CARE: Collaborative AI-Assisted Reading Environment
This work addresses the problem of limited AI support for reading, particularly for researchers and developers, by providing an extensible platform, though it is incremental as it builds on existing NLP techniques.
The authors tackled the lack of AI-assisted reading tools by proposing inline commentary as a method and introducing CARE, an open platform for studying and enhancing reading with NLP assistance, which they evaluated in a user study on scholarly peer review.
Recent years have seen impressive progress in AI-assisted writing, yet the developments in AI-assisted reading are lacking. We propose inline commentary as a natural vehicle for AI-based reading assistance, and present CARE: the first open integrated platform for the study of inline commentary and reading. CARE facilitates data collection for inline commentaries in a commonplace collaborative reading environment, and provides a framework for enhancing reading with NLP-based assistance, such as text classification, generation or question answering. The extensible behavioral logging allows unique insights into the reading and commenting behavior, and flexible configuration makes the platform easy to deploy in new scenarios. To evaluate CARE in action, we apply the platform in a user study dedicated to scholarly peer review. CARE facilitates the data collection and study of inline commentary in NLP, extrinsic evaluation of NLP assistance, and application prototyping. We invite the community to explore and build upon the open source implementation of CARE.