I am a lecturer in Computer Science at Teesside University, holding a Ph.D. from Liverpool John Moores University. My academic background integrates computer science, business, and educational leadership, enabling a multidisciplinary approach to both teaching and research.

My research interests span machine learning and the application of artificial intelligence in general, with a particular focus on its use in education. I am especially interested in how AI can be leveraged to improve learning experiences, enhance student success, and support data-driven decision-making in higher education. My work explores areas such as learning analytics, predictive modelling, early-warning systems, and cognitive risk tracking. Building on my PhD research, I am currently developing a Wide-Spectrum Intelligent Student Early-Warning and Cognitive Risk Tracking Framework, utilising longitudinal datasets to improve the identification and support of at-risk students.

I have contributed to several international conferences and publications, including IEEE, ICERI, and CAIDA. In 2026, I was a speaker at the Future Facing Learning & AI in Higher Education Conference 2026, where I presented two papers, one of which was awarded first prize. I also served as a panel member and session chair.

In parallel with my research, I am committed to high-quality, student-centred teaching that promotes active learning and critical thinking. I hold Associate Fellowship (AFHEA) and continuously work to integrate innovative and evidence-based teaching practices into my courses.

Fluent in Arabic and English, I bring a global perspective shaped by diverse academic and professional experiences. My combined expertise in research, teaching, and academic leadership supports my ongoing contribution to advancing the role of artificial intelligence in education.