PreprintCited by 2Intelligent Tutoring Systems and Adaptive LearningInnovative Teaching and Learning MethodsArtificial Intelligence in Education

Self-Regulated AI Use Hinders Long-Term Learning

Open access
Poulidis, Stefanos, Bastani, Hamsa, Bastani, Osbert·2025·SSRN Electronic Journal

Integrity signals

Registry

Crossref · 10.2139/ssrn.5604932

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?

  • GenAI can undermine learning when students rely on these tools to perform the cognitive work that instructional activities are intended to develop.

    “However, GenAI can also undermine learning when students rely on these tools to perform the cognitive work that instructional activities are intended to develop.”

Cites

Flagged first, then by citations

Authors
Poulidis, S., Bastani, H., & Bastani, O.
Affiliations
  • INSEAD (FR)
  • University of Pennsylvania (US)
Corresponding author
Poulidis, Stefanos
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2025
ISSN
1556-5068 (ISSN-L)
OpenAlex
W7108336357
Cited by
1
References
8
Citations by year
2025: 1
Type
posted content
Language
en
Open access
Locations
SSRN Electronic Journal (acceptedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Keywords
AI-assisted learning, long-term learning, productive struggle, zone of proximal development, student engagement, chess training, AI scaffolding, student motivation, AI reliance, skill acquisition

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