AI is transforming education by providing new ways to support teaching and learning. Instructors can use AI to personalize learning experiences, streamline administrative tasks, and enhance student engagement. This page serves as a starting point for understanding how AI can be integrated into your teaching practice.
Teaching with Generative AI
Explore NYU's comprehensive FAQ page on teaching with generative AI tools. This resource provides valuable insights and answers to common questions about effectively integrating AI in your classroom, addressing topics like ethical use, academic integrity, and practical applications.
Access the FAQ page to get startedAI Literacy Basics
A few essential terms:
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A branch of computer science focused on creating systems that can perform tasks that would typically require human intelligence, like recognizing patterns, learning from experience, or understanding language. AI has been around for decades. It is utilized in autocorrect, Google Maps, search and recommendation algorithms, and banking, like e-payments, just to name a few.
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A subset of AI that enables systems to learn and improve from experience.
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AI that creates content, such as text or images, based on prompts (e.g., ChatGPT).
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A type of generative AI model trained on vast text data to understand and generate human-like language. LLMs, like GPT (Generative Pre-trained Transformer), power many modern AI text tools, like ChatGPT.
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The input or question given to a generative AI tool, like ChatGPT, to generate responses. The effectiveness of AI-generated results often depends on the clarity and specificity of the prompt. Check out this deep dive into Prompt Engineering.
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A chatbot is a program that is designed to communicate with people through text or voice commands in a way that mimics human-to-human conversation. When you are engaging with a chatbot, you are not talking to a human.
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The umbrella term for any machine’s ability to perform conversational tasks, such as recognizing what is said to it, understanding the intended meaning, and responding intelligibly.
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Tokens are the basic units of text or code that an LLM AI uses to process and generate language. Tokens can be characters, words, subwords, or other segments of text or code, depending on the chosen tokenization method or scheme. Tokens are assigned numerical values or identifiers, and are arranged in sequences or vectors, and are fed into or outputted from the model. Tokens are the building blocks of language for the model.
Guidelines for AI Use and Academic Integrity
Permitted and Prohibited Use Cases
Clearly outline where AI tools are allowed and restricted in your course. For example, AI might be used for brainstorming or initial drafts but should not be used to complete graded assignments independently. Establish boundaries to prevent academic misconduct, ensuring students understand when AI use crosses into prohibited territory, such as generating full essays or solutions. Note: You may include multiple policy statements in your syllabus to specify when AI tools are permitted or prohibited, depending on the assignment or activity.
Ethical AI Use
Encourage ethical AI usage, including proper attribution when AI tools contribute to an assignment. Highlight that AI is a supplement, not a substitute for original work.
Sample Policy Statements
- General Use: "Students may use Generative AI tools for brainstorming, but final submissions should reflect their own work."
- Prohibited Use: "Using Generative AI tools to generate full assignments is considered plagiarism."
- Attribution: "Any AI-generated content used must be acknowledged; failure to disclose may lead to academic consequences."
Engaging Students in Ethical AI Use
Consider starting the semester with discussions on AI’s role in learning, including ethical considerations, to build awareness and foster critical thinking about technology's role in academic work.