Automated Educational Question Generation at Different Bloom's Skill Levels using Large Language Models: Strategies and Evaluation
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929468792111104 |
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| author | Scaria, Nicy Chenna, Suma Dharani Subramani, Deepak |
| author_facet | Scaria, Nicy Chenna, Suma Dharani Subramani, Deepak |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04394 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Educational Question Generation at Different Bloom's Skill Levels using Large Language Models: Strategies and Evaluation Scaria, Nicy Chenna, Suma Dharani Subramani, Deepak Computation and Language Artificial Intelligence 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. |
| title | Automated Educational Question Generation at Different Bloom's Skill Levels using Large Language Models: Strategies and Evaluation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2408.04394 |