PreprintCited by 2Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationIntelligent Tutoring Systems and Adaptive Learning

The Double-Edge Sword of AI: Mitigating the Risk and Maximizing the Benefits of Artificial Intelligence in Education (AIED)

Open access
Asuncion, Ivy, Natividad, Lexter·2025·SSRN Electronic Journal

Integrity signals

Registry

Crossref · 10.2139/ssrn.5085611

Cites

7 of 7 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?

Cites

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Cited by

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Authors
Asuncion, I., & Natividad, L.
Affiliations
Central Luzon State University (PH)
Corresponding author
Asuncion, Ivy
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2025
ISSN
1556-5068 (ISSN-L)
OpenAlex
W4408017434
Cited by
2
References
7
Citations by year
2026: 2
Type
posted content
Language
en
Open access
Locations
SSRN Electronic Journal (acceptedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords
artificial intelligence in education, benefits and risks of AI, personalized learning, student engagement, data privacy, algorithmic bias, critical thinking skills, TPACK, technology integration in teaching, stakeholder collaboration, educational 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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