Summary of How Teachers Can Use Large Language Models and Bloom’s Taxonomy to Create Educational Quizzes, by Sabina Elkins et al.
How Teachers Can Use Large Language Models and Bloom’s Taxonomy to Create Educational Quizzes
by Sabina Elkins, Ekaterina Kochmar, Jackie C.K. Cheung, Iulian Serban
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
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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 presents a natural language processing approach to question generation (QG) with potential benefits and use cases in education. A large language model-based QG system generates questions aligned with learning goals from Bloom’s taxonomy. The automatically generated questions are evaluated in experiments involving teachers, who prefer writing quizzes using these questions without sacrificing quality. Metrics show that the generated questions can even improve quiz quality, highlighting the promise of large-scale QG adoption in classrooms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create better questions for students to learn. It uses a special computer program to make lots of questions based on what kids should know. Teachers like using these questions because they’re just as good as ones they write themselves. This means we can use computers to help teachers create more questions, which will be super helpful in schools. |
Keywords
» Artificial intelligence » Large language model » Natural language processing
