Artificial intelligence-driven prediction of optimal technology-aided alternative operations in post-emergency contexts: A case study from an Emirati university

Abstract

Despite the challenges posed by the recent pandemic, educational institutions were prompted to explore alternative operation modes to enhance teaching, learning, and service delivery through technology. However, effective implementation of these technology-aided modes in post-emergency contexts necessitates evidence-based practices and contextual insights into stakeholders' challenges, comfortability, and preferences. This study aims to support the efficient planning and execution of digital transformation within a university in the United Arab Emirates (UAE) by examining stakeholders’ comfortability, challenges, and preferences following the pandemic's impact. The novelty of this research lies in its use of artificial intelligence, specifically fuzzy logic, to predict stakeholder preferences, complemented by comprehensive stakeholder-centric analysis and an in-depth examination of demographic influences on digital transformation preferences. Additionally, the study provides unique regional insights within the UAE context, addressing cultural, economic, and technological factors underrepresented in international literature. Utilizing a survey method, data were analyzed through descriptive statistics and AI-driven predictive analytics. Findings indicate that institutional support and familiarity with online platforms reduced stress during the transition to technology-aided modes, with a strong preference for hybrid flexible models influenced significantly by demographic factors. This study contributes by demonstrating the enhanced predictive capabilities of AI in understanding stakeholder needs, offering tailored digital transformation strategies, highlighting the importance of demographic considerations, and providing a practical roadmap for building a sustainable and resilient digital ecosystem. Furthermore, it informs educational policy and governance, ensuring that technology-aided operations are effectively planned and implemented to meet the evolving needs of the academic community in post-emergency settings.