A self-improving quadratic tutor
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
The development of AIEd systems has often been motivated by their potential to promote educational equity and reduce achievement gaps across different groups of learners.
“The development of AIEd systems has often been motivated by their potential to promote educational equity and reduce achievement gaps across different groups of learners”
From Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education? (2021) · cites this paper as O'Shea, 1979
The citation lists haven’t been fetched yet
References 20 · Cited by 87
- Authors
- O'Shea, T.
- Venue
- International Journal of Man-Machine Studies
- Publisher
- Elsevier BV
- Published
- 1979-01
- Volume
- 11
- Issue
- 1
- Pages
- 97-124
- ISSN
- 0020-7373 (ISSN-L)
- OpenAlex
- W2068742478
- Cited by
- 87
- References
- 20
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 2 · 2022: 2 · 2021: 1 · 2020: 2 · 2019: 1 · 2018: 2 · 2017: 1 · 2016: 3
- FWCI
- 3.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Man-Machine Studies (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Edcuational Technology Systems
- Keywords
- adaptive teaching strategies, production rules, quadratic equations, discovery learning, theory of instruction, educational objectives, frame problem
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.
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