Enhancing Text Comprehension forLanguage Learning by GeneratingQuestions with Eye-Tracking and LLM
| Language | English |
|---|---|
| Type | Journal Articles |
| Authors | Kyoya Iwatsuru, Shoya Ishimaru, Andrew Vargo, Koichi Kise |
| Title | Enhancing Text Comprehension forLanguage Learning by GeneratingQuestions with Eye-Tracking and LLM |
| Journal | Activity and Behavior Computing |
| Location | Abu Dhabi |
| Reviewed | Reviewed |
| Presentation type | Oral |
| Date | April 2025 |
| Research fields | Reading-Life Log, Sensing |
| Key | Iwatsuru2025 (id=2025) |
Abstract
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.
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