Unlocking the power of AI in education
Students’ intentions and AI tool use driving learning success in an emerging economy
Datos Bibliográficos
| ID | 12427770 |
|---|---|
| Autores | Priya Saha (0009-0004-1205-4097, University of Barishal, autor de correspondencia), Shakhawat Hossain (0000-0001-6627-4635, University of Barishal), Nirmal Chandra Roy (0000-0001-8157-4898, University of Burdwan), Abdullah Al Masud (0000-0001-9345-4703), Ruhul Amin (0000-0001-5329-8152) |
| Año | 2025 |
| Volumen | 33 |
| Número | 1 |
| Páginas | 126-144 |
| Fecha de publicación | 2025-01-24 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | On the Horizon The International Journal of Learning Futures (JOURNAL) |
| Identificadores de la revista | ISSN: 1074-8121 • E-ISSN: 2054-1708 |
| Editorial | Emerald Publishing Limited (PUBLISHER • GB) |
| DOI | 10.1108/oth-10-2024-0066 |
| OpenAlex | W4406816441 |
| Idioma | EN |
| Citas recibidas | 1 |
| Referencias citadas | 44 |
Purpose This study aims to evaluate students’ intention and actual use (AU) of artificial intelligence (AI) tools’ to discover how the power of AI influences learning and academic success. Design/methodology/approach This paper used the unified theory of acceptance and use of technology (UTAUT) to develop a structural equation model (SEM) and used convenience sampling to measure 304 students’ five-point Likert scale responses. The model was tested with AMOS-24 and SPSS-25, and the study found that AI boosted students’ learning experiences and explain importance of AI skills and knowledge. Findings Performance expectancy (PE), effort expectancy (EE), social influence and facilitating condition directly and indirectly affect AU via intent to use (IU), while subjective norms determining the use of AI tools’ and have no substantial influence. Attitude (ATT) moderates PE and EE, although the data show that ATT has no substantial effect on EE. Originality/value These insights may help student to understand how AI tools’ benefit them and what factors affect their utilization. When correctly designed and executed, UTAUT provides an appropriate integrated theoretical framework for robust statistical analysis like SEM
China · Data science · Driving factors · Economics · Economy · Knowledge management · Mathematics education · Political science · Power (physics · AI in Service Interactions · Computer Science · Online Learning and Analytics · Psychology · Technology Adoption and User Behaviour
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| Obras citantes distintas | 1 |
|---|---|
| Citas por año | 1 |
| Intervalo de citas | 2026 - 2026 (1) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 1 |