Evidence map›Paper›PMID 38371704›Full record

ArticleFrontiers in psychology2024

Grading by AI makes me feel fairer? How different evaluators affect college students' perception of fairness.

Fangyuan Chai, Jiajia Ma, Yi Wang, Jun Zhu, Tingting Han

Abstract read
In one paragraph

Article in Frontiers in psychology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Fangyuan ChaiGraduate School of Education, Beijing Foreign Studies University, Beijing, China.
Jiajia MaGraduate School of Education, Beijing Foreign Studies University, Beijing, China.
Yi WangGraduate School of Education, Beijing Foreign Studies University, Beijing, China.
Jun ZhuGraduate School of Education, Beijing Foreign Studies University, Beijing, China.
Tingting HanSchool of Marxism, Hubei University of Economics, Wuhan, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: In the field of education, new technologies have enhanced the objectivity and scientificity of educational evaluation. However, concerns have been raised about the fairness of evaluators, such as artificial intelligence (AI) algorithms. This study aimed to assess college students' perceptions of fairness in educational evaluation scenarios through three studies using experimental vignettes. Methods: Three studies were conducted involving 172 participants in Study 1, 149 in Study 2, and 145 in Study 3. Different evaluation contexts were used in each study to assess the influence of evaluators on students' perception of fairness. Information transparency and explanations for evaluation outcomes were also examined as potential moderators. Results: Study 1 found that different evaluators could significantly influence the perception of fairness under three evaluation contexts. Students perceived AI algorithms as fairer evaluators than teachers. Study 2 revealed that information transparency was a mediator, indicating that students perceived higher fairness with AI algorithms due to increased transparency compared with teachers. Study 3 revealed that the explanation of evaluation outcomes moderated the effect of evaluator on students' perception of fairness. Specifically, when provided with explanations for evaluation results, the effect of evaluator on students' perception of fairness was lessened. Discussion: This study emphasizes the importance of information transparency and comprehensive explanations in the evaluation process, which is more crucial than solely focusing on the type of evaluators. It also draws attention to potential risks like algorithmic hegemony and advocates for ethical considerations, including privacy regulations, in integrating new technologies into educational evaluation systems. Overall, this study provides valuable theoretical insights and practical guidance for conducting fairer educational evaluations in the era of new technologies.

Indexed as

AI algorithmexplanationfairness perceptionhigher education evaluationinformation transparency

Identifiers

PMID38371704
PMCPMC10869489

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.