Automated Educational Question Generation at Different Bloom's Skill Levels using Large Language Models: Strategies and Evaluation
<|reference_start|>Automated Educational Question Generation at Different Bloom's Skill Levels using Large Language Models: Strategies and Evaluation: 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.<|reference_end|>
@article{scaria2024automated,
    title={Automated Educational Question Generation at Different Bloom's Skill
  Levels using Large Language Models: Strategies and Evaluation},
    author={Nicy Scaria, Suma Dharani Chenna, Deepak Subramani},
    journal={Artificial Intelligence in Education. AIED 2024},
    year={2024},
    doi={10.1007/978-3-031-64299-9_12},
    archivePrefix={arXiv},
    eprint={2408.04394},
    primaryClass={cs.CL cs.AI}
}