Understanding age and technology experience differences in use of prior knowledge for everyday technology interactions
Abstract
Technology designers must understand relevant prior knowledge in a target user population to facilitate adoption and effective use. To assess prior knowledge used in naturalistic settings, we systematically collected information about technologies used over 10-day periods from older adults with high and low technology experience and younger adults. Technology repertoires for younger adults and high technology older adults were similar; differences reflected typically different needs for kitchen and health care technologies between the age groups. Technology repertoires for low-technology older adults showed substantial technology usage in many categories. Lower usage compared to high-tech older adults for each category was limited primarily to PC and Internet technologies. Experience differences suggest preferences among low-technology older adults for basic technology usage and for working with people rather than technologies. Participants in all groups were generally successful using their everyday technologies to achieve their goals. Prior knowledge was the most common attribution for success, but external information was also commonly referenced. Relevant prior knowledge included technical, functional, strategy, and self knowledge. High tech older adults did not report more problems than younger adults, but they did attribute more problems to insufficient prior knowledge. Younger adults attributed more problems to interference from prior knowledge. Low-tech older adults reported fewer problems, typically attributing them to insufficient prior knowledge or product/system faults. We discuss implications for further research and design improvements to increase everyday technology success and adoption for high-tech and low-tech older adults.
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Previous experience with technology may be a differentiating factor in individual interactions with computer technologies.
“analysis of individual interactions with computer technologies shows that previous experience with technology may be a differentiating factor (O'Brien et al., 2012).”
From Using Generative AI to Enhance Experiential Learning: An Exploratory Study of ChatGPT Use by University Students (2025) · cites this paper as O'Brien et al., 2012
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- Authors
- O'brien, M. A., Rogers, W. A., & Fisk, A. D.
- Affiliations
- University of Alabama in Huntsville, Huntsville, AL
- Georgia Institute of Technology (US)
- ORCID
- Rogers, Wendy A.
- Venue
- ACM Transactions on Accessible Computing
- Published
- 2012-03
- Volume
- 4
- Issue
- 2
- Pages
- 1-27
- ISSN
- 1936-7228 (ISSN-L)
- 1936-7236
- OpenAlex
- W1975167942
- Cited by
- 61
- References
- 56
- Citations by year
- 2026: 7 · 2025: 2 · 2024: 1 · 2022: 8 · 2021: 6 · 2020: 6 · 2019: 4 · 2018: 4 · 2017: 3 · 2016: 5 · 2015: 5 · 2014: 4 · 2013: 5 · 2012: 1
- FWCI
- 12.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Accessible Computing (publishedVersion)
- Funders
- National Institute on Aging (R01 AG15019, P01 AG017211)
- Topics
- Social Sciences › Demography › Technology Use by Older Adults
- Social Sciences › Psychology › Neuropsychology and Physiological Psychology › Aging and Gerontology Research
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- prior knowledge, older adults, technology experience, everyday technology use, age differences, technology adoption, self-knowledge, technical knowledge, kitchen technology, health technology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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