Department of Education and Youth & Oide Technology in Education, October 2025
Summary
This national guidance document provides Irish schools with a framework for the safe, ethical, and effective use of artificial intelligence (AI), particularly generative AI (GenAI), in teaching, learning, and school leadership. It aims to support informed decision-making, enhance digital competence, and align AI use with Ireland’s Digital Strategy for Schools to 2027. The guidance recognises AI’s potential to support learning design, assessment, and communication while emphasising human oversight, teacher professionalism, and data protection.
It presents a balanced view of benefits and risks—AI can personalise learning and streamline administration but also raises issues of bias, misinformation, data privacy, and environmental impact. The report introduces a 4P framework—Purpose, Planning, Policies, and Practice—to guide schools in integrating AI responsibly. Teachers are encouraged to use GenAI as a creative aid, not a substitute, and to embed AI literacy in curricula. The document stresses the need for ethical awareness, alignment with GDPR and the EU AI Act (2024), and continuous policy updates as technology evolves.
Key Points
AI should support, not replace, human-led teaching and learning.
Responsible use requires human oversight, verification, and ethical reflection.
AI literacy for teachers, students, and leaders is central to safe adoption.
Compliance with GDPR and the EU AI Act ensures privacy and transparency.
GenAI tools must be age-appropriate and used within consent frameworks.
Bias, misinformation, and “hallucinations” demand critical human review.
The 4P Approach (Purpose, Planning, Policies, Practice) structures school-level implementation.
Environmental and wellbeing impacts must be considered in AI use.
Collaboration between the Department, Oide, and schools underpins future updates.
Guidance will be continuously revised to reflect evolving practice and research.
Conclusion
The guidance frames AI as a powerful but high-responsibility tool in education. By centring ethics, human agency, and data protection, schools can harness AI’s potential while safeguarding learners’ wellbeing, trust, and equity. Its iterative, values-led approach ensures Ireland’s education system remains adaptive, inclusive, and future-ready.
A major new study has delivered a sobering revelation: AI chatbots are significantly failing when it comes to reporting accurate news. This image highlights the frustration and concern arising from AI’s inability to provide reliable information, underscoring the critical need for verification and human oversight in news consumption. Image (and typos) generated by Nano Banana.
Source
Deutsche Welle (DW)
Summary
A landmark study by 22 international public broadcasters, including DW, BBC, and NPR, found that leading AI chatbots—ChatGPT, Copilot, Gemini, and Perplexity—misrepresented or distorted news content in 45 per cent of their responses. The investigation, which reviewed 3,000 AI-generated answers, identified widespread issues with sourcing, factual accuracy, and the ability to distinguish fact from opinion. Gemini performed the worst, with 72 per cent of its responses showing significant sourcing errors. Researchers warn that the systematic nature of these inaccuracies poses a threat to public trust and democratic discourse. The European Broadcasting Union (EBU), which coordinated the study, has urged governments to strengthen media integrity laws and called on AI companies to take accountability for how their systems handle journalistic content.
Key Points
AI chatbots distorted or misrepresented news 45 per cent of the time.
31 per cent of responses had sourcing issues; 20 per cent contained factual errors.
Gemini and Copilot were the least accurate, though all models underperformed.
Errors included outdated information, misattributed quotes, and false facts.
The EBU and partner broadcasters launched the “Facts In: Facts Out” campaign for AI accountability.
Researchers demand independent monitoring and regulatory enforcement on AI-generated news.
As AI continues to disrupt industries, education holds the key to transforming these advancements into unprecedented workforce opportunities. This image visualizes how strategic educational initiatives can bridge the gap between AI innovation and career readiness, equipping individuals to thrive in an evolving job market. Image (and typos) generated by Nano Banana.
Source
World Economic Forum
Summary
Mallik Tatipamula and Azad Madni argue that education systems must evolve rapidly to prepare workers for the AI-native, autonomous, and ethically aligned economy of the future. While AI is expected to displace 92 million jobs globally, it will also create 170 million new roles requiring AI literacy, ethical judgment, and transdisciplinary thinking. The authors call for a “transdisciplinary systems mindset” in education—integrating physical sciences, life sciences, computation, and engineering—to equip graduates with creative, contextual, and ethical reasoning skills that AI cannot replicate. Future success will depend less on narrow technical expertise and more on the ability to collaborate across disciplines, apply systems thinking, and use AI to augment human potential responsibly.
Key Points
AI will both displace and create millions of jobs, demanding rapid educational adaptation.
Education must prioritise AI literacy, ethics, and cognitive resilience alongside technical skills.
A “net-positive AI framework” should ensure technology benefits society and human cognition.
Transdisciplinary curricula combining science, engineering, and ethics are vital for future-ready workers.
Physical AI, data fluency, and human-AI collaboration will become core competencies.
Universities should promote challenge-driven learning and convergence hubs for innovation.
As AI tools become more sophisticated, the challenge of maintaining academic integrity intensifies. This image depicts lecturers undergoing specialised training to hone their skills in identifying AI-generated misconduct, ensuring fairness and originality in student work. Image (and typos) generated by Nano Banana.
Source
BBC News
Summary
Academics at De Montfort University (DMU) in Leicester are receiving specialist training to identify when students misuse artificial intelligence in coursework. The initiative, led by Dr Abiodun Egbetokun and supported by the university’s new AI policy, seeks to balance ethical AI use with maintaining academic integrity. Lecturers are being taught to spot linguistic “markers” of AI generation, such as repetitive phrasing or Americanised language, though experts acknowledge that detection is becoming increasingly difficult. DMU encourages students to use AI tools to support critical thinking and research, but presenting AI-generated work as one’s own constitutes misconduct. Staff also highlight the flaws of AI detection software, which has produced false positives, prompting calls for education over punishment. Students, meanwhile, recognise both the value and ethical boundaries of AI in their studies and future professions.
Key Points
DMU lecturers are being trained to recognise signs of AI misuse in student work.
The university’s policy allows ethical AI use for learning support but bans misrepresentation.
Detection focuses on linguistic patterns rather than unreliable software tools.
Staff warn that false accusations can harm students as much as confirmed misconduct.
Educators stress fostering AI literacy and integrity rather than “catching out” students.
Students value AI for translation, study support, and clinical applications but accept clear ethical limits.
by Jonathan Sansom – Director of Digital Strategy, Hills Road Sixth Form College, Cambridge
Estimated reading time: 5 minutes
Bridging the gap: This image illustrates how Microsoft Copilot can be leveraged in secondary education, moving from a “force analysis” of opportunities and challenges to the implementation of “pedagogical copilot agents” that assist both students and educators. Image (and typos) generated by Nano Banana.
At Hills Road, we’ve been living in the strange middle ground of generative AI adoption. If you charted its trajectory, it wouldn’t look like a neat curve or even the familiar ‘hype cycle’. It’s more like a tangled ball of wool: multiple forces pulling in competing directions.
The Forces at Play
Our recent work with Copilot Agents has made this more obvious. If we attempt a force analysis, the drivers for GenAI adoption are strong:
The need to equip students and staff with future-ready skills.
Policy and regulatory expectations, from DfE and Ofsted, to show assurance around AI integration.
National AI strategies that frame this as an essential area for investment.
The promise of personalised learning and workload reduction.
A pervasive cultural hype, blending existential narratives with a relentless ‘AI sales’ culture.
But there are also significant restraints:
Ongoing academic integrity concerns.
GDPR and data privacy ambiguity.
Patchy CPD and teacher digital confidence.
Digital equity and access challenges.
The energy cost of AI at scale.
Polarisation of educator opinion, and staff change fatigue.
The result is persistent dissonance. AI is neither fully embraced nor rejected; instead, we are all negotiating what it might mean in our own settings.
Educator-Led AI Design
One way we’ve tried to respond is through educator-led design. Our philosophy is simple: we shouldn’t just adopt GenAI; we must adapt it to fit our educational context.
That thinking first surfaced in experiments on Poe.com, where we created an Extended Project Qualification (EPQ) Virtual Mentor. It was popular, but it lived outside institutional control – not enterprise and not GDPR-secure.
So in 2025 we have moved everything in-house. Using Microsoft Copilot Studio, we created 36 curriculum-specific agents, one for each A Level subject, deployed directly inside Teams. These agents are connected to our SharePoint course resources, ensuring students and staff interact with AI in a trusted, institutionally managed environment.
Built-in Pedagogical Skills
Rather than thinking of these agents as simply ‘question answering machines’, we’ve tried to embed pedagogical skills that mirror what good teaching looks like. Each agent is structured around:
Explaining through metaphor and analogy – helping students access complex ideas in simple, relatable ways.
Prompting reflection – asking students to think aloud, reconsider, or connect their ideas.
Stretching higher-order thinking – moving beyond recall into analysis, synthesis, and evaluation.
Encouraging subject language use – reinforcing terminology in context.
Providing scaffolded progression – introducing concepts step by step, only deepening complexity as students respond.
Supporting responsible AI use – modelling ethical engagement and critical AI literacy.
These skills give the agents an educational texture. For example, if a sociology student asks: “What does patriarchy mean, but in normal terms?”, the agent won’t produce a dense definition. It will begin with a metaphor from everyday life, check understanding through a follow-up question, and then carefully layer in disciplinary concepts. The process is dialogic and recursive, echoing the scaffolding teachers already use in classrooms.
The Case for Copilot
We’re well aware that Microsoft Copilot Studio wasn’t designed as a pedagogical platform. It comes from the world of Power Automate, not the classroom. In many ways we’re “hijacking” it for our purposes. But it works.
The technical model is efficient: one Copilot Studio authoring licence, no full Copilot licences required, and all interactions handled through Teams chat. Data stays in tenancy, governed by our 365 permissions. It’s simple, secure, and scalable.
And crucially, it has allowed us to position AI as a learning partner, not a replacement for teaching. Our mantra remains: pedagogy first, technology second.
Lessons Learned So Far
From our pilots, a few lessons stand out:
Moving to an in-tenancy model was essential for trust.
Pedagogy must remain the driver – we want meaningful learning conversations, not shortcuts to answers.
Expectations must be realistic. Copilot Studio has clear limitations, especially in STEM contexts where dialogue is weaker.
AI integration is as much about culture, training, and mindset as it is about the underlying technology.
Looking Ahead
As we head into 2025–26, we’re expanding staff training, refining agent ‘skills’, and building metrics to assess impact. We know this is a long-haul project – five years at least – but it feels like the right direction.
The GenAI systems that students and teachers are often using in college were in the main designed mainly by engineers, developers, and commercial actors. What’s missing is the educator’s voice. Our work is about inserting that voice: shaping AI not just as a tool for efficiency, but as an ally for reflection, questioning, and deeper thinking.
The challenge is to keep students out of what I’ve called the ‘Cognitive Valley’, that place where understanding is lost because thinking has been short-circuited. Good pedagogical AI can help us avoid that.
We’re not there yet. Some results are excellent, others uneven. But the work is underway, and the potential is undeniable. The task now is to make GenAI fit our context, not the other way around.
Jonathan Sansom
Director of Digital Strategy, Hills Road Sixth Form College, Cambridge
Passionate about education, digital strategy in education, social and political perspectives on the purpose of learning, cultural change, wellbeing, group dynamics, – and the mysteries of creativity…
