Summary of Automated Educational Question Generation at Different Bloom’s Skill Levels Using Large Language Models: Strategies and Evaluation, by Nicy Scaria et al.
Automated Educational Question Generation at Different Bloom’s Skill Levels using Large Language Models: Strategies and Evaluation
by Nicy Scaria, Suma Dharani Chenna, Deepak Subramani
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the potential of large language models (LLMs) to generate high-quality educational questions, promoting learning and scalability in online education. Researchers examine the ability of five state-of-the-art LLMs to produce diverse and relevant questions across different cognitive levels (Bloom’s taxonomy). They employ advanced prompting techniques with varying complexity for automated educational question generation (AEQG). Expert and machine-based evaluations assess the linguistic and pedagogical quality of generated questions. Results indicate that LLMs can generate relevant and high-quality questions when prompted correctly, although performance variance exists among the five models considered. Additionally, they find that automated evaluation is not as effective as human evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) can help teachers create great educational questions. Researchers tested five different LLMs to see if they could generate good questions at various levels of thinking (remembering, understanding, applying, analyzing, evaluating). They used special prompts to encourage the LLMs to ask better questions. Experts and computers checked the quality of the generated questions. The study found that LLMs can make good educational questions when given the right information, but they don’t all perform equally well. It also shows that computers aren’t as good at evaluating questions as human experts are. |
Keywords
» Artificial intelligence » Prompting
