Cited by 1Artificial Intelligence in EducationEducational Innovations and TechnologyOnline and Blended Learning

Bridging the Digital Divide: Socioeconomic Equity in AI-Assisted Hybrid Learning

Poh, Racheal, Tan, Bee Wah, Lee, Heng Wei, Tan, Kian Lam·2025·SN Computer Science

Integrity signals

Registry

Crossref · 10.1007/s42979-025-04593-y

Cites

24 of 24 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Cites

Flagged first, then by citations

Research works printed but not linked: 3

  1. Chin WW. The partial least squares approach to structural equation modeling. In: Marcoulides GA, editor. Modern methods for business research. Hillsdale: Lawrence Erlbaum Associates; 1998. p. 295–336.
  2. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale: Lawrence Erlbaum Associates; 1988.
  3. Sen A. Development as freedom. Oxford: Oxford University Press; 1999.

Cited by

Still being checked.

Authors
Poh, R., Tan, B. W., Lee, H. W., & Tan, K. L.
Affiliations
  • Wawasan Open University (MY)
  • Universiti Sains Malaysia (MY)
Corresponding author
Poh, Racheal
Venue
SN Computer Science
Published
2025-12-26
Volume
7
Issue
1
ISSN
2661-8907 (ISSN-L)
OpenAlex
W7117319193
Cited by
1
References
24
Citations by year
2026: 1
FWCI
1.05
Type
journal article
Language
en
Open access
closed
Locations
SN Computer Science (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Information Systems › Educational Innovations and Technology
  • Social Sciences › Education › Online and Blended Learning
Keywords
digital divide, socioeconomic status, generative AI in education, self-directed learning, hybrid learning, open and distance learning, self-management, self-monitoring, student motivation, educational inequality, AI literacy, access to 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.

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