What is Generative AI?
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: February 2026
Before proceeding, it is useful to explain what we mean by Generative AI. In fact, this is a useful question to discuss with colleagues and students before using it any way, as there are many common misconceptions.
Common Misconceptions
❌ GenAI is NOT a Search Engine
| Feature | Google Search | Generative AI |
|---|---|---|
| Primary Goal | Find accurate information | Generate relevant responses |
| Source Verification | ✅ Links to sources | ❌ Often no citations |
| Accuracy for Clinical Data | ✅ Reliable (e.g., NICE) | ⚠️ Can hallucinate |
| Best For | Finding evidence-based guidelines | Brainstorming, drafting, summarizing |
GenAI is notoriously unreliable as a means of searching for information, given that it prioritises relevance based on the prompt over accuracy or impartiality of information retrieved (Hemsworth et al., 2024).
For nursing educators: This is particularly important when students might use GenAI to look up clinical information or evidence-based guidelines. Always emphasise the need to verify information against authoritative sources like:
- NICE guidelines
- NMC standards
- Cochrane reviews
- Peer-reviewed nursing journals
How GenAI Actually Works
GenAI employs deep machine learning techniques to process information contained within huge datasets to generate outputs based on human prompts inputted by the user.
The "Next Token Prediction" Model
Most GenAI we refer to are Large Language Models (LLMs) trained on vast amounts of text data to decode, generate, and manipulate human language. However, GenAI is increasingly capable of producing multimodal content.
🎨 Multimodal Capabilities
GenAI can create content across multiple formats:
| Mode | Examples | Nursing Use Case |
|---|---|---|
| 📝 Text | Essays, care plans | Patient documentation drafts |
| 🗣️ Speech | Text-to-speech | Accessibility for learning disabilities |
| 🎵 Audio | Podcasts, narrations | Commute-friendly lectures |
| 🖼️ Images | Diagrams, infographics | Anatomy illustrations |
| 🎥 Video | Demonstrations | Clinical skill tutorials |
| 🎭 3D Models | Anatomical structures | Interactive learning |
🧬 Anatomy of a Prompt
To get the best results from these models, you need to structure your requests effectively. Hover over the parts of this prompt to understand their function:
GenAI technology enables users to create, manipulate, and adapt content and integrate different semiotic forms to produce multimodal artefacts, and thus can be embedded into pedagogical practices that already emphasise diverse modes of engagement.
Defining GenAI's Role
GenAI's rapid development has been accompanied by suggestions on how to define and use this technology:
Co-Intelligence (Mollick, 2024)
Suggests we should consider GenAI to be a 'co-intelligence' that works alongside human intelligence.
Co-Creator (Cope & Kalantzis, 2024)
Suggests that we should understand it as a unique 'co-creator' that works alongside users in an assistive but unique role. They call this cyber-social learning – a collaborative partnership between human and machine intelligences, each with distinct, but complementary, strengths for completing an activity.
They suggest that this collaboration 'enables new processes for knowledge creation', where educators and students learn by evaluating, refining and re-imagining AI outputs, assembling them into multimodal artefacts.
The Human-AI Partnership
While human and artificial intelligences can work together and have a unique role to play within a cyber-social partnership, the term 'intelligence', taken as part of 'Generative AI' can, and perhaps should, be challenged in favour of more specific computer-science-driven terminologies, such as LLMs (large language models).
'Intelligence' implies 'consciousness' that AI simply does not have, despite its (and their parent companies') attempts to lead users into believing it does.
It is GenAI's very lack of 'intelligence', either emotional or intellectual, that highlights how it operates in the cyber-social relationship with a human, whereby both parties occupy unique but symbiotic positions and consequently complement each other.
What GenAI Can Do:
- ✅ Generate and transform multimodal content at scale
- ✅ Work with extreme efficiency
- ✅ Process large amounts of information quickly
- ✅ Create drafts, artefacts and prototypes
What GenAI Cannot Do:
- ❌ Understand context or nuance
- ❌ Demonstrate spontaneity or creativity
- ❌ Interpret emotional states
- ❌ Make critical ethical judgements
- ❌ Provide clinical judgement or accountability
The Bottom Line: GenAI can offer raw material – drafts, artefacts and prototypes - but educators and learners are needed to bring vision, purpose, nuanced critique and meaning-making.
Types of Multimodal GenAI
The guide's aim is to encapsulate strategies for the effective incorporation of GenAI in multimodal teaching, learning, and assessment and to position Generative AI most effectively within the cyber-social relationship with human users.
We interpret 'multimodal GenAI' in different ways:
1. Platform Capabilities
Multimodal GenAI can refer to platform capabilities that utilise modalities beyond text-to-text:
🚀 Frontier Models (February 2026)
Text Generation & Multimodal Understanding
- GPT-5.3 (OpenAI, Feb 2026) - Latest flagship with Codex integration
- GPT-5.2 (OpenAI, Dec 2025) - Deep reasoning and professional knowledge
- Claude Opus 4.6 (Anthropic, Feb 2026) - Most capable for complex agentic tasks
- Claude Sonnet 4.5 (Anthropic, Feb 2026) - Balanced performance for everyday work
- Claude Haiku 4.5 (Anthropic, Feb 2026) - Fast, lightweight responses
- Gemini 3 Pro (Google DeepMind, Jan 2026) - State-of-the-art reasoning
- Gemini 3 Flash (Google DeepMind, Jan 2026) - Frontier intelligence at speed
- Gemini 2.5 Flash-Lite (Google DeepMind) - Cost-efficient for high volume
Image Generation
- DALL-E 3 (OpenAI) - High-quality images from text
- Midjourney v6/v7 - Artistic and photorealistic outputs
- Adobe Firefly - Commercial-safe image generation
- Stable Diffusion XL - Open-source flexibility
Text-to-Speech
- ElevenLabs - Hyper-realistic voice cloning
- Google TTS - Natural-sounding multilingual
- Azure Speech Services - Enterprise-grade reliability
Text-to-Video
- Runway Gen-2 - Creative video generation
- Synthesia - AI avatar presenters
- HeyGen - Personalized video content
- Google Veo 2 (Dec 2024) - High-fidelity video
Speech-to-Text
- Whisper (OpenAI) - Industry-leading transcription
- Otter.ai - Meeting notes and summaries
- Google Speech-to-Text - Real-time transcription
Image-to-Text & Vision
- GPT-5.3 Vision - Understanding images and charts
- Gemini 3 Pro (native multimodal) - Unified text/image processing
- Claude Opus 4.6 Vision - Document and image analysis
AI models evolve extremely rapidly. The models listed above were current as of February 2026. Always check the latest releases from:
Nursing Example: Creating visual care pathways from text descriptions, or transcribing verbal patient handovers.
2. Multimodal Learning Activities
A multimodal learning or teaching activity itself (e.g. a lecture or a virtual simulation) that utilises GenAI within its process (whether GenAI itself is text-to-text or multimodal).
Nursing Example: A simulation where students interact with an AI-generated patient avatar that responds via text and speech.
3. Modal Conversion
Using GenAI to convert one artefact/modality (e.g. slides or images) into another modality (e.g. text or sound).
Nursing Example: Converting a PowerPoint lecture on wound care into a podcast for students to listen to during commute.
The Multimodality Continuum
The following illustrates GenAI capabilities' development in terms of educators' uses of multimodal resources from text to immersive simulation:
We might contend that GenAI currently offers interactive content to learners and educators, via real-time interactivity with avatars or personas. However, just a year ago, this would perhaps have been closer to static textual, or perhaps audio/visual, content.
This guide offers broad principles and approaches rather than platform-specific suggestions to ensure relevance across disciplines and learning contexts. Even during the lifespan of this project (2024/25), GenAI's multimodal capabilities have evolved so rapidly that listing very specific concrete examples risks the information becoming quickly outdated.
For Nursing Educators: Key Takeaways
- GenAI is not a search engine — Don't let students use it as one for clinical information
- GenAI lacks clinical judgement — Human review and verification is essential
- GenAI is a tool, not a replacement — It augments, not replaces, nursing expertise
- Multimodal possibilities are vast — From text to video to simulations
- Evolution is rapid — Stay current but focus on principles, not specific platforms
Next: Continue to the Main Introduction for a comprehensive overview of multimodal learning and nursing context.