Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
ChatGPT in Learning: Assessing Students’ Use Intentions through the Lens of Perceived Value and the Influence of AI Literacy
68
Zitationen
2
Autoren
2024
Jahr
Abstract
This study sought to understand students' intentions regarding the use of ChatGPT in learning from the perspective of perceived value, exploring the influence of artificial intelligent (AI) literacy. Drawing on a sample of 676 university students from diverse academic backgrounds, we employed a structured survey questionnaire to measure their perceptions of ChatGPT as a learning tool. The collected data were then analyzed using structural equation modeling (SEM) via SmartPLS 4 software. The findings showed a strong effect of the students' perceived value of ChatGPT on their intention to use it. Our findings suggest that perceived usefulness, perceived enjoyment and perceived fees had a significant influence on students' perceived value of ChatGPT, while perceived risk showed no effect. Moreover, the role of AI literacy emerged as pivotal in shaping these perceptions. Students with higher AI literacy demonstrated an enhanced ability to discern the value of ChatGPT. AI literacy proved to be a strong predictor of students' perception of usefulness, enjoyment, and fees for using ChatGPT in learning. However, AI literacy did not have an impact on students' perceptions of using ChatGPT in learning. This study underscores the growing importance of integrating AI literacy into educational curricula to optimize the reception and utilization of innovative AI tools in academic scenarios. Future interventions aiming to boost the adoption of such tools should consider incorporating AI literacy components to maximize perceived value and, subsequently, use intention.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.214 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.071 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.429 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 · 5.418 Zit.