Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
AI in higher education: Faculty perspective towards artificial intelligence through UTAUT approach
11
Zitationen
1
Autoren
2024
Jahr
Abstract
Applications of AI in higher education have been around for several years, and numerous studies have looked at the pedagogical potential that these applications can offer to support the learning processes. However, there are still concerns and misunderstandings about acceptance in higher education, particularly among faculty members, despite the growing number of studies and their opportunities for supporting the educational and learning process. This paper aims to investigate the Behavioral Intention (BI) of Higher Education Institution faculty (HEI-faculty) towards adopting AI from a pedagogical perspective. The hypotheses in this paper were tested using a technology acceptance model with four major constructs: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC), as well as the effects of four mediating variables: age, gender, experience, and voluntariness. The result has shown that the Behavioral Intention (BI) of adopting AI among HEI faculty has a strong positive significant correlation with PE, EE, SI, and FC. Interestingly, the social influence of adopting AI from colleagues has a strong influence on the use of AI for education. Thus, one of the proposed hypotheses was disproven. Furthermore, the result of this paper also suggests considerations for the future development of AI applications for HE.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.418 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.288 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.726 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.516 Zit.