Understanding deep learning across academic domains: A structural equation modelling approach with a partial least squares approach

Abstract

This study investigates the impact of deep learning on various academic disciplines including arts and humanities, social sciences, engineering, health and management to explore its implications on academic achievement, research and societal relevance. This study shows that deep learning impacts social sciences, engineering, health, the arts, the humanities and management disciplines. Deep learning combines artificial intelligence and machine learning revolutionizing the teaching and learning experience. This research carefully explores the implications of deep learning on academic achievement, research and societal relevance, hence filling gaps in understanding deep learning in diverse academic domains. The quantitative research approach collects data from top-ranked global university students producing 971 valid responses from different academic disciplines. SEM and CFA were employed to validate the measurement model, thereby providing a robust statistical foundation for the study. This study illustrates that diverse academic domains have strong and positive relationships between deep learning, academic influence, research enhancement and social relevance. However, the observation of deep learning has a greater impact on the field of science and technology. The findings of this study emphasize ethical frameworks, model interpretability and responsible resource allocation in deep learning integration. This research guides teachers, policymakers and institutions to maximize the benefits of deep learning in diverse academic fields by emphasizing ethical considerations, interdisciplinary collaboration and long-term planning for responsible and effective integration.