Summary of The Future Of Learning in the Age Of Generative Ai: Automated Question Generation and Assessment with Large Language Models, by Subhankar Maity et al.
The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models
by Subhankar Maity, Aniket Deroy
First submitted to arxiv on: 12 Oct 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 This chapter explores the transformative potential of large language models (LLMs) in automated question generation and answer assessment for education. The authors examine the mechanisms behind LLMs, emphasizing their ability to comprehend and generate human-like text. They discuss methodologies for creating diverse, contextually relevant questions, enhancing learning through tailored, adaptive strategies. Key prompting techniques, such as zero-shot and chain-of-thought prompting, are evaluated for their effectiveness in generating high-quality questions, including open-ended and multiple-choice formats in various languages. The authors also explore advanced NLP methods like fine-tuning and prompt-tuning for generating task-specific questions despite associated costs. Additionally, they cover the human evaluation of generated questions, highlighting quality variations across different methods and areas for improvement. Furthermore, the chapter delves into automated answer assessment, demonstrating how LLMs can accurately evaluate responses, provide constructive feedback, and identify nuanced understanding or misconceptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This chapter shows how special computers called large language models (LLMs) can change education by making it easier to create questions and check answers. The authors explain how LLMs work and how they can be used to make better learning materials. They also talk about different ways to ask questions, like asking students to explain their answers or provide examples. The authors found that some ways of asking questions are better than others at getting good results. Finally, the chapter shows how LLMs can be used to check if student answers are correct and provide feedback. |
Keywords
» Artificial intelligence » Fine tuning » Nlp » Prompt » Prompting » Zero shot
