Skip to main content

Institutional Considerations

Attribution

Original work: "Educators' guide to multimodal learning and Generative AI" — Tünde Varga-Atkins, Samuel Saunders, et al. (2024/25) — CC BY-NC 4.0
Adapted for UK Nursing Education by: Lincoln Gombedza, RN (LD)
Last Updated: December 2025

Deploying AI platforms at scale within a nursing school or university requires coordination across several departments. This page outlines the systemic factors to consider.

1. Procurement and Licensing​

  • Centralized vs. Individual: Does the university provide an enterprise license for tools like Microsoft Copilot (Enterprise) or Google Gemini (Enterprise), or are departments expected to purchase their own?
  • Predictable Budgeting: Many AI tools use "metered" pricing (pay per use) or monthly subscriptions. Universities typically require fixed, annual pricing for budgeting.
  • Equality of Access: Institutional licenses prevent the "digital divide" where only students who can afford $20/month subscriptions have access to the most powerful models (e.g., GPT-4 or Claude 3.5 Sonnet).

2. Data Security & Data Protection (DP)​

Nursing educators must ensure that patient-led scenarios and student data remain private.

  • "Opt-Out" of Training: Ensure your institutional agreement specifies that user data will not be used to train future iterations of the AI model.
  • Local Cloud Hosting: Whenever possible, choose providers that host data within the UK or EEA to simplify GDPR compliance.
  • Impact Assessments: Work with your Data Protection Officer (DPO) to complete a Data Protection Impact Assessment (DPIA) before any large-scale deployment.

3. Training and Support​

Simply providing a link is not enough. Implementation requires a support structure.

  • Staff Upskilling: Dedicated time for educators to learn "Prompt Engineering" specifically for nursing pedagogy.
  • Student Onboarding: Workshops during orientation weeks to explain the responsible use of AI and the institution's specific toolkit.
  • Technical Support: The IT helpdesk needs to be equipped to handle issues specific to AI tools (e.g., login errors with SSO, API rate limits).

4. Academic Integrity & Policy​

The institution needs a clear, unified stance on AI.

  • Consistent Grading Policies: Can a student use AI to summarize a research paper for a reflective essay? Can they use it for brainstorming? Policies should be consistent across modules to avoid student confusion.
  • Disclosure Standards: Establish a standard format for students to "declare" their AI use (e.g., an AI use appendix).

5. Leading Change​

Moving a nursing department toward AI-enhanced education is a cultural shift.

  • AI Champions: Identify "early adopters" among nursing faculty to mentor others.
  • Evidence Base: Collect and share internal case studies showing how AI has improved either the student experience or staff productivity.
  • Ethical Leadership: Ensure the school leadership models the transparent and ethical use of AI.

Next: Move to the Design Framework to learn how to integrate these tools directly into your curriculum.