IntroAssist: A Tool to Support Writing Introductory Help Requests
Abstract
Writing introductory help requests is a key part of develop-ing new professional connections, such as through email and other online messaging systems. This paper presents the design and an experimental evaluation of IntroAssist-a web-based tool that leverages cognitive apprenticeship in-structional methods to support writing introductory help requests through an expert-informed checklist, tagged peer examples, self-tagging, and suggested word limit. In a study of IntroAssist with novice entrepreneurs, we find that 1) expert raters consider help requests written with the tool as more effective, 2) participants are able to perform introduc-tory help seeking skills after the tool is removed, and 3) participants report being more likely to send help requests written with the tool. We present implications for the de-velopment of systems that support the initiation of profes-sional relationships.
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AI reduces creative workloads by providing valuable suggestions and refining content for professional use, thereby helping writers overcome creative blocks.
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Lave, J., & Wenger, E. (1991) · Cambridge University Press eBooks · doi:10.1017/cbo9780511815355 · Cited by 33,219 · see citing works
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- Authors
- Hui, J. S., Gergle, D., & Gerber, E. M.
- Affiliations
- Northwestern University (US)
- Venue
- Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2018-04-19
- Pages
- 1-13
- OpenAlex
- W2796371790
- Cited by
- 76
- References
- 2
- Citations by year
- 2026: 17 · 2025: 7 · 2024: 13 · 2023: 11 · 2022: 10 · 2021: 9 · 2020: 3 · 2019: 5 · 2018: 1
- FWCI
- 3.53
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Expert finding and Q&A systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- cognitive apprenticeship, new entrepreneurs, email writing, help-seeking
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