Artificial Intelligence: Glossary of terms

• AI Literacy – The ability to understand, critically evaluate, and effectively use AI tools in education and everyday life.
• Automated Assessment – AI-powered grading or feedback systems that evaluate student work and provide insights to educators.
• Bias – The presence of unfair or unbalanced perspectives in AI outputs due to biased training data, which can impact educational fairness.
• Chatbot – A conversational AI tool that assists students and educators by answering questions, tutoring, or supporting administrative tasks.

• Creative AI – AI that generates original artistic content, such as stories, poems, music, and artwork.
• Deep Learning – A subset of machine learning that uses multiple layers of artificial neural networks to analyse complex data patterns and improve AI capabilities.
• Ethical AI – The responsible development and use of AI, ensuring fairness, transparency, and accountability in education.
• EdTech – The use of technology, including AI, to enhance teaching and learning experiences.
• Generative AI (GenAI) – A type of artificial intelligence that can create novel text, images, audio, video, and other content by learning patterns from vast datasets.
• Hallucination – When an AI generates false or misleading information that appears credible but is not based on factual data.
• Large Language Model (LLM) – A type of AI trained on massive text datasets to generate human-like language and perform various text-based tasks (e.g., ChatGPT, Google Gemini, Anthropic Claude).

• The Conversational Nature of Engaging with LLMs as an Educator
  • One of the most powerful aspects of Large Language Models (LLMs) is their conversational nature. Unlike traditional search engines or static teaching materials, LLMs allow educators to engage in a dynamic, back-and-forth interaction—almost like having a virtual assistant / critical friend.LLMs respond in real time, meaning educators can:
    • Ask follow-up questions – If an answer isn’t quite right, you can refine your question or ask for clarification.
    • Explore different perspectives – LLMs can generate multiple viewpoints on a topic, supporting critical thinking.
    • Role-play different scenarios – Want to simulate a historical debate or practice a parent-teacher conversation? LLMs can adapt to different roles.For example, if you’re planning a lesson on Shakespeare, you could ask:
      • “Summarise Macbeth in simple terms for 12-year-olds.”
      • “Now explain it as if you were a drama teacher focusing on character emotions.”
      • “Give me three discussion questions for an advanced literature class.
  • This interactive approach makes LLMs a flexible and responsive tool, helping educators refine ideas, create engaging materials, and adapt content for different learners—all through natural conversation.

• Multimodal AI – AI that can process and generate multiple types of content, such as text, images, and audio, simultaneously.
• Natural Language Processing (NLP) – A branch of AI that enables computers to understand, interpret, and generate human language in a meaningful way.
• Neural Networks – AI models inspired by the human brain that process information through interconnected layers of nodes (neurons) to recognize patterns and make predictions.
• Prompt Engineering – The practice of crafting effective inputs (prompts) to guide AI models in generating accurate and relevant outputs.
• Reinforcement Learning from Human Feedback (RLHF) – aligns generative AI models, particularly LLMs, with human preferences by using human feedback to train a reward model, which then guides the AI through reinforcement learning to produce more desirable outputs. 

Tokens – The basic units of text that AI models process, which can be words, parts of words, or characters, depending on the model. AI systems use tokens to break down and generate text efficiently