AI Lingo

AI Lingo

AI Lingo is a glossary of terms used in artificial intelligence. For most people, these terms may seem unfamiliar. Still, as you learn more about this technology, you'll realize that they form a secret language that people in the industry use to communicate. The language of technology, particularly AI, can be challenging to understand, especially if you're new to the field. But don't worry, we're here to assist you! This list briefly guides some of the most common AI terms, their definitions, and real-world applications. We plan on adding to this list as this technology advances so you're always up to date. Stay tuned!


  1. Algorithm: A set of rules for solving a problem. Example: A math algorithm that helps a computer program solve complex equations faster.
  2. Artificial Intelligence (AI): Simulating human intelligence in machines. Example: A chatbot that can answer student queries on a school website.
  3. Machine Learning (ML): Allowing computers to learn and improve from experience. Example: An educational app that adapts its teaching style based on student performance.
  4. Deep Learning: Advanced machine learning involving artificial neural networks. Example: A program that can grade student essays by understanding context and content.
  5. Neural Network: Computer systems modeled on the human brain. Example: A recommendation system for personalized learning paths based on student's learning history.
  6. Natural Language Processing (NLP): Computers understanding and responding to human language. Example: Voice-activated assistants in classrooms that can interpret and answer students' questions.
  7. Computer Vision: Computers interpreting and understanding visual information. Example: A system that monitors classroom attendance through facial recognition.
  8. Robotics: Design and use of robots. Example: Educational robots that help teach coding and STEM subjects in schools.
  9. Data Mining: Extracting useful information from large datasets. Example: Analyzing test results across schools to identify teaching effectiveness.
  10. Supervised Learning: Machine learning with labeled data. Example: An AI system trained with labeled math problems to help solve similar problems.
  11. Unsupervised Learning: Learning from unlabeled data. Example: Clustering students based on their learning habits without predefined categories.
  12. Reinforcement Learning: Learning by trial and error, using feedback. Example: An AI tutoring system that improves its teaching methods based on student engagement and quiz scores.
  13. Predictive Analytics: Using data to make predictions. Example: Forecasting student performance in exams based on their course activities.
  14. Chatbot: An AI-driven program that simulates human conversation. Example: A chatbot on a university website providing course information to prospective students.
  15. Semantic Analysis: Understanding the meaning and interpretation of words and sentences. Example: AI software grading essays by analyzing the semantics of student's responses.
  16. Image Recognition: AI's ability to identify and process images. Example: An educational app that teaches children about animals by recognizing animal pictures they upload.
  17. Speech Recognition: AI's ability to understand spoken language. Example: Language learning apps that help students improve their pronunciation.
  18. Text Mining: Extracting meaningful information from text. Example: Analyzing survey responses from students to improve teaching methods.
  19. Bias in AI: Prejudice in AI decision-making. Example: An AI recruitment tool that shows bias against candidates from certain backgrounds.
  20. Ethics in AI: Moral implications and responsibilities in AI development and use. Example: Discussing the ethical use of student data in educational AI applications.
  21. AI in Education: Application of AI technologies in educational contexts. Example: Adaptive learning platforms that customize content for each student.
  22. Cognitive Computing: AI systems that mimic human brain functioning. Example: Cognitive tutors that help students learn complex subjects by adapting to their cognitive processes.
  23. Big Data: Extremely large data sets analyzed computationally. Example: Using student performance data from multiple schools to derive insights about educational trends.
  24. AI Literacy: Understanding AI concepts and their implications. Example: Teaching modules for students and teachers about how AI works and its impact on society.
  25. Data Science: The science of analyzing and interpreting complex data. Example: Courses that teach educators how to use data science to improve teaching outcomes.
  26. Internet of Things (IoT): Network of interconnected devices. Example: Smart classroom technologies that adjust lighting and temperature based on occupancy.
  27. Augmented Reality (AR): Enhancing real-world environments with computer-generated elements. Example: AR apps that help in visualizing complex scientific concepts in classrooms.
  28. Virtual Reality (VR): Completely immersive computer-generated environments. Example: VR simulations for medical students to practice surgeries.
  29. Algorithmic Bias: Unintended prejudice in AI algorithms. Example: A student assessment tool that unfairly grades essays based on certain writing styles.
  30. Data Privacy: Protecting personal information. Example: Ensuring student data privacy in online learning platforms.
  31. Cloud Computing: Storing and accessing data and programs over the Internet. Example: Cloud-based learning management systems used by schools and universities.
  32. Adaptive Learning: Customizing educational content to individual learner's needs. Example: Software that adjusts math problems' difficulty based on student's proficiency.
  33. EdTech: Technology applied to enhance education. Example: Interactive whiteboards and digital textbooks used in modern classrooms.
  34. E-Learning: Learning conducted via electronic media, typically the Internet. Example: Online courses and degree programs offered by universities.
  35. Digital Divide: The gap between those who have access to modern information and communication technology, and those who don't. Example: Addressing internet access issues for remote learners.
  36. Large Language Model (LLM): A type of AI model designed to understand, generate, and interact using human language. Example: GPT-4, which can write essays, answer questions, and even generate programming code.
  37. Natural Language Generation (NLG): The process of producing coherent text from data. Example: An LLM creating summaries of historical events for educational content.
  38. Transformer Models: A type of model architecture used in many LLMs. Example: BERT, a transformer model that helps improve search engine results by understanding the context of search queries.
  39. Fine-Tuning: Adjusting an LLM on specific data to enhance performance in certain tasks. Example: Fine-tuning an LLM on educational texts to make it better at answering academic queries.
  40. Tokenization: Breaking down text into smaller units (tokens) for processing. Example: An LLM analyzing student essays by breaking the text into tokens to understand grammar and context.
  41. Language Model Training: The process of teaching an LLM using a large dataset. Example: Training an LLM with a diverse set of educational materials to make it adept at academic assistance.
  42. Zero-Shot Learning: An LLM's ability to perform tasks it wasn't explicitly trained on. Example: An LLM answering advanced chemistry questions accurately, despite not being specifically trained on chemistry texts.
  43. Few-Shot Learning: Teaching an LLM to perform a task with only a few examples. Example: Demonstrating to an LLM how to solve algebraic equations with a few examples, and it then being able to solve similar problems.
  44. Prompt Engineering: Crafting inputs to effectively guide LLMs in producing desired outputs. Example: Designing prompts that help an LLM generate lesson plans or study guides.
  45. Text Embeddings: Representations of text in numerical form that LLMs can understand and process. Example: Converting a paragraph from a history textbook into embeddings for thematic analysis.
  46. Autoregressive Models: LLMs that generate text one token at a time, based on previous tokens. Example: A model writing a story by predicting each next word based on the previous text.
  47. Attention Mechanisms: Part of LLMs that helps the model focus on relevant parts of the input when generating output. Example: An LLM focusing on key words in a question to generate a more accurate answer.
  48. Sequence-to-Sequence Models: LLMs that transform an input sequence into an output sequence. Example: Translating a French text to English for language learning purposes.
  49. Natural Language Understanding (NLU): The ability of LLMs to comprehend human language. Example: An LLM interpreting student feedback from course evaluations.
  50. OpenAI's GPT (Generative Pre-trained Transformer): A series of LLMs known for their language generation capabilities. Example: GPT-3 used to create interactive educational content for online platforms.
  51. Bias in LLMs: Inherent prejudices in language models due to training data. Example: An LLM showing gender bias in career-related suggestions due to biased training data.
  52. Interpretability in AI: Understanding how AI models arrive at their conclusions. Example: Analyzing how an LLM determines the reading level of a text.
  53. AI Ethics: The study of moral issues and standards in AI. Example: Debating the ethical implications of using LLMs to write student essays.
  54. Transfer Learning: Applying knowledge gained in one task to different but related tasks. Example: Using an LLM trained on adult literature to generate child-friendly stories.
  55. Data Annotation: The process of labeling data for AI training. Example: Tagging educational texts with specific learning objectives for better LLM training.
  56. Model Robustness: The ability of an LLM to handle diverse and challenging inputs. Example: An LLM consistently providing accurate scientific explanations regardless of the phrasing of the questions.
  57. Language Model Fine-Tuning: Customizing a pre-trained LLM for specific applications. Example: Fine-tuning an LLM for generating curriculum-aligned science quizzes.
  58. Conversational AI: AI models designed for human-like interactions. Example: An LLM-based chatbot that can converse with students on a wide range of academic topics.
  59. AI-Assisted Education: Integrating AI, like LLMs, into teaching and learning processes. Example: An LLM assisting in creating personalized learning experiences for students.
  60. Scalability in AI: The capability of AI models to handle increasing amounts of work. Example: An LLM scaling to provide real-time essay feedback to a large number of students simultaneously.