  This study explores the effectiveness of Large Language Models (LLMs) for
Automatic Question Generation in educational settings. Three LLMs are compared
in their ability to create questions from university slide text without
fine-tuning. Questions were obtained in a two-step pipeline: first, answer
phrases were extracted from slides using Llama 2-Chat 13B; then, the three
models generated questions for each answer. To analyze whether the questions
would be suitable in educational applications for students, a survey was
conducted with 46 students who evaluated a total of 246 questions across five
metrics: clarity, relevance, difficulty, slide relation, and question-answer
alignment. Results indicate that GPT-3.5 and Llama 2-Chat 13B outperform Flan
T5 XXL by a small margin, particularly in terms of clarity and question-answer
alignment. GPT-3.5 especially excels at tailoring questions to match the input
answers. The contribution of this research is the analysis of the capacity of
LLMs for Automatic Question Generation in education.
