Artificial Intelligence & Chatbots: Guidelines for Academic Use

Submitting writing or other content generated by an artificial intelligence as your own work is a violation of Long Island University's Student Academic Conduct Policy. Citation guides across disciplines all recommend that scholars acknowledge ChatGPT and similar AI tools when reproducing its words and other content (e.g., images) within one's own work.

When using AI in your course assignments, it is important to:

• Confirm with your professor whether AI tools like ChatGPT are allowed for each assignment
• Verify information and sources generated by AI tools

Remember that AI can generate false or biased information, cite non-existent sources (or, "hallucinations"), and ignore principles of attribution. This raises ethical concerns when choosing it for your scholarly research.

What is AI?

AI or Artificial Intelligence is used to refer to systems and algorithms that can perform tasks that typically require human intelligence. AI is frequently associated with Machine Learning (ML), Large Language Models (LLMs), and Generative Pre-Trained Transformers (GPT).

To learn more about AI at Long Island University, see LIU's Statement on the Use of Artificial Intelligence.

Definitions

ARTIFICIAL INTELLIGENCE

• is the capacity of machines to mimic human cognitive functions such as learning, problem-solving, and pattern recognition, enabling them to perform tasks that normally require human intelligence. It includes various subfields, such as machine learning and natural language processing. 

GENERATIVE PRE-TRAINED TRANSFORMER

• is a type of large language model (LLM) neural network that can perform various natural language processing tasks such as answering questions, summarizing text and even generating lines of code.

LARGE LANGUAGE MODELS

• are AI models that can understand, generate, and interpret human-like text based on the input it receives.

MACHINE LEARNING

• is a subfield of AI where computer systems are given the ability to learn and improve from experience without being explicitly programmed. This is usually achieved by training the system with large amounts of data.

NEURAL NETWORKS

• are mathematical systems that act like a computerized brain, helping the technology find connections in data. They’re modeled after the human brain, with layers of artificial “neurons” that communicate information to one another.

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