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Institutional AI Literacy Framework

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

An institutional framework provides the foundation for consistent, ethical, and effective AI integration across all nursing programmes and supports sustainable AI literacy development.

Institutional Vision​

Strategic Alignment​

University Strategy

  • Align with institutional digital strategy
  • Support teaching excellence goals
  • Enhance student experience
  • Promote innovation
  • Ensure sustainability

Nursing School Strategy

  • Professional body compliance
  • Quality education delivery
  • Graduate employability
  • Research excellence
  • Sector leadership

Governance Structure​

AI Literacy Steering Group​

Composition

  • Senior leadership (Chair)
  • Programme leaders
  • Module leaders
  • Student representatives
  • Practice partners
  • IT services
  • Library services
  • Quality assurance

Responsibilities

  • Policy development
  • Strategy oversight
  • Resource allocation
  • Quality monitoring
  • Risk management
  • Stakeholder engagement

Meeting Frequency: Quarterly with annual review

Working Groups​

Curriculum Development

  • Design AI-integrated curriculum
  • Create learning resources
  • Develop assessments
  • Share best practices

Staff Development

  • Training programmes
  • Support networks
  • Research opportunities
  • Innovation projects

Quality Assurance

  • Monitor standards
  • Evaluate outcomes
  • Address issues
  • Continuous improvement

Policy Framework​

Institutional AI Policy​

Core Principles

  1. Student-Centered: AI enhances learning
  2. Ethical: Responsible and transparent use
  3. Evidence-Based: Informed by research
  4. Inclusive: Equitable access for all
  5. Sustainable: Environmentally conscious

Policy Components

  • Acceptable use guidelines
  • Data protection requirements
  • Academic integrity standards
  • Assessment regulations
  • Support provisions

Implementation Guidelines​

For Students

  • Clear expectations
  • Disclosure requirements
  • Support resources
  • Consequences of misuse
  • Appeals process

For Staff

  • Teaching guidance
  • Assessment design
  • Tool recommendations
  • Support access
  • Professional development

For Practice Partners

  • Placement policies
  • Mentor guidance
  • Assessment alignment
  • Communication protocols

Infrastructure and Resources​

Technology Infrastructure​

AI Tool Access

  • Institutional subscriptions (ChatGPT, Claude, etc.)
  • Specialized nursing AI tools
  • Integration with VLE
  • Mobile accessibility
  • Technical support

IT Support

  • Help desk services
  • Training resources
  • Troubleshooting guides
  • Security measures
  • Regular updates

Learning Resources​

Digital Library

  • AI literacy guides
  • Video tutorials
  • Case studies
  • Assessment exemplars
  • Research papers

Physical Resources

  • AI literacy workshops
  • Drop-in sessions
  • One-to-one support
  • Peer mentoring
  • Practice spaces

Staff Development Programme​

Training Pathway​

Foundation Level (All Staff)

  • AI basics and terminology
  • Institutional policies
  • Ethical considerations
  • Tool demonstrations
  • Support resources

Intermediate Level (Teaching Staff)

  • Pedagogical integration
  • Assessment design
  • Student support
  • Quality assurance
  • Troubleshooting

Advanced Level (Leaders/Innovators)

  • Strategic planning
  • Research methods
  • Policy development
  • Sector engagement
  • Innovation leadership

Support Mechanisms​

Communities of Practice

  • Regular meetings
  • Shared resources
  • Problem-solving
  • Innovation sharing
  • Peer support

Mentoring

  • Experienced staff support
  • Peer mentoring
  • Cross-disciplinary learning
  • Practice sharing

Research Opportunities

  • Scholarship of teaching
  • Innovation projects
  • Conference presentations
  • Publications
  • Funding support

Quality Assurance Framework​

Monitoring and Evaluation​

Key Metrics

  • Student AI literacy levels
  • Staff confidence and competence
  • Assessment outcomes
  • Academic integrity incidents
  • Stakeholder satisfaction

Data Sources

  • Student surveys
  • Staff feedback
  • Assessment analytics
  • External examiner reports
  • Graduate outcomes

Reporting

  • Quarterly dashboards
  • Annual reports
  • External reviews
  • Sector benchmarking

Continuous Improvement​

Review Cycle

  • Annual policy review
  • Biennial strategy refresh
  • Ongoing practice evaluation
  • Regular stakeholder consultation
  • Evidence-based adjustments

Risk Management​

Identified Risks​

Academic Integrity

  • Undisclosed AI use
  • Over-reliance on AI
  • Plagiarism concerns

Mitigation

  • Clear policies
  • AI-resilient assessment
  • Detection tools (used ethically)
  • Education and support

Equity and Access

  • Digital divide
  • Unequal resources
  • Disability barriers

Mitigation

  • Institutional subscriptions
  • Device loan schemes
  • Accessibility features
  • Alternative provisions

Data Privacy

  • Patient confidentiality breaches
  • GDPR violations
  • Data security

Mitigation

  • Clear guidelines
  • Training
  • Monitoring
  • Incident response

Quality Concerns

  • Inconsistent implementation
  • Variable standards
  • Staff capacity

Mitigation

  • Quality assurance
  • Staff development
  • Resource allocation
  • External validation

Stakeholder Engagement​

Student Partnership​

Involvement

  • Policy co-creation
  • Resource development
  • Peer support leadership
  • Feedback mechanisms
  • Innovation projects

Communication

  • Regular updates
  • Town halls
  • Digital channels
  • Student representatives
  • Feedback loops

Practice Partner Collaboration​

Engagement

  • Joint policy development
  • Mentor training
  • Placement integration
  • Quality assurance
  • Best practice sharing

Professional Body Liaison​

NMC Engagement

  • Standards alignment
  • Policy consultation (NMC Code review 2025-2027)
  • Inspection preparation
  • Sector contribution
NMC AI Standards Timeline

The NMC is currently reviewing its Code to integrate AI standards. Consultation expected Q3-Q4 2026, with publication in October 2027. Institutions should actively participate in the consultation process and align policies with forthcoming standards.

RCN Collaboration

  • Professional development
  • Research partnerships
  • Policy influence
  • Member support

Sustainability​

Environmental Considerations​

Green AI Practices

  • Energy-efficient tools
  • Sustainable procurement
  • Carbon offsetting
  • Environmental education
  • Policy integration

Financial Sustainability​

Funding Model

  • Institutional investment
  • External funding
  • Cost-benefit analysis
  • Resource optimization
  • Long-term planning

Organizational Sustainability​

Change Management

  • Phased implementation
  • Staff buy-in
  • Cultural change
  • Continuous adaptation
  • Leadership commitment

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