Penquiry is a pen-based system that uses Large Language Models to allow students to ask questions directly on digital study materials, reducing interaction friction.

In plain terms

Students often prefer using digital pens for active study, but leveraging AI tools like Large Language Models (LLMs) for learning can be cumbersome due to their reliance on typed input. This creates a gap between the natural, fluid interaction of a pen and the more rigid requirements of asking questions to an AI. The paper identifies two main challenges: a "Referential Barrier," where it's hard to clearly point to specific parts of a document with a text query, and an "Expressive Barrier," where non-textual things like diagrams are hard to convert into typed questions. To solve this, the researchers created "Penquiry," a system allowing learners to ask questions directly on their digital study materials using a pen. Penquiry uses "Content Snapping" to precisely link pen input to content and "Question Autocompletion" to expand sparse ink keywords into full, semantically rich questions for the LLM. Through user studies, they found that Penquiry significantly reduced the effort involved in asking questions compared to traditional typing, offering a more intuitive way to use AI for self-directed learning.