Automated Educational Question Generation at Different Bloom's Skill Levels using Large Language Models: Strategies and Evaluation

• URL: http://arxiv.org/abs/2408.04394v1
• Date: Thu, 8 Aug 2024 11:56:57 GMT
• Title: Automated Educational Question Generation at Different Bloom's Skill Levels using Large Language Models: Strategies and Evaluation
• Authors: Nicy Scaria, Suma Dharani Chenna, Deepak Subramani,
• Abstract summary: We examine the ability of five state-of-the-art large language models to generate diverse and high-quality questions of different cognitive levels. Our findings suggest that LLms can generate relevant and high-quality educational questions of different cognitive levels when prompted with adequate information.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Developing questions that are pedagogically sound, relevant, and promote learning is a challenging and time-consuming task for educators. Modern-day large language models (LLMs) generate high-quality content across multiple domains, potentially helping educators to develop high-quality questions. Automated educational question generation (AEQG) is important in scaling online education catering to a diverse student population. Past attempts at AEQG have shown limited abilities to generate questions at higher cognitive levels. In this study, we examine the ability of five state-of-the-art LLMs of different sizes to generate diverse and high-quality questions of different cognitive levels, as defined by Bloom's taxonomy. We use advanced prompting techniques with varying complexity for AEQG. We conducted expert and LLM-based evaluations to assess the linguistic and pedagogical relevance and quality of the questions. Our findings suggest that LLms can generate relevant and high-quality educational questions of different cognitive levels when prompted with adequate information, although there is a significant variance in the performance of the five LLms considered. We also show that automated evaluation is not on par with human evaluation.

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