Artificial intelligence  and higher education

This systematic review explores the definitions and research surrounding Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI) within higher education literature. The study aims to provide a comprehensive synthesis of FATE’s evolution and the challenges and opportunities identified across the analyzed papers. The review encompasses 25 SCOPUS articles published between 2019 and 2023. FATE definitions were classified as either technical or descriptive, with some studies offering multiple definitions. Findings indicate a predominance of descriptive definitions, particularly for fairness, which are primarily quantitative. Ethics definitions are mostly qualitative, while fairness research tends to be quantitative. Future research could bridge the divide between experts and the public by integrating technical and descriptive definitions and combining qualitative and quantitative approaches.

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Institutions

Publication Details

Journal
American Journal of STEM Education
Published
2025-05-15
DOI
https://doi.org/10.32674/0x43s107
Citations
4
Primary Topic
AI and HR Technologies
Type
article
Field-Weighted Citation Impact
11.05
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article

Artificial intelligence  and higher education

Puja Trivedi, Subhasis Patra, A. Behera, Chandayya Makeni
4 citations
American Journal of STEM Education
AI and HR Technologies
11.05
article

Artificial intelligence  and higher education

Puja Trivedi, Subhasis Patra, A. Behera, Chandayya Makeni
article en
4 citations

Abstract

This systematic review explores the definitions and research surrounding Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI) within higher education literature. The study aims to provide a comprehensive synthesis of FATE’s evolution and the challenges and opportunities identified across the analyzed papers. The review encompasses 25 SCOPUS articles published between 2019 and 2023. FATE definitions were classified as either technical or descriptive, with some studies offering multiple definitions. Findings indicate a predominance of descriptive definitions, particularly for fairness, which are primarily quantitative. Ethics definitions are mostly qualitative, while fairness research tends to be quantitative. Future research could bridge the divide between experts and the public by integrating technical and descriptive definitions and combining qualitative and quantitative approaches.

American Journal of STEM EducationVol. 11
National Council Of Educational Research And Training (IN), Nagaland University (IN), Central University of Rajasthan (IN)
Openalex Percentile: Top 2%
AI and HR Technologies
11.05
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