التفاصيل البيبلوغرافية
| العنوان: |
Automated Educational Question Generation at Different Bloom's Skill Levels using Large Language Models: Strategies and Evaluation |
| المؤلفون: |
Scaria, Nicy, Chenna, Suma Dharani, Subramani, Deepak |
| المصدر: |
Artificial Intelligence in Education. AIED 2024 |
| سنة النشر: |
2024 |
| المجموعة: |
Computer Science |
| مصطلحات موضوعية: |
Computer Science - Computation and Language, Computer Science - Artificial Intelligence |
| الوصف: |
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. |
| نوع الوثيقة: |
Working Paper |
| DOI: |
10.1007/978-3-031-64299-9_12 |
| URL الوصول: |
http://arxiv.org/abs/2408.04394 |
| رقم الانضمام: |
edsarx.2408.04394 |
| قاعدة البيانات: |
arXiv |