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Individual AI Competencies

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

AI literacy is not just about using tools—it's about developing critical competencies that enable safe, effective, and ethical integration of AI into nursing practice. This page outlines the core competencies every nursing student should develop.

AI Literacy Framework for Nursing​

Foundation Level: Understanding AI​

Knowledge Competencies

  • Understand what AI is and how it works
  • Recognize different types of AI (LLMs, image generation, etc.)
  • Identify AI capabilities and limitations
  • Understand training data and bias
  • Recognize hallucinations and errors

Application to Nursing

  • Explain AI in patient-friendly language
  • Identify when AI might be helpful in clinical scenarios
  • Recognize when AI is inappropriate or unsafe
  • Understand AI's role in healthcare systems

Intermediate Level: Critical Evaluation​

Analytical Competencies

  • Evaluate AI outputs for accuracy
  • Identify bias in AI responses
  • Assess clinical relevance and safety
  • Compare AI information with evidence-based sources
  • Recognize when to seek human expertise

Nursing Application

  • Verify AI-generated clinical information against NICE guidelines
  • Identify potential bias in patient care recommendations
  • Assess whether AI suggestions align with NMC standards
  • Determine when AI advice requires clinical validation

Advanced Level: Ethical Integration​

Professional Competencies

  • Apply ethical frameworks to AI use
  • Maintain patient confidentiality with AI tools
  • Practice academic and professional integrity
  • Advocate for responsible AI use
  • Contribute to AI policy development

Nursing Application

  • Ensure AI use aligns with NMC Code
  • Protect patient data when using AI
  • Disclose AI use appropriately
  • Participate in institutional AI governance
  • Advocate for patient-centered AI implementation

Core Competency Domains​

1. Technical Competence​

Basic Skills

  • Navigate AI interfaces effectively
  • Craft clear, specific prompts
  • Interpret AI outputs correctly
  • Troubleshoot common issues
  • Manage AI tool settings

Nursing-Specific Skills

  • Use AI for care plan development
  • Generate patient education materials
  • Create clinical scenario simulations
  • Develop study resources
  • Assist with literature reviews

Self-Assessment Questions

  1. Can you use at least two different AI tools effectively?
  2. Do you understand how to improve prompts for better results?
  3. Can you identify when AI output is incorrect or biased?
  4. Do you know how to verify AI information?
  5. Can you explain AI limitations to patients or colleagues?

2. Information Literacy​

Source Evaluation

  • Distinguish AI from authoritative sources
  • Verify AI claims against evidence
  • Recognize when citation is needed
  • Identify primary vs. secondary sources
  • Evaluate research quality

Evidence-Based Practice

  • Use AI to find research (with verification)
  • Critically appraise AI summaries
  • Cross-reference with peer-reviewed sources
  • Apply evidence hierarchy
  • Maintain research integrity

Nursing Application

  • Never rely solely on AI for clinical decisions
  • Always verify against NICE, NMC, or Cochrane
  • Use AI as starting point, not endpoint
  • Maintain evidence-based practice standards
  • Document information sources properly

3. Critical Thinking​

Analytical Skills

  • Question AI assumptions
  • Identify logical fallacies
  • Recognize oversimplification
  • Detect missing context
  • Evaluate completeness

Clinical Reasoning

  • Apply nursing process independently
  • Use AI to supplement, not replace, thinking
  • Maintain clinical judgment
  • Recognize when AI lacks nuance
  • Prioritize patient safety

Reflection Questions

  • Did I think through this problem before using AI?
  • Am I using AI as a crutch or a tool?
  • Can I explain my reasoning without AI?
  • Would I be confident defending this decision?
  • Does this align with my clinical judgment?

4. Ethical Awareness​

Privacy and Confidentiality

  • Never input patient-identifiable data
  • Understand data retention policies
  • Recognize privacy risks
  • Protect sensitive information
  • Comply with GDPR

Academic Integrity

  • Disclose AI use when required
  • Cite AI appropriately
  • Distinguish own work from AI assistance
  • Avoid plagiarism
  • Maintain honesty

Professional Integrity

  • Align AI use with NMC Code
  • Maintain accountability
  • Practice transparency
  • Uphold professional standards
  • Advocate for ethical AI

5. Communication Skills​

Explaining AI

  • Describe AI in accessible language
  • Explain limitations to patients
  • Discuss AI use with colleagues
  • Educate others about AI
  • Address concerns and misconceptions

Professional Communication

  • Disclose AI use appropriately
  • Document AI-assisted decisions
  • Collaborate on AI integration
  • Provide constructive feedback
  • Share learning experiences

Competency Development Pathway​

Year 1: Foundation​

Learning Outcomes

  • Understand basic AI concepts
  • Use AI tools safely
  • Recognize limitations
  • Practice ethical use
  • Develop critical evaluation skills

Activities

  • AI literacy workshops
  • Guided AI exploration
  • Critical analysis exercises
  • Ethical case discussions
  • Reflection on AI use

Assessment

  • AI knowledge quiz
  • Prompt crafting exercise
  • Critical evaluation assignment
  • Ethical scenario analysis
  • Reflective portfolio entry

Year 2: Integration​

Learning Outcomes

  • Apply AI to nursing scenarios
  • Evaluate clinical relevance
  • Integrate with evidence-based practice
  • Maintain professional standards
  • Develop independent judgment

Activities

  • AI-enhanced care planning
  • Clinical scenario analysis
  • Research literature reviews
  • Patient education material creation
  • Peer teaching sessions

Assessment

  • AI-assisted care plan (with justification)
  • Critical appraisal of AI outputs
  • Evidence-based practice assignment
  • Professional portfolio
  • Peer evaluation

Year 3: Mastery​

Learning Outcomes

  • Demonstrate advanced AI literacy
  • Lead AI integration initiatives
  • Mentor peers
  • Contribute to policy
  • Advocate for responsible use

Activities

  • Complex clinical decision support
  • Quality improvement projects
  • AI policy development
  • Student mentoring
  • Research participation

Assessment

  • Advanced clinical scenarios
  • Leadership project
  • Policy contribution
  • Mentoring evaluation
  • Capstone portfolio

Self-Assessment Tool​

Rate Your Competence (1-5)​

Technical Skills

  • I can use multiple AI tools effectively (1-5)
  • I craft clear, effective prompts (1-5)
  • I troubleshoot AI issues independently (1-5)
  • I understand AI capabilities and limitations (1-5)

Critical Evaluation

  • I verify AI information against authoritative sources (1-5)
  • I identify bias and errors in AI outputs (1-5)
  • I assess clinical safety of AI suggestions (1-5)
  • I know when to seek human expertise (1-5)

Ethical Practice

  • I protect patient confidentiality with AI (1-5)
  • I disclose AI use appropriately (1-5)
  • I maintain academic integrity (1-5)
  • I align AI use with NMC Code (1-5)

Professional Application

  • I integrate AI with evidence-based practice (1-5)
  • I use AI to enhance, not replace, clinical reasoning (1-5)
  • I communicate AI use effectively (1-5)
  • I contribute to responsible AI culture (1-5)

Scoring

  • 16-32: Developing - Focus on foundation skills
  • 33-48: Competent - Continue building expertise
  • 49-64: Proficient - Ready for advanced integration
  • 65-80: Expert - Mentor others and lead initiatives

Development Resources​

Self-Directed Learning​

  • Online AI literacy courses
  • Nursing informatics resources
  • Professional body guidance
  • Peer learning groups
  • Reflective practice

Institutional Support​

  • Workshops and training
  • Mentoring programs
  • Communities of practice
  • Policy guidance
  • Technical support

Professional Development​

  • Continuing education
  • Conference attendance
  • Research participation
  • Policy involvement
  • Leadership opportunities

Competency Maintenance​

Continuous Learning​

  • Stay current with AI developments
  • Engage with new tools
  • Participate in professional development
  • Share knowledge with peers
  • Reflect on practice

Regular Review​

  • Self-assess competencies annually
  • Seek feedback from mentors
  • Identify development needs
  • Set learning goals
  • Track progress

Next: Explore Module Integration to see how these competencies are developed through curriculum.