Exploring paediatric conditions through a critical analysis using Copilot

Context

This activity was designed for a cohort of twenty-two Junior Sophister undergraduate children’s and general nursing students. The activity was developed within the context of nursing education to respond to the evolving demands of contemporary healthcare, particularly the growing integration of generative AI in clinical practice. The initiative sought to improve both teaching strategies and student learning outcomes by enhancing digital literacy and promoting a critical understanding of AI technologies. As part of the activity, students used Microsoft Copilot - a generative AI tool that responds to natural language prompts- to explore paediatric renal and endocrine conditions and to develop individualised nursing care plans based on realistic clinical scenarios. Students were also provided with evidence-based resources to assess the accuracy of the AI-generated content and to strengthen their analytical and critical thinking abilities. 

What was your goal in utilising GenAI as part of the teaching process?

The goal of this activity was to enhance both the teaching approach and learning outcomes for children’s and general nursing students and to better prepare them for contemporary clinical practice. In the third year of their training, nursing students are nearing their transition into professional roles, making it essential to equip them with skills relevant to today’s healthcare environment. Our goal was to prepare students for a future in healthcare where generative AI will play an increasingly prominent role in clinical decision-making.

The activity aimed to enhance teaching and learning by fostering digital literacy and developing students’ ability to think critically and ethically about AI-generated content.  By evaluating outputs from Microsoft Copilot against evidence-based resources, students were encouraged to analyse and question information, strengthening their clinical reasoning and decision-making skills. This approach supported the development of essential competencies for safe, effective, and informed practice in a healthcare system increasingly shaped by digital tools and innovations.

How did you use GenAI to enhance teaching, learning and/or assessment?

GenAI was embedded into a structured, multi-stage activity designed for third-year children’s and general nursing students. The activity aligned with key learning outcomes for this module. Preparation began with self-directed learning on Blackboard, where students accessed lecture slides on the role of generative AI in children’s nursing. Students also received step-by-step guidance on using Microsoft Copilot to create nursing care plans.

The core teaching was delivered over several in-person sessions where students were organised into small groups and assigned realistic clinical scenarios involving children with renal or endocrine conditions. Using Microsoft Copilot, each group generated a draft nursing care plan by inputting their scenario as a prompt. These drafts were then discussed collaboratively within groups. Students used their clinical judgment and evidence-based resources to critically evaluate the AI-generated care plans, discussing discrepancies with current guidelines to promote reflection. The activity concluded with student feedback and reflection on their learning experience.

What were the outcomes of using GenAI in this way?

Due to initial disruptions caused by Storm Eowyn in January 2025, initial sessions had to be adapted into a self-directed format, which limited opportunities for student engagement. Later, the activity was delivered in a face-to-face format which allowed for meaningful student participation and collaboration. Evaluation of this activity was conducted through student feedback, both informal and via the module evaluation. Overall, the outcomes were positive. Students found the use of generative AI quick, easy-to-use, and helpful in structuring nursing care plans, which supported their understanding and made the process more efficient.

While students appreciated the interactive and innovative approach, some raised concerns about the accuracy and credibility of AI-generated content. They noted the need to verify information independently, which was time-consuming, and highlighted challenges in obtaining sources relevant to the Irish healthcare context. Nonetheless, many students found the activity relevant and engaging, recognising its alignment with technological trends in healthcare and education.

What did you learn as part of this process, and is there anything you would do differently?

One key learning point was the importance of delivering this activity face-to-face whenever possible. The initial shift to a self-directed format, due to unforeseen disruptions, made it difficult to gauge student engagement, and there were limited opportunities for real-time discussion and feedback. In the future, in-person delivery of this activity would be prioritised to support more interactive learning opportunities for students.

Student feedback also highlighted the need to revise the format of this activity. Rather than focusing on a single scenario, students expressed a preference for engaging with multiple case studies to deepen their understanding of GenAI's applications across various clinical contexts. To accommodate this, adjustments will be made to the students’ timetable to allow more time for exploration, discussion, and feedback. While students appreciated the supporting materials provided, more resources will be provided in future to help them critically appraise AI-generated content with greater confidence. Finally, more detailed feedback will be sought from students to support ongoing improvements to this activity and potential academic dissemination through publication in an academic journal and presentation at a relevant conference.

GenAI Tool Used:

• Microsoft Copilot