Artificial intelligence is creating a growing challenge for higher education in Ireland, with academics warning that poorly managed use of the technology could weaken confidence in third-level qualifications. Speakers at a conference in Dublin said universities and technological universities need clearer policies, stronger funding and more direct engagement with students to protect academic integrity.
AI and Irish qualifications: what happened?
The warning was issued at an AI in third-level education conference organised by the Irish Federation of University Teachers and the Teachers’ Union of Ireland. The event took place at Technological University Dublin and examined how colleges should respond to generative AI in teaching, assessment and student work.
Policies across the sector remain under development. Academics said institutional guidance is struggling to keep pace with tools that can generate essays, summaries, code and other material in seconds.
The central concern is not simply whether students use AI. It is whether they can use it in a way that supports learning while still demonstrating their own knowledge, reasoning and judgement.
Why academic integrity is under pressure
A survey presented by the unions found that almost 90 per cent of academics had encountered issues involving AI either frequently or occasionally. The figures cited at the conference indicated that 54 per cent experienced such problems frequently and 33 per cent occasionally.
In the same survey, 83 per cent of academics said they remained concerned about academic integrity and plagiarism. Those concerns extend beyond individual assignments. If institutions cannot reliably establish what a student understands, the credibility of the qualification itself may be questioned.
That issue affects students, lecturers, employers and professional bodies. Third-level awards are intended to signal that a graduate has met defined academic standards. Assessment methods that do not distinguish between independent work and unverified machine-generated material may make that signal less reliable.
Why face-to-face contact matters
Speakers argued that personal engagement with students will remain important as colleges adapt assessment. Conversations, presentations and supervised work can help academics establish whether a student understands the ideas contained in a submission.
Individual interaction may also provide a clearer picture of how a student reached an answer, rather than relying solely on the finished document. However, academics acknowledged that this approach requires additional staff time and cannot be introduced at scale without resources.
- Almost 90 per cent of surveyed academics reported encountering AI-related issues.
- Most respondents remained concerned about academic integrity and plagiarism.
- In-person engagement could help assess genuine understanding.
- Academics warned that monitoring and redesigned assessment require funding.
Funding is central to the response
James O’Sullivan of University College Cork said monitoring student use of AI can be expensive and time-consuming. He questioned how institutions would pay for additional assessment, staff and support when higher education was already facing funding pressures.
The conference heard that colleges with enough resources to appoint full-time staff in this area were seeing stronger progress. Many institutions, however, do not have the capacity to create dedicated roles or provide extensive training for every academic and student.
This makes the debate about AI policy inseparable from the wider question of public investment in higher education. Clear rules alone will not resolve the problem if staff do not have the time or tools to apply them consistently.
Different approaches across third-level institutions
One approach discussed at the conference would allow students to use AI, provided they can explain and critically evaluate the output. Under such a model, students might be required to disclose how a tool was used and demonstrate that they checked its accuracy.
Other assessments may restrict or prohibit AI, particularly where the purpose is to test unaided knowledge, writing or technical ability. The appropriate approach can vary according to the learning outcome.
Denise MacGiolla Rí of the Technological University of the Shannon described a tiered model of AI integration, ranging from prohibition to full engagement. The principle is that academics should decide what level of use is appropriate based on what students are expected to learn.
What students may need to show
As policies develop, students may increasingly be expected to:
- Declare when and how AI tools were used.
- Check generated information against reliable sources.
- Explain the reasoning behind their submitted work.
- Demonstrate practical or verbal understanding in person.
- Follow course-specific rules rather than assuming one policy applies everywhere.
Students should therefore consult their college’s assessment guidance before using generative AI. A tool that is permitted for brainstorming may not be allowed to produce an assessed answer.
Follow further education developments and student guidance at DailyDigest.ie.
What happens next for Irish higher education?
The conference indicates that Ireland’s third-level sector is still moving towards a consistent response. Colleges will need to balance innovation with fairness, ensuring that students who follow the rules are not disadvantaged while also avoiding blanket policies that prevent useful learning applications.
Likely areas of continued work include assessment redesign, staff training, student guidance and investment in academic integrity systems. Institutions will also need to communicate rules clearly, particularly as AI tools change rapidly.
The debate is not about removing technology from education. It is about ensuring that technology does not replace the learning and judgement that qualifications are meant to represent.
Frequently asked questions
Does the conference call for a total ban on AI?
No. Speakers discussed a range of approaches, from prohibiting AI in particular assessments to allowing wider use where students can explain, verify and take responsibility for the result.
Why are academics concerned about qualifications?
If submitted work does not reliably show a student’s own understanding, employers and the public may have less confidence in the meaning of a third-level award.
Who is responsible for setting the rules?
Individual higher education institutions and course teams set assessment requirements, while national funding and policy decisions influence how effectively those rules can be implemented.
Conclusion
The conference warning places AI at the centre of Ireland’s education debate. Used carefully, it may support teaching and learning; used without clear boundaries, it could weaken trust in third-level qualifications. The immediate priority is a properly funded system that combines transparent rules, meaningful student engagement and assessments capable of showing genuine understanding. For students, the key message is simple: check the policy, disclose permitted use and be prepared to demonstrate your own work.