Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
The Role of Artificial Intelligence in Developing Scientific Research Skills from the Perspective of Faculty Members: Laghouat University as a Case Study
0
Zitationen
3
Autoren
2025
Jahr
Abstract
This study aimed to explore the role of artificial intelligence (AI) in developing scientific research skills among Faculty Members at Laghouat University. The social Survey method was employed, and a questionnaire was used as the primary tool for data collection. The study sampleconsisted of 124 Faculty Members. The study yielded several key findings, most notably: awidespread use of AI Tools in the academicenvironment, with the majority of participants reportingreliance on academic research platforms such as Google Scholar and Semantic Scholar, as well as reference management tools like End Note and Zotero. A significant portion of the samplealsoreported using generative AI Tools—most notably Chatgpt—to support researchthinking and to answer scientific questions. The resultsfurtherindicated the effectiveness of AI technologies such as naturallanguageprocessing and machine Learning in accelerating data collection and analysisprocesses. However, some participants identified technical obstacles, including a lack of training and difficulties in understanding how to applythesetools. Concernswerealsoraised about data privacy and the potential impact of AI on researchoutcomes. The findingsshowed no statisticallysignificantdifferencesbetween male and female participants in their use of these technologies or in their academic achievement, indicating a degree of genderparity in This Domain.
Ähnliche Arbeiten
Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study
2020 · 22.609 Zit.
La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19
2020 · 19.271 Zit.
A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
2024 · 14.254 Zit.
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
2018 · 8.503 Zit.
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
2021 · 7.117 Zit.