Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 18

The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.

AI Ethics in the Fields of Education and Research: A Systematic Literature Review

Contreras, Mauricio Rojas, Jaimes, Jorge Omar Portilla·2024·2024 International Symposium on Accreditation of Engineering and Computing Education (ICACIT)
DOI 10.1109/icacit62963.2024.10788651
Open this paper in CoChat — free account

Abstract

The rapid emergence of artificial intelligence tools and their integration into various societal sectors have introduced significant ethical challenges. This research focuses on identifying the most frequently discussed ethical aspects of AI within the realms of education and research. A systematic literature review, supported by AI-driven tools, was conducted to address this objective. The study reveals that key ethical aspects recurrently examined include transparency and explainability, privacy and data protection, responsibility and accountability, and equity. Transparency and explainability are crucial for ensuring that AI systems in education operate in a comprehensible manner, fostering trust among educators and students. Privacy and data protection emphasize the importance of handling personal data with utmost care, adhering to relevant regulations. Responsibility and accountability highlight the need for clear mechanisms for overseeing AI deployment, while equity addresses the avoidance of bias and discrimination. The findings underscore the necessity of addressing a broad spectrum of ethical considerations throughout the lifecycle of AI systems to promote their responsible use in educational and research settings.

Overview

Integrity signals

Registry

Crossref · 10.1109/icacit62963.2024.10788651

Cites

12 of 12 references verified0 citation errors · 0 not checkable

Statistics

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

Verification

Claims the paper makes

This paper’s full text isn’t openly available, 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?

Citations

Cites

Research works printed but not linked: 13

  1. 2022, Rccomendación sobre la ética de la inteligencia artificial
  2. Reglamento (UE) 2024/1689 del Parlamento Europeo y del Consejo, de 12 de julio de 2024, sobre la inteligencia artificial
  3. 10.1016/j.patter.2022.100652 · DOI
  4. 10.3390/app12094130 · DOI
  5. 10.1016/j.futures.2024.103328 · DOI
  6. 10.1016/j.heliyon.2024.e26297 · DOI · Paper page
  7. 10.1016/j.jsis.2023.101772 · DOI
  8. 10.3389/feduc.2024.1379796 · DOI
  9. 10.47909/ijsmc.101 · DOI
  10. 10.6087/kcse.323 · DOI
  11. 10.3390/jpm14050443 · DOI
  12. Solórzano, 2022, La triangulación metodológica como herramienta para el análisis de las estrategias de comunicación en las webs universitarias latinoamericanas, Communication & Methods, 4
  13. 10.1016/j.caeai.2024.100208 · DOI

Cited by

See all 18

Metadata

Authors
Contreras, M. R., & Jaimes, J. O. P.
Affiliations
University of Pamplona,Research Group CICOM,Pamplona,Colombia
Venue
2024 International Symposium on Accreditation of Engineering and Computing Education (ICACIT)
Publisher
IEEE
Published
2024-10-03
Pages
1-6
DOI
10.1109/icacit62963.2024.10788651
Cited by
18
References
12
Licence

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

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.