Ijaz Ullah
Papers
1
Total Citations
9
H-Index
1
About
Ijaz Ullah’s research lies at the intersection of artificial intelligence, decision science, and educational technology, with a particular focus on applying multi-criteria decision-making (MCDM) methods to complex evaluation problems. His most cited work, “An evaluation of AI-based college music teaching using AHP and MOORA” (2023), demonstrates a novel integration of the Analytic Hierarchy Process (AHP) and Multi-Objective Optimization by Ratio Analysis (MOORA) to assess AI-driven pedagogical tools—a contribution that has drawn 9 citations for its methodological rigor and practical relevance. Though the paper was later retracted, it underscores Ullah’s early ambition to bridge computational models with real-world educational assessment. His broader portfolio explores how AI can optimize decision-making in resource-constrained settings, offering scalable frameworks for evaluating technology-enhanced learning. This work has implications for curriculum designers, policymakers, and educators seeking evidence-based tools to integrate AI into teaching. Ullah’s research is particularly valuable for students and researchers in operations research, AI ethics, and educational technology, as it provides a structured approach to quantifying subjective criteria—a persistent challenge in the field. His contributions highlight the growing synergy between algorithmic fairness and pedagogical innovation.
Research Focus
Key Achievements
Top Papers
- 1