Cited by 53Ethics and Social Impacts of AIArtificial Intelligence in EducationExplainable Artificial Intelligence (XAI)

A Transparency Index Framework for AI in Education

Chaudhry, Muhammad Ali, Cukurova, Mutlu, Luckin, Rose·2022·Lecture Notes in Computer Science

Integrity signals

Registry

Crossref · 10.1007/978-3-031-11647-6_33

Cites

8 of 8 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.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Based on transparency index and three tiers, most reviewed study reached at-most Tier 1 (n = 109), merely considered transparent to AI researchers and practitioners.

    “Based on the transparency index and the three tiers of transparency (Chaudhry et al., 2022), most of the reviewed study reached at-most Tier 1 (n = 109), which is merely considered transparent to AI researchers and practitioners.”

Cites

Flagged first, then by citations

1 of the 9 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Chaudhry, M. A., Cukurova, M., & Luckin, R.
Affiliations
University College London (GB)
Corresponding author
Chaudhry, Muhammad Ali
Venue
Lecture Notes in Computer Science
Published
2022
Pages
195-198
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
OpenAlex
W4287964718
Cited by
53
References
9
Citations by year
2026: 15 · 2025: 18 · 2024: 14 · 2023: 5 · 2022: 1
FWCI
9.44
Type
conference paper
Language
en
Open access
closed
Locations
Lecture notes in computer science (publishedVersion)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Keywords
AI transparency, transparency index, artificial intelligence in education, AI ethics, co-design, stakeholder needs, educational technology, AI ethics framework

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 ›

Are you an author, or do you see a problem with this page? Request a review