Pith. sign in

REVIEW 5 cited by

The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2303.14334 v2 pith:BN4KDZI6 submitted 2023-03-25 cs.HC cs.AIcs.CL

classification cs.HCcs.AIcs.CL
keywords readingresearchinterfacesprojectscholarsaccessibilitychallengesgrows
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Scholarly publications are key to the transfer of knowledge from scholars to others. However, research papers are information-dense, and as the volume of the scientific literature grows, the need for new technology to support the reading process grows. In contrast to the process of finding papers, which has been transformed by Internet technology, the experience of reading research papers has changed little in decades. The PDF format for sharing research papers is widely used due to its portability, but it has significant downsides including: static content, poor accessibility for low-vision readers, and difficulty reading on mobile devices. This paper explores the question "Can recent advances in AI and HCI power intelligent, interactive, and accessible reading interfaces -- even for legacy PDFs?" We describe the Semantic Reader Project, a collaborative effort across multiple institutions to explore automatic creation of dynamic reading interfaces for research papers. Through this project, we've developed ten research prototype interfaces and conducted usability studies with more than 300 participants and real-world users showing improved reading experiences for scholars. We've also released a production reading interface for research papers that will incorporate the best features as they mature. We structure this paper around challenges scholars and the public face when reading research papers -- Discovery, Efficiency, Comprehension, Synthesis, and Accessibility -- and present an overview of our progress and remaining open challenges.

Discussion (0). Sign in to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. ReVoicer: Conversational Voice Annotation for Human-Centered, LLM-Assisted Peer Review

    cs.HC 2026-07 conditional novelty 6.0 of 10

    ReVoicer is a prototype that turns spoken, in-the-moment reactions to a paper into cleaned, tagged annotations and a draft review aligned with the reviewer's own style.

  2. When Constraints Limit and Inspire: Characterizing Presentation Authoring Practices for Evolving Narratives

    cs.HC 2026-04 unverdicted novelty 6.0 of 10

    Presenters treat constraints as active guides for building and reusing slide narratives across sessions, supported by the new CMPA framework and ReSlide tool that improves constraint-aware authoring compared to baselines.

  3. How College Students Use AI to Navigate Course Readings: Evidence from an Eight-Week Study

    cs.HC 2026-02 conditional novelty 6.0 of 10

    College students in an eight-week study used AI chatbots mostly for comprehension and summary, rarely went beyond the required minimum of three prompts, and treated AI output as the primary reading material.

  4. ReVoicer: Conversational Voice Annotation for Human-Centered, LLM-Assisted Peer Review

    cs.HC 2026-07 conditional novelty 5.0 of 10

    ReVoicer is a prototype annotation tool that cleans a reviewer's spoken/immediate reactions and drafts a rubric-aligned review using only the reviewer's own comments.

  5. LinkNav: Surfacing Interconnected Information in Scientific Articles

    cs.HC 2026-06 unverdicted novelty 5.0 of 10

    LinkNav creates intra-document connections in academic papers by generating questions from passages via LLM and retrieving answer passages from other parts of the document, with connected passages averaging ten segmen...

Pith tools