AI in Education: A Comprehensive Glossary for Educators
Apr 29, 2025The world of artificial intelligence (AI) in education is expanding rapidly. With so many platforms offering a wide range of features and buzzwords, it can be overwhelming for educators to keep up. Whether you’re exploring AI tools for the first time or looking to deepen your understanding, this glossary offers a clear and practical guide to the most searched and significant AI terms in education today.
1. AI Teaching Assistant
An AI-powered helper that automates repetitive tasks like grading, feedback, and student queries. Unlike a general chatbot, it is typically embedded within a course or subject to provide context-specific support, and unlike an AI tutor, it’s often more focused on administrative and support functions than teaching concepts directly.
See how AI Teaching Assistants are transforming education →
2. AI Avatar
A digital, AI-powered character that interacts with learners through speech, motion, or visual cues. AI avatars can appear in different formats: as animated chatbot profile images, voice-enabled assistants in mobile apps, or full-body characters in virtual or augmented reality (VR/AR) environments. While a chatbot may only use text or voice, an avatar adds a visual, often animated presence. Compared to an AI teaching assistant or tutor, an avatar is usually a front-end experience, often powered by those systems behind the scenes.
Make AI Teaching Authentic with Avatars →
3. Chatbot
An AI tool that responds to questions or prompts via text or voice. Chatbots are usually designed to support learners by answering FAQs, providing resources, or guiding users through a learning journey. They are often simpler than AI teaching assistants and lack personalized learning pathways that tutors or adaptive systems provide.
Explore the role of chatbots in education →
4. Inclusive Education
The principle of offering equal learning opportunities to all students. AI supports this through tools that personalize learning, translate content, provide text-to-speech or speech-to-text functionality, and adapt to various learning needs and accessibility requirements.
Explore Universal Design for Learning →
5. LMS AI Integration
When artificial intelligence is built into Learning Management Systems (LMS), enabling features like learning analytics, automated feedback, content recommendations, and personalized pacing within existing course platforms. Often includes chatbots, AI TAs, or analytics dashboards built into LMS environments.
See how teachers are integrating AI tools →
💡 Related Reading:
- What We’ve Learned from Designing Chatbots for Education
- AI for Good: Supporting Inclusive Classrooms
6. Generative AI Content
AI that creates original content—such as lesson plans, quizzes, slides, or even feedback—based on prompts or templates. Popular tools include ChatGPT, Canva Magic Write, or Quizizz AI.
7. AI Teachers
AI systems or assistants that deliver instructional content, simulate teaching scenarios, and respond to student progress in real time. Some platforms use “AI Teacher” and “AI Tutor” interchangeably. However, AI Teachers are typically designed to manage a broader learning experience—delivering lessons, overseeing classroom-level activities, or scaling instruction across many learners.
How AI Tutors Are Revolutionizing Teaching →
8. Virtual Avatar
A subtype of AI avatar, used in immersive or virtual reality (VR) settings. These avatars can interact with students as characters in a simulation—e.g., a patient in a medical simulation or a client in a business role-play.
See 5 Ways to Customize Educational Chatbots →
9. Adaptive Learning
A method where AI continuously assesses student performance and adjusts content delivery accordingly. Adaptive systems may use dashboards, quizzes, or branching content to support mastery-based progression and targeted intervention.
Why Adaptive Learning Is the Future →
10. 2-Sigma Problem
A concept introduced by Benjamin Bloom that highlights the learning gains students achieve from one-on-one tutoring—typically two standard deviations (2 sigmas) higher than students in a traditional classroom setting. This means tutored students often perform better than 98% of their peers. The challenge is that such personalized instruction is difficult to scale. AI-powered tools like adaptive learning platforms and AI tutors aim to bridge this gap by simulating the benefits of one-on-one instruction in scalable, digital formats.
11. Natural Language Processing (NLP)
The technology that allows AI to understand and respond to human language. Enables conversational AI tools, grading automation, summarization, and content generation.
Explore NLP in Southeast Asian Classrooms →
12. Machine Learning
A branch of AI where algorithms learn from data to improve over time. In education, this powers personalization, predictions (e.g., dropout risk), recommendation engines, and adaptive content.
How Conversational AI Works in Practice →
13. Prompt Engineering
The skill of writing effective prompts to get the best results from AI tools.
10 Ways Educators Can Use GPT-3 →
💡 Recommended Reads:
14. Hallucination (in AI)
When AI generates information that sounds correct but isn’t. Teachers using generative AI for lesson planning or student feedback should always double-check for accuracy.
More in the Generative AI FAQ →
15. Large Language Model (LLM)
An AI system trained on massive text datasets to understand and generate language. LLMs, like ChatGPT, are at the heart of most education-focused AI tools.
Everything Teachers Need to Know About GPT-4 →
16. Ethical AI Use
Refers to responsible AI practices that respect data privacy, equity, and transparency. This includes how data is collected, how biases in AI models may impact learning outcomes, and how decisions made by AI systems are explained and governed.
How We Safeguard Student Data →
17. AI Bias
Bias in AI occurs when the data used to train AI models reflects existing stereotypes or inequalities. In education, AI bias can manifest in unfair grading, skewed feedback, or misrepresentation of diverse learners. Understanding and mitigating bias is critical for using AI ethically and inclusively.
18. AI Marking and Feedback
AI tools that grade written assignments based on rubrics and offer suggestions for improvement.
3 AI Grading Hacks to Save Time →
19. Personalized Learning Pathways
AI-curated sequences of content tailored to each learner’s pace, strengths, and goals.
Using AI to Personalize Learning Journeys →
20. Digital Pedagogy
Digital pedagogy is the art and science of teaching with technology. It goes beyond using tools and focuses on intentional design for digital learning environments. With AI, digital pedagogy includes integrating AI in lesson planning, feedback, communication, and assessment in meaningful, learner-centered ways.
21. AI-Powered Course Authoring
Tools that help teachers design courses with the help of AI. These platforms suggest activities, generate resources, align outcomes, and check for content gaps.
22. AI-Powered Assessment
Tests or quizzes enhanced with AI that can adapt in difficulty, detect guessing patterns, or auto-grade different types of responses.
23. Learning Analytics
The collection and analysis of student data, often enhanced by AI, to help educators track engagement, predict outcomes, and tailor support.
As AI becomes more embedded in teaching and learning, understanding these terms is key to making confident, informed decisions. Whether you’re experimenting with AI for the first time or ready to scale its use across your curriculum, this glossary is your starting point.
👉 Want more insights or a hands-on workshop to get started with AI in your classroom?
Join our free AI in Education Boot Camp or explore how our Concierge Services can support your school or institution.
