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1 – 2 of 2Margarida Rodrigues, Rui Silva, Ana Pinto Borges, Mário Franco and Cidália Oliveira
This study aims to address a systematic literature review (SLR) using bibliometrics on the relationship between academic integrity and artificial intelligence (AI), to bridge the…
Abstract
Purpose
This study aims to address a systematic literature review (SLR) using bibliometrics on the relationship between academic integrity and artificial intelligence (AI), to bridge the scattering of literature on this topic, given the challenge and opportunity for the educational and academic community.
Design/methodology/approach
This review highlights the enormous social influence of COVID-19 by mapping the extensive yet distinct and fragmented literature in AI and academic integrity fields. Based on 163 publications from the Web of Science, this paper offers a framework summarising the balance between AI and academic integrity.
Findings
With the rapid advancement of technology, AI tools have exponentially developed that threaten to destroy students' academic integrity in higher education. Despite this significant interest, there is a dearth of academic literature on how AI can help in academic integrity. Therefore, this paper distinguishes two significant thematical patterns: academic integrity and negative predictors of academic integrity.
Practical implications
This study also presents several contributions by showing that tools associated with AI can act as detectors of students who plagiarise. That is, they can be useful in identifying students with fraudulent behaviour. Therefore, it will require a combined effort of public, private academic and educational institutions and the society with affordable policies.
Originality/value
This study proposes a new, innovative framework summarising the balance between AI and academic integrity.
Details
Keywords
Gopi Battineni, Nalini Chintalapudi and Francesco Amenta
As of July 30, 2020, more than 17 million novel coronavirus disease 2019 (COVID-19) cases were registered including 671,500 deaths. Yet, there is no immediate medicine or…
Abstract
Purpose
As of July 30, 2020, more than 17 million novel coronavirus disease 2019 (COVID-19) cases were registered including 671,500 deaths. Yet, there is no immediate medicine or vaccination for control this dangerous pandemic and researchers are trying to implement mathematical or time series epidemic models to predict the disease severity with national wide data.
Design/methodology/approach
In this study, the authors considered COVID-19 daily infection data four most COVID-19 affected nations (such as the USA, Brazil, India and Russia) to conduct 60-day forecasting of total infections. To do that, the authors adopted a machine learning (ML) model called Fb-Prophet and the results confirmed that the total number of confirmed cases in four countries till the end of July were collected and projections were made by employing Prophet logistic growth model.
Findings
Results highlighted that by late September, the estimated outbreak can reach 7.56, 4.65, 3.01 and 1.22 million cases in the USA, Brazil, India and Russia, respectively. The authors found some underestimation and overestimation of daily cases, and the linear model of actual vs predicted cases found a p-value (<2.2e-16) lower than the R2 value of 0.995.
Originality/value
In this paper, the authors adopted the Fb-Prophet ML model because it can predict the epidemic trend and derive an epidemic curve.
Details