Impact of retrieval augmented generation and large language model complexity on undergraduate exams created and taken by AI agents

Impact of retrieval augmented generation and large language model complexity on undergraduate exams created and taken by AI agents

The capabilities of large language models (LLMs) have advanced to the point where entire textbooks can be queried using retrieval-augmented generation (RAG), enabling AI to integrate external, up-to-date information into its responses. This study evaluates the ability of two OpenAI models, GPT-3.5 T...

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Bibliographic Details
Main Authors: Erick Tyndall, Colleen Gayheart, Alexandre Some, Joseph Genz, Torrey Wagner, Brent Langhals
Format: Article
Language:English
Published: Cambridge University Press 2025-01-01
Series:Data & Policy
Subjects:
academic examinations
artificial intelligence
generative pre-training transformer
large language models
retrieval augmented generation
Online Access:https://www.cambridge.org/core/product/identifier/S2632324925100242/type/journal_article
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https://www.cambridge.org/core/product/identifier/S2632324925100242/type/journal_article

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