphrases—”discipline-specific prompts” (15.2), “policy guidance” (14.7), and “curriculum mapping” (14.1)—reveal a growing sophistication in how AI is being contextualised within disciplinary and institutional frameworks. These phrases reflect not only experimentation but also the codification of AI practices into curriculum design and institutional strategy.
Conclusions
The projects mapped to date reveal that, among those staff and institutions who have adopted AI in the context of teaching and learning with Irish higher education, there is a commitment to balancing innovation and ethics.
As initiatives mature from experimental modules to programme-level transformation, maintaining this balance of innovation, ethics, and pedagogical focus will be crucial for realising AI’s educational potential while preserving academic values.
The multi-discipline and collaborative spirit evident in individual and cross-institutional initiatives provides a strong foundation for continued development.
By building on these foundations, strengthening assessment practices, scaling successful initiatives, and maintaining ethical leadership, Irish higher education can continue pioneering thoughtful, educationally-focused AI integration that enhances rather than replaces human educational relationships.