Designing and Evaluating AI Margin Notes in Document Reader Software

AI capabilities for document reader software are usually presented in separate chat interfaces. We explore integrating AI into document comments, a concept we formalize as AI margin notes. Three design parameters characterize this approach: margin notes are integrated with the text while chat interfaces are not; selecting text for a margin note can be automated through AI or manual; and the generation of a margin note can involve AI to various degrees. Two experiments investigate integration and selection automation, with results showing participants prefer integrated AI margin notes and manual selection. A third experiment explores human and AI involvement through six alternative techniques. Techniques with less AI involvement resulted in more psychological ownership, but faster and less effortful designs were generally preferred. Surprisingly, the degree of AI involvement had no measurable effect on reading comprehension. Our work shows that AI margin notes are desirable and contributes implications for their design.

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Published
2026-04-13
DOI
https://doi.org/10.1145/3772318.3790786
Citations
1
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
0.00

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article

Designing and Evaluating AI Margin Notes in Document Reader Software

Nikhita Joshi, Daniel Vogel
1 citations
Topic Modeling
article

Designing and Evaluating AI Margin Notes in Document Reader Software

Nikhita Joshi, Daniel Vogel
article en
1 citations

Abstract

AI capabilities for document reader software are usually presented in separate chat interfaces. We explore integrating AI into document comments, a concept we formalize as AI margin notes. Three design parameters characterize this approach: margin notes are integrated with the text while chat interfaces are not; selecting text for a margin note can be automated through AI or manual; and the generation of a margin note can involve AI to various degrees. Two experiments investigate integration and selection automation, with results showing participants prefer integrated AI margin notes and manual selection. A third experiment explores human and AI involvement through six alternative techniques. Techniques with less AI involvement resulted in more psychological ownership, but faster and less effortful designs were generally preferred. Surprisingly, the degree of AI involvement had no measurable effect on reading comprehension. Our work shows that AI margin notes are desirable and contributes implications for their design.

Centre National de la Recherche Scientifique (FR), University of Waterloo (CA), Université Paris-Saclay (FR)
Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 99%
Topic Modeling
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