Data Analytics using AI in higher education: A stakeholder’s analysis | ||
| المجلة العلمية للدراسات والبحوث المالية والتجارية | ||
| Volume 7, Issue 1, January 2026, Pages 587-622 PDF (1.56 M) | ||
| Document Type: المقالة الأصلية | ||
| DOI: 10.21608/cfdj.2025.428142.2387 | ||
| Authors | ||
| Mustafa Fuad* ; Rasha Abdelaziz; Meer Hamza | ||
| BIS, AASTMT, Alexandria, Egypt | ||
| Abstract | ||
| This study aims to assess the readiness of Egyptian higher education institutions to adopt data analytics through artificial intelligence, with a focus on resource availability. These institutions face unprecedented strategic and operational challenges due to global events such as the COVID-19 pandemic and regional conflicts, including declining enrollment and graduation rates, which threaten institutional sustainability. Data analytics and data mining techniques are promising tools for supporting decision-making and improving operational efficiency. Institutional readiness depends on leadership’s ability to embrace and support these technologies. The study employed a mixed-methods approach, incorporating the Delphi method to gather insights from decision-makers, developing surveys to measure Big Data Readiness Assessment (BDRA), and conducting a quantitative analysis using the DELTTA model, which includes six key components: Data, Enterprise, Leadership, Targets, Technology, and Data Scientists. The findings indicate that the readiness of Egyptian higher education institutions to adopt data analytics and artificial intelligence is influenced by multiple factors, including the type and size of the institution, available financial resources, and the active involvement and commitment of senior leadership. Furthermore, targeted professional development for staff and data scientists has enhanced institutional capacity to adopt these technologies. The study highlights the importance of artificial intelligence and data mining analytics in supporting decision-making, advancing scientific research, improving student enrollment and retention, and increasing graduation rates. These tools serve as a strategic asset for enhancing resource efficiency and ensuring institutional sustainability in a competitive and digital educational environment. | ||
| Keywords | ||
| Data Analysis; Artificial Intelligence; and Higher Education | ||
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