10 A. Glossary of Key Terms
- This section provides a simple, accessible glossary to aid in understanding AI-related terms.
Glossary for Educators: Understanding Gen-AI and AI-Related Terms
Part 1: Core Concepts
- Generative AI (GenAI): AI that generates new content, such as text, images, or audio, based on learning from vast amounts of data. Useful for creating teaching materials and enhancing student engagement.
- Machine Learning (ML): A subset of AI where algorithms learn from data to make predictions or decisions without being explicitly programmed for specific tasks.
- Neural Networks: Computational models inspired by the human brain’s architecture, enabling AI to recognize patterns and perform complex tasks like image and speech recognition.
- Data Training: The process of teaching AI to understand and generate content by exposing it to large datasets.
- Natural Language Processing (NLP): AI’s ability to understand, interpret, and generate human language. Essential for tools like automated grading and feedback systems.
- Personalized Learning: Using AI to tailor education to individual student needs, preferences, and learning paces, often through adaptive learning systems.
- Chatbots: Conversational AI that can simulate human-like interactions, aiding in personalized learning and student engagement.
- Adaptive Learning: AI systems that adapt educational content and difficulty based on real-time feedback from students’ performance.
- GPT (Generative Pre-trained Transformer): A type of AI model known for its ability to generate coherent and contextually relevant text based on prompts. “Custom GPT” is used to refer to the custom chatbot (like ChatGPT) that can be created by users through the GPT Builder offered by Open AI.
- AI Ethics: The study of moral implications and best practices for designing, developing, and deploying AI technologies responsibly, especially concerning fairness, privacy, and inclusivity.
- Automated Grading: The use of AI to evaluate student assignments, providing quick and consistent feedback.
- Bias in AI: The tendency of AI systems to reflect the prejudices existing in their training data, leading to unfair outcomes or decisions.
- Continuous Learning: The ability of an AI system to learn and adapt to new information post-deployment, ensuring its outputs remain relevant and accurate.
- Deployment: The process of making an AI model available for use, whether in an educational app, website, or another platform.
This glossary aims to provide educators with a foundational understanding of key GenAI and AI-related terms, supporting their integration into educational practices.
Part 2: Extension Concepts
- AI-Enabled Content Creation: Techniques for generating educational materials using AI.
- Collaborative AI: Systems designed to work alongside humans, enhancing learning experiences.
- Ethics in AI Education: Teaching and integrating ethical considerations of AI use.
- AI in Assessment: Using AI to support the evaluation of student learning.
- Digital Literacy in AI: Skills required to use AI tools effectively in education or other contexts.
- AI for Inclusive Education: Tools designed to accommodate diverse learning needs.
- AI-Driven Curriculum Design: Using AI to tailor educational programs to learning outcomes.
- Predictive Analytics in Education: Applying AI to predict student performance and outcomes.
- AI in Educational Research: Leveraging AI for insights into learning processes and effectiveness.
- AI for Classroom Management: Tools to assist teachers in managing classroom activities.
- Virtual Tutors: AI systems providing personalized tutoring to students.
- AI for Language Learning: Tools using NLP for language teaching and practice.
- AI in STEM Education: Specific applications of AI in teaching STEM subjects.
- Ethical Data Use in Education: Principles governing the ethical use of data in educational AI applications.
- AI for Educational Games: The use of AI in designing and personalizing educational gaming experiences.
These additional terms encapsulate a broader spectrum of AI’s role in education, reflecting its potential to transform teaching, learning, and administrative processes within educational contexts.
