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Artificial Intelligence in the Classroom

Glossary of Terms

This glossary provides concise definitions of key terms related to artificial intelligence in education, offering educators insight into how AI, particularly generative AI, influences teaching and learning processes. It aims to support faculty members in understanding emerging technologies and their implications for educational practices.

  1. Adaptive Learning: Educational systems or platforms that use AI algorithms to adapt the learning experience to the individual needs and abilities of each student, providing personalized instruction and feedback.
    • Example: An adaptive learning platform that adjusts the difficulty level of math problems based on a student's performance and learning pace.
  2. Artificial Intelligence (AI): Refers to the simulation of human intelligence in machines that are programmed to think and learn like humans.
  3. AI Winter: A downturn in artificial intelligence (AI) research and development, marked by reduced funding and interest due to unmet expectations. These periods reflect skepticism in the AI field, leading to fewer investments and a slowdown in innovation. 
  4. Blockchain in Education: The use of blockchain technology, often combined with AI, to securely record and verify academic credentials, track learner progress, and facilitate transparent and decentralized educational systems.
  5. Data-driven Decision Making: The practice of using data analysis, often facilitated by AI, to inform educational decision-making processes, such as curriculum design, assessment strategies, and student interventions.
  6. Deep Learning: A type of machine learning that utilizes neural networks with many layers (hence "deep"), allowing the system to learn hierarchical representations of data.
  7. Educational Chatbots: AI-powered conversational agents designed to assist learners, provide support, answer questions, and facilitate interactions within educational environments.
    • Example: A chatbot integrated into an online learning platform that helps students find resources, answer questions about assignments, and provide study tips. 
  8. Educational Data Mining (EDM): The use of data mining techniques and methods to analyze large sets of educational data, such as student performance and behavior, to improve teaching and learning processes.
  9. Ethical AI in Education: The consideration of ethical implications and responsible use of AI technologies in educational contexts, including issues such as data privacy, algorithmic bias, and equitable access.
    • Example: Ensuring that AI-based educational tools are designed and implemented in a way that respects student privacy rights and promotes fairness and inclusivity.
  10. Generative AI: Generative AI uses advanced technology to autonomously create text, images, and videos by analyzing patterns in extensive training datasets. Despite producing content that mimics human creativity, generative AI lacks human consciousness and emotions.
    • Example: A generative AI model that can generate realistic-looking images of imaginary landscapes based on input descriptions.
    • Common Generative AI tools: ChatGPT (OpenAI), Google Bard, Adobe Firefly 
  11. Hallucination: Hallucinations refer to instances when large language models generate factually inaccurate or illogical answers, attributed to limitations in data and architecture.
    • Example: A large language model providing nonsensical responses when asked questions outside its trained domain, such as medical advice from a model trained on general knowledge.
  12. Intelligent Tutoring Systems (ITS): AI-based systems designed to provide personalized instruction or support to learners, often simulating the role of a human tutor.
  13. Large Language Model (LLM): Large language models, neural networks that predict word sequences, have significantly advanced in the past year, evolving as they are increasingly utilized. These models can engage in conversations, compose prose, and analyze vast amounts of text from the internet.
    • Example: A large language model generating human-like text for various applications, such as writing articles, generating poetry, or composing music.
  14. Machine Learning (ML): A subset of AI that enables systems to learn and improve from experience without being explicitly programmed, often through the use of algorithms.
  15. Natural Language Processing (NLP): The field of AI focused on enabling computers to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant.
    • Example: Natural language processing algorithms used in virtual assistants like Siri or Alexa to understand and respond to user voice commands.
  16. Neural Networks: Neural Networks, inspired by the human brain, learn skills by identifying statistical patterns in data through layers of artificial neurons. These computational models process information, with the first layer handling input data and the final layer producing results, sometimes puzzling even expert designers with intricate processes between layers.
  17. Virtual Reality (VR) in Education: The integration of virtual reality technology into educational settings to create immersive learning experiences, often enhanced by AI algorithms for personalized content delivery.
    • Example: Using virtual reality simulations to provide students with hands-on experiences in historical settings or scientific experiments, supplemented with AI-generated feedback and guidance.