Designing and Evaluating AI Margin Notes in Document Reader Software
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911394864037888 |
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| author | Joshi, Nikhita Vogel, Daniel |
| author_facet | Joshi, Nikhita Vogel, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09840 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Designing and Evaluating AI Margin Notes in Document Reader Software Joshi, Nikhita Vogel, Daniel Human-Computer Interaction 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. |
| title | Designing and Evaluating AI Margin Notes in Document Reader Software |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.09840 |