Undergraduate Pacific Studies Exam Generation and Answering Using Retrieval Augmented Generation and Large Language Models

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Presented at HICSS 2025 by

Erick Tyndall

Colleen Gayheart

Alexandre Some

Joseph Genz

Brent Langhals

Torrey Wagner

The capabilities of large language models have increased to the point where entire textbooks can be queried using retrieval-augmented generation (RAG). The study evaluates the ability of OpenAI’s ChatGPT-3.5-Turbo and ChatGPT-4-Turbo models to create and answer exam questions based on an undergraduate textbook. 14 exams were created with true-false, multiple-choice, and short-answer questions from a textbook available online. The accuracy of the models in answering these questions is assessed both with and without access to the source material. Performance was evaluated using text-similarity metrics including ROUGE-1, cosine similarity, and word embeddings. 56 exam scores were analyzed to find that RAG-assisted models outperformed those without access to the textbook, and that ChatGPT-4-Turbo was more accurate than ChatGPT-3.5-Turbo on nearly all exams. The findings demonstrate the potential of generative artificial intelligence tools in academic assessments and provide insights into comparative performance of these models.