Featured image source: Yutong Liu & Kingston School of Art / https://betterimagesofai.org / https://creativecommons.org/licenses/by/4.0/
MICHAEL FEGAN FCCT, ST COLMAN’S COLLEGE, NEWRY, UK
Introduction
St Colman’s College, Newry, first engaged with AI in late 2023. What began as curiosity soon highlighted a wider need for structure, clarity and a whole-school approach that could harness AI’s potential while safeguarding both teacher and student agency. Forecasts from the World Economic Forum (2025) identify AI and big data, technological literacy and creative thinking as among the fastest growing skills by 2030, emphasising the importance of preparing young people for an AI-enabled world.
At the same time, evidence shows that many young people struggle to evaluate AI critically. An international study by EY (Merriman and Sanz Sáiz, 2024) found that almost half of Gen Z, the generation now moving into further study and employment, performed poorly when asked to identify limitations in AI systems, including their tendency to fabricate information. This reinforced the need for explicit teaching around critical evaluation and responsible use.
This case study outlines how we developed a whole-school approach to ethical and purposeful AI use and how it has continued to evolve through teacher learning and responsive policy implementation. It highlights the decisions that helped us to manage risk while supporting creativity and agency across the school.
Foundations
From the outset, we adopted an evidence-informed approach, and the research made it clear that we needed more than surface-level familiarity with AI tools. Teachers could use the tools, but many lacked deeper understanding of how AI systems operate, how to judge the reliability of outputs and how to apply professional judgement in a rapidly changing landscape. Teachers also raised concerns about academic integrity, the erosion of key skills and the risks of adopting new methods without clear guidance.
Pratschke (2024) suggests that the first critical step towards successful integration of AI in education is developing AI literacy, and this aligned closely with our strategy. Through a series of initial professional learning workshops, teachers explored how generative AI (GenAI) works, its limitations, common misconceptions and the implications for learning and teaching. These sessions helped teachers to move beyond simple tool use and begin to engage with AI in a more informed and critical way.
This shared learning directly informed the development of our ‘Generative AI Policy’. The policy provides clarity, establishes expectations and helps teachers to make consistent decisions about when and how AI might support their work. It offers a framework that grounds our early innovation in a clear, safe and purposeful direction. The purpose of the policy is not to police AI use, but to empower teachers as they explore new ways of working with emerging technologies.
Professional learning
Developing teacher confidence was central to our strategy. Early whole-school sessions helped to establish a common baseline, but staff feedback showed that more tailored support was needed. We therefore moved to a model that prioritised choice and subject relevance.
Professional learning was offered through online courses, small-group sessions and one-to-one support. As confidence grew, teachers increasingly requested training linked to specific tools, creating a demand-led model shaped by their roles and responsibilities.
Our approach was informed by Davis’s (1989) technology acceptance model, which highlights that perceived usefulness and ease of use are key factors in whether individuals choose to adopt new tools. This guided our emphasis on practical, low-stakes experimentation, allowing teachers to build confidence before applying AI within classroom contexts.
The technological pedagogical and content knowledge framework also helped to shape our thinking. Developed by Mishra and Koehler (2006), it identifies the knowledge that teachers need to integrate technology effectively into classroom practice. By situating AI within the intersections of pedagogy, content and technology, we encouraged teachers to make intentional, context-sensitive decisions rather than viewing AI as an add-on. This supported more thoughtful experimentation and helped teachers to make informed choices.
Responsive policy
As teacher confidence increased, we saw growing experimentation with custom AI agents and student-facing tools. This demonstrated a willingness to innovate, but it also raised important questions about privacy and responsible use.
To support emerging practice, we introduced a set of practical guardrails. One early development involved teachers designing custom AI agents to scaffold learning and act as a personalised tutor. To ensure safe use, we created a protocol outlining standards around data protection, transparency and curriculum alignment while preserving teacher autonomy.
Included within the protocol was a recommendation to survey students before and after using these agents to evaluate impact.
We also began trialling AI tools to transcribe and summarise meetings. Although efficient, this raised concerns about consent, data handling and the potential capture of sensitive information. In response, we established a process requiring clear communication with participants, the right to decline recording and defined data-handling procedures. This allowed us to benefit from workload efficiencies without compromising trust.
Additionally, academic integrity became a key focus as students engaged more widely with GenAI tools. Traditional indicators of plagiarism were no longer reliable, so we made a shift from detection to dialogue. We developed procedures to support teachers in addressing suspected misuse and provided students with guidance on transparency and attribution. This helped to reposition integrity as something to be taught and discussed.
These steps created conditions where innovation could develop within clear, shared expectations. They also prompted deeper conversations about curriculum design, student learning and assessment.
AI literacy and assessment
We took a similar approach to support students. AI literacy is now embedded in our Key Stage 3 digital skills curriculum, replacing earlier one-off workshops. Students explore potential AI career pathways and engage with issues such as bias, reliability and data ethics. This has helped to normalise AI as a topic for inquiry and has enabled students to develop confidence as thoughtful users.
Growing student engagement prompted us to reflect on assessment. It became clear that traditional definitions of plagiarism were no longer satisfactory in an environment where hybrid human and AI writing is becoming normal. Eaton’s (2023) principles of postplagiarism helped us to understand how concepts of authorship and originality are shifting in an AI-enabled world. At the same time, this remains an area of tension as schools continue to balance new possibilities for learning with legitimate concerns around integrity, fairness and accountability.
To respond constructively, we are actively exploring the AI Assessment Scale developed by Perkins et al. (2024). This offers a continuum of AI use, from prohibited to fully permitted support, and is helping us to frame discussions about when and why AI may be appropriate.
Together, these frameworks have encouraged us to rethink assessment in ways that prioritise student agency. We are beginning to explore tasks that emphasise process, reflection and personal voice, recognising that creativity can grow when the role of AI is acknowledged openly.
Outcomes and early impact
Evidence from teacher and student surveys shows several emerging benefits. Teachers report growing confidence in using AI to support administrative tasks and resource creation, alongside greater clarity about when AI is appropriate. Informal sharing of practice has also increased across departments.
Students show increased awareness of AI’s capabilities and limitations and greater ease when engaging with AI-assisted tasks. Conversations about integrity, purpose and authorship have become more open and informed. Embedding AI literacy at Key Stage 3 has helped to position students as active participants rather than passive users.
We have seen shifts in how teachers understand risk. Rather than avoiding AI, teachers are engaging with it more critically and creatively. Our policy and accompanying structures have provided a shared language for discussing challenges and helped to create a safer environment for experimentation.
Conclusion
Developing an ethical and creative approach to AI at St Colman’s College has led to more than a set of protocols. It has fostered a culture rooted in transparency and reflective, shared professional responsibility. By connecting policy with day-to-day practice and investing in teacher and student understanding, we have begun to navigate the opportunities and challenges of AI in a deliberate and principled way.
Although the landscape continues to evolve, our experience suggests that strengthening integrity, encouraging creativity and supporting teachers and students to act with confidence are essential to meaningful AI integration. Through reflective leadership and collaboration, we are moving beyond compliance towards a culture where AI supports, rather than replaces, human judgement and purposeful learning. We hope that our experience offers a useful reference point for others as they shape their own approaches.
The examples of AI use and specific tools in this article are for context only. They do not imply endorsement or recommendation of any particular tool or approach by the Department for Education or the Chartered College of Teaching and any views stated are those of the individual. Any use of AI also needs to be carefully planned, and what is appropriate in one setting may not be elsewhere. You should always follow the DfE’s Generative AI In Education policy position and product safety standards in addition to aligning any AI use with the DfE’s latest Keeping Children Safe in Education guidance. You can also find teacher and leader toolkits on gov.uk.
