Cited by 117Teaching and Learning ProgrammingArtificial Intelligence in EducationProblem and Project Based Learning

Enhancing Computational Thinking Skills Through Artificial Intelligence Education at a STEAM High School

Huang, Xiaodong, Qiao, Chengche·2024·Science & Education

Integrity signals

Registry

Crossref · 10.1007/s11191-022-00392-6

Cites

26 of 26 references verified0 citation errors · 0 not checkable

Statistics

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

Claims the paper makes

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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?

  • Studies in ICT often involve teaching students about AI itself or using AI to support algorithmic thinking, coding instruction, or learning analytics.

    “Studies in this area often involve teaching students about AI itself or using AI to support algorithmic thinking, coding instruction, or learning analytics [56].”

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Authors
Huang, X., & Qiao, C.
Affiliations
  • Capital Normal University (CN)
  • Beijing Jiaotong University (CN)
Corresponding author
Qiao, Chengche
Venue
Science & Education
Published
2022-10-10
Volume
33
Issue
2
Pages
383-403
ISSN
  • 0926-7220 (ISSN-L)
  • 1573-1901
OpenAlex
W4304140101
Cited by
116
References
26
Citations by year
2026: 36 · 2025: 48 · 2024: 24 · 2023: 8
FWCI
15.44
Type
journal article
Language
en
Open access
closed
Locations
Science & Education (publishedVersion)
Funders
National New Liberal Arts Research and Reform Practice Project (2021180001)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Education › Problem and Project Based Learning
Keywords
artificial intelligence in education, computational thinking, STEAM education, high school students, interdisciplinary curriculum, student motivation, self-efficacy, AI curriculum design

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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