Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
ChatGPT in the academia: exploring teachers' and students' practices and perspectives in higher education
0
Zitationen
3
Autoren
2025
Jahr
Abstract
Purpose This study investigates the practices and perspectives of students and teachers regarding the use of ChatGPT in an educational setting. Design/methodology/approach For this purpose, the study employed a cross-sectional survey design and a convenience sampling procedure to collect data from 380 students and 116 teachers in the sciences and social sciences fields. The study used the Partial Least Squares Structural Equation Modeling (PLS-SEM) procedure. Findings The study revealed that a significant number of students and teachers occasionally utilized ChatGPT in their academic work as needed. Regarding perceived ease of use (PEOU), the study found contradictory perspectives among students and teachers concerning attitude (AT). Specifically, students perceived that PEOU had a positive and significant effect on AT, whereas teachers perceived that PEOU had a positive but insignificant influence on AT. Moreover, the study, conducted under the Technology Acceptance Model framework, revealed that perceived usefulness, perceived credibility, perceived social presence and hedonic motivation are essential factors in boosting students' and teachers' attitudes and intentions to use ChatGPT for learning and teaching at higher education institutions. Practical implications The study recommends that universities implement Artificial Intelligence (AI) literacy training for students and teachers, update pedagogy and curricula to integrate generative AI and develop policies that guarantee the ethical use of AI tools to maintain academic integrity. Originality/value To the best of the authors’ knowledge, this study is novel and the first to empirically examine both the practices and perspectives of students and teachers regarding the use of ChatGPT in academia.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.260 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.116 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.493 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.438 Zit.