Development and validation of a scale for dependence on artificial intelligence in university students
Background Artificial Intelligence (AI) has permeated various aspects of daily life, including education, specifically within higher education settings. These AI technologies have transformed pedagogy and learning, enabling a more personalized approach. However, ethical and practical concerns have a...
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| Formato: | Artículo |
| Lenguaje: | inglés |
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2024
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| author | Morales Garcia, Wilter Charming Sairitupa Sanchez, Liset Zenaida Morales-García, Sandra B. Morales García, Wilter Eyvi Mardel |
| author_browse | Morales Garcia, Wilter Charming Morales García, Wilter Eyvi Mardel Morales-García, Sandra B. Sairitupa Sanchez, Liset Zenaida |
| author_facet | Morales Garcia, Wilter Charming Sairitupa Sanchez, Liset Zenaida Morales-García, Sandra B. Morales García, Wilter Eyvi Mardel |
| author_sort | Morales Garcia, Wilter Charming |
| collection | Producción científica |
| description | Background Artificial Intelligence (AI) has permeated various aspects of daily life, including education, specifically within higher education settings. These AI technologies have transformed pedagogy and learning, enabling a more personalized approach. However, ethical and practical concerns have also emerged, including the potential decline in cognitive skills and student motivation due to excessive reliance on AI. Objective To develop and validate a Scale for Dependence on Artificial Intelligence (DIA). Methods An Exploratory Factor Analysis (EFA) was used to identify the underlying structure of the DIA scale, followed by a Confirmatory Factor Analysis (CFA) to assess and confirm this structure. In addition, the scale’s invariance based on participants’ gender was evaluated. Results A total of 528 university students aged between 18 and 37 years ( M = 20.31, SD = 3.8) participated. The EFA revealed a unifactorial structure for the scale, which was subsequently confirmed by the CFA. Invariance analyses showed that the scale is applicable and consistent for both men and women. Conclusion The DAI scale emerges as a robust and reliable tool for measuring university students’ dependence on AI. Its gender invariance makes it applicable in diverse population studies. In the age of digitalization, it is essential to understand the dynamics between humans and AI to navigate wisely and ensure a beneficial coexistence. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/469 |
| institution | UPeU |
| language | English |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/4692026-10-30T17:05:11ZDevelopment and validation of a scale for dependence on artificial intelligence in university studentsMorales Garcia, Wilter CharmingSairitupa Sanchez, Liset ZenaidaMorales-García, Sandra B.Morales García, Wilter Eyvi Mardelartificial intelligencedependencestudenttechnologyinteractionuniversityBackground Artificial Intelligence (AI) has permeated various aspects of daily life, including education, specifically within higher education settings. These AI technologies have transformed pedagogy and learning, enabling a more personalized approach. However, ethical and practical concerns have also emerged, including the potential decline in cognitive skills and student motivation due to excessive reliance on AI. Objective To develop and validate a Scale for Dependence on Artificial Intelligence (DIA). Methods An Exploratory Factor Analysis (EFA) was used to identify the underlying structure of the DIA scale, followed by a Confirmatory Factor Analysis (CFA) to assess and confirm this structure. In addition, the scale’s invariance based on participants’ gender was evaluated. Results A total of 528 university students aged between 18 and 37 years ( M = 20.31, SD = 3.8) participated. The EFA revealed a unifactorial structure for the scale, which was subsequently confirmed by the CFA. Invariance analyses showed that the scale is applicable and consistent for both men and women. Conclusion The DAI scale emerges as a robust and reliable tool for measuring university students’ dependence on AI. Its gender invariance makes it applicable in diverse population studies. In the age of digitalization, it is essential to understand the dynamics between humans and AI to navigate wisely and ensure a beneficial coexistence.Articlehttps://www.frontiersin.org/articles/10.3389/feduc.2024.1323898/pdf?isPublishedV2=Falsehttps://doi.org/10.3389/feduc.2024.132389810.3389/feduc.2024.1323898https://hdl.handle.net/20.500.15037/469W4392714531enFrontiers in Education2504-284X2024-03-122026-06-13T08:14:21Z2026-06-13T08:14:21Z |
| spellingShingle | artificial intelligence dependence student technology interaction university Morales Garcia, Wilter Charming Sairitupa Sanchez, Liset Zenaida Morales-García, Sandra B. Morales García, Wilter Eyvi Mardel Development and validation of a scale for dependence on artificial intelligence in university students |
| title | Development and validation of a scale for dependence on artificial intelligence in university students |
| title_full | Development and validation of a scale for dependence on artificial intelligence in university students |
| title_fullStr | Development and validation of a scale for dependence on artificial intelligence in university students |
| title_full_unstemmed | Development and validation of a scale for dependence on artificial intelligence in university students |
| title_short | Development and validation of a scale for dependence on artificial intelligence in university students |
| title_sort | development and validation of a scale for dependence on artificial intelligence in university students |
| topic | artificial intelligence dependence student technology interaction university |
| topic_facet | artificial intelligence dependence student technology interaction university |
| url | https://www.frontiersin.org/articles/10.3389/feduc.2024.1323898/pdf?isPublishedV2=False https://doi.org/10.3389/feduc.2024.1323898 https://hdl.handle.net/20.500.15037/469 |
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