Introducing the INSPIRE Framework: Guidelines From Expert Librarians for Search and Selection in HCI Literature

Joseph Tu et al.

Interacting with Computers2025https://doi.org/10.1093/iwc/iwaf001article
AJG 2ABDC B
Weight
0.41

Abstract

Formalized literature reviews are crucial in human–computer interaction (HCI) because they synthesize research and identify unsolved problems. However, current practices lack transparency when reporting details of a literature search. This restricts replicability. This paper introduces the INSPIRE framework for HCI research. It focuses on the search stage in literature reviews to support a search that prioritizes transparency and quality-of-fit to a research question. It was developed based on guiding principles for successful searches and precautions advised by librarian experts in HCI (n=8) for search strategies in (primarily systematic) literature reviews. We discuss how their advice aligns with the HCI field and their concerns about computational AI tools assisting or automating these reviews. Based on their advice, the framework outlines pivotal stages in conducting a literature search. These essential stages are: (1) defining research goals, (2) navigating relevant databases and (3) using searching techniques (like divergent and convergent searching) to identify a set of relevant studies. The framework also emphasizes the importance of team involvement, transparent reporting, and a flexible, iterative approach to refining the search terms.

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https://doi.org/https://doi.org/10.1093/iwc/iwaf001

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@article{joseph2025,
  title        = {{Introducing the INSPIRE Framework: Guidelines From Expert Librarians for Search and Selection in HCI Literature}},
  author       = {Joseph Tu et al.},
  journal      = {Interacting with Computers},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1093/iwc/iwaf001},
}

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.