Опубликована: Сен. 20, 2024
Язык: Английский
Journal of International Education in Business, Год журнала: 2025, Номер unknown
Опубликована: Март 3, 2025
Purpose This study explores the factors influencing artificial intelligence (AI)-driven decision-making proficiency (AIDP) among management students, focusing on foundational AI knowledge, data literacy, problem-solving, ethical considerations and collaboration skills. The research examines how these competencies enhance self-efficacy engagement, with curriculum design, industry exposure faculty support as moderating factors. aims to provide actionable insights for educational strategies that prepare students AI-driven business environments. Design/methodology/approach adopts a hybrid methodology, integrating partial least squares structural equation modeling (PLS-SEM) neural networks (ANNs), using quantitative collected from 526 across five Indian universities. PLS-SEM model validates linear relationships, while ANN captures nonlinear complexities, complemented by sensitivity analyses deeper insights. Findings results highlight pivotal roles of literacy problem-solving in fostering self-efficacy. Behavioral, cognitive, emotional social engagement significantly influence AIDP. Moderation analysis underscores importance design enhancing efficacy constructs. identifies most critical predictors AIDP, respectively. Research limitations/implications is limited central universities may require contextual adaptation global applications. Future could explore longitudinal impacts AIDP development diverse cultural settings. Practical implications findings designers, policymakers educators integrate into education. Emphasis experiential learning, frameworks interdisciplinary preparing AI-centric landscapes. Social By equipping future leaders proficiency, this contributes societal readiness technological disruptions, promoting sustainable contexts. Originality/value To author’s best uniquely integrates analyze interplay shaping It advances theoretical models linking learning theories practical education strategies, offering comprehensive framework developing students.
Язык: Английский
Процитировано
2
Education and Information Technologies, Год журнала: 2024, Номер unknown
Опубликована: Окт. 22, 2024
Язык: Английский
Процитировано
9
Education and Information Technologies, Год журнала: 2025, Номер unknown
Опубликована: Янв. 22, 2025
Язык: Английский
Процитировано
1
Acta Psychologica, Год журнала: 2025, Номер 253, С. 104708 - 104708
Опубликована: Янв. 14, 2025
Язык: Английский
Процитировано
0
Computers and Education Artificial Intelligence, Год журнала: 2025, Номер unknown, С. 100381 - 100381
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
0
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
IGI Global eBooks, Год журнала: 2025, Номер unknown, С. 311 - 332
Опубликована: Фев. 13, 2025
This chapter will explore how AI transforms the personalized learning experience by describing is implicated in tailoring of a student's experience. has emerged as an important approach toward improvement engagement, motivation, and success students within diversity present any educational setting. These technologies empower teachers to effectively analyze data set customized paths adaptive designs for curriculum. It also refers practical applications practically institutions well mentioning case studies from schools higher education institutes. In addition, author gives possibility which tools offer profiling students, constructing individualized plan interaction through active learning. The addresses recommendations educators on privacy algorithmic bias make ethical use.
Язык: Английский
Процитировано
0
Frontiers in Education, Год журнала: 2025, Номер 10
Опубликована: Март 5, 2025
Язык: Английский
Процитировано
0
Behavioral Sciences, Год журнала: 2025, Номер 15(3), С. 328 - 328
Опубликована: Март 7, 2025
Although the adoption and benefits of GenAI (Generative Artificial Intelligence) tools among higher education students have been widely explored in existing studies, less is known about how individual capabilities influence use these tools. Drawing on Information System Success Model (ISSM) Expectation–Confirmation (ECM), this study examines students’ capabilities, including critical thinking, self-directed learning ability, AI literacy, impact quality information obtained from Additionally, it explores relationships quality, student satisfaction, intention to continue using education. Survey data 1448 users Chinese universities reveal that with stronger tend extract higher-quality information, which turn fosters their satisfaction The findings highlight crucial role maximizing potential tools, emphasizes need cultivate literacy achieve sustainable success era. Theoretically, extends ISSM ECM by exploring mediating user between Practically, provides implications for educators policymakers enhance thus
Язык: Английский
Процитировано
0
Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 37 - 60
Опубликована: Март 14, 2025
This chapter explores the integration of cultural dimensions in AI-enhanced sustainability education, emphasizing need to tailor pedagogies for a diverse global learner community. As challenges transcend geographical boundaries, it is imperative develop educational approaches that are inclusive and culturally sensitive. The discusses potential Artificial Intelligence (AI) personalizing learning experiences addressing unique needs learners from various backgrounds. By examining case studies empirical evidence, we demonstrate how AI can be leveraged adapt education different contexts, fostering deeper understanding engagement among students. also highlights importance interdisciplinary collaboration role educators bridging gaps, ultimately contributing development more sustainable equitable society.
Язык: Английский
Процитировано
0