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: December 2025
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
There is a common belief that GenAI technology can function efficiently as a 'search engine', while, in fact, it 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 emphasize 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.
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, including (but not limited to):
- 📝 Text — Essays, care plans, documentation
- 🗣️ Speech — Text-to-speech for accessibility
- 🎵 Audio — Podcasts, narrated explanations
- 🖼️ Images — Diagrams, infographics, anatomical illustrations
- 🎥 Video — Clinical demonstrations, patient scenarios
- 🎭 Three dimensional models — Anatomical structures, medical devices
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 Question of 'Intelligence'
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:
Latest Models (as of December 2025):
Text Generation & Multimodal Understanding:
- Gemini 3 Pro (Google DeepMind, Nov 2025) - State-of-the-art reasoning and multimodal understanding
- Gemini 3 Flash (Google DeepMind, Dec 2025) - Frontier intelligence at speed
- Gemini 3 Deep Think - Advanced reasoning for complex problems
- GPT-5 (OpenAI, Aug 2025) - Current flagship with enhanced capabilities
- Claude Sonnet 4 (Anthropic, Aug 2025) - Default model with strong performance
- Claude Opus 4.1 (Anthropic, Aug 2025) - Most capable Claude model
Image Generation:
- DALL-E 3 (OpenAI)
- Midjourney v6/v7
- Adobe Firefly
- Stable Diffusion XL
Text-to-Speech:
- ElevenLabs
- Google TTS
- Azure Speech Services
Text-to-Video:
- Runway Gen-2
- Synthesia
- HeyGen
- Google Veo 2 (Dec 2024)
Speech-to-Text:
- Whisper (OpenAI)
- Otter.ai
- Google Speech-to-Text
Image-to-Text & Vision:
- GPT-5 Vision
- Gemini 3 Pro (native multimodal)
- Claude Opus 4.1 Vision
AI models evolve extremely rapidly. The models listed above were current as of December 2025. 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:
Text → Visual Text → Audio/Video → Interactive Audio/Video → Avatars → Immersive Simulations
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.