Cited by 116Artificial Intelligence in EducationEthics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.

Ethical artificial intelligence for teaching-learning in higher education

Airaj, Mohammed·2024·Education and Information Technologies

Overview

Integrity signals

Registry

Crossref · 10.1007/s10639-024-12545-x

Cites

49 of 49 references verified0 citation errors · 0 not checkable

Statistics

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Verification

Claims the paper makes

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Citations

Cites

Research works printed but not linked: 3

  1. Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 17, 42. https://doi.org/10.1186/s41239-020-00218-x. · DOI
  2. Blackler, F., Crump, N., & McDonald, S. (2000). Organising processes in complex activity networks. Organisation, 7(2), 277–300.
  3. Nicolini, D. (2012). Practice theory, work & organization. Oxford University Press.

Cited by

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Metadata

Authors
Airaj, M.
Affiliations
Sidi Mohamed Ben Abdellah University (MA)
Corresponding author
Airaj, Mohammed
Venue
Education and Information Technologies
Published
2024-02-21
Volume
29
Issue
13
Pages
17145-17167
ISSN
  • 1360-2357 (ISSN-L)
  • 1573-7608
OpenAlex
W4392016362
Cited by
115
References
48
Citations by year
2026: 55 · 2025: 50 · 2024: 10
FWCI
43.24
Type
journal article
Language
en
Open access
closed
Locations
Education and Information Technologies (publishedVersion)
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
ethical artificial intelligence, artificial intelligence in education, human-centered AI, AI teachers, data privacy, educational technology ethics, equitable access to knowledge, personalized learning, critical thinking

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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.

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