文献の詳細
| 論文の言語 | 英語 |
|---|---|
| 著者 | Kyoya Iwatsuru, Shoya Ishimaru, Andrew Vargo, Koichi Kise |
| 論文名 | Enhancing Text Comprehension by Generating Questions with Eye-Tracking and LLM |
| 論文誌名 | Activity and Behavior Computing |
| 発表場所 | Abu Dhabi |
| 査読の有無 | 有 |
| 発表の種類 | ポスター発表 |
| 年月 | 2025年4月 |
| 要約 | This paper presents a system that supports a reader by identifying difficult paragraphs using eye-tracking implicitly and generating compre- hension questions with a Large Language Model (LLM). Our aim is to evaluate the potential of generative AI as a tool to augment human cog- gnitive capabilities. If readers answer the questions correctly, this confirms that they understand the text. Conversely, if they answer incorrectly, it indicates a lack of understanding, allowing the system to provide explana- tions for the misunderstood parts, thereby improving the readers’ under- standing of the text. Our experiment showed that half of the participants improved their comprehension of the text. Further analysis revealed that the motivation to read/use system is one of the most important factor to increase the effectiveness of the proposed system. |
- BibTeX用エントリー
@Article{Iwatsuru2025, author = {Kyoya Iwatsuru and Shoya Ishimaru and Andrew Vargo and Koichi Kise}, title = {Enhancing Text Comprehension by Generating Questions with Eye-Tracking and LLM}, journal = {Activity and Behavior Computing}, year = 2025, month = apr, location = {Abu Dhabi} }