A Hybrid Multi-Objective AI Framework for Curriculum-Aware Examination Generation

Automated examination generation has become increasingly important in modern education, where assessments must satisfy multiple pedagogical constraints, including cognitive balance, curriculum alignment, and question diversity. Existing approaches often address these requirements independently, limiting exam coherence and overall quality. To overcome this limitation, this study proposes a curriculum-aware hybrid framework that integrates Curriculum Knowledge Graphs (CKG), NSGA-II, and Proximal Policy Optimization (PPO). The problem is formulated as a constrained multi-objective optimization task that simultaneously maximizes Bloom’s taxonomy alignment, difficulty balance, and CLO coverage while minimizing semantic redundancy. The CKG captures relationships among questions, concepts, and CLOs to ensure structured curriculum alignment; NSGA-II generates Pareto-optimal exam candidates, and PPO further refines them through adaptive policy learning. The framework was evaluated on a dataset of 8,000 annotated questions using cross-validation, ablation studies, and statistical significance testing. Results demonstrate strong performance, achieving a Bloom Balance Score of 0.84 and CLO coverage of 87.5%, while reducing semantic redundancy from 10.7% to 3.2% (Δ = 7.5 percentage points; 70 % relative reduction, p < 0.001).

@article{Yehia2026,
  title     = {A Hybrid Multi-Objective AI Framework for Curriculum-Aware Examination Generation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Mohamed Fathy Yehia and Yehia M. Helmi and Mahmoud Mohamed Bahloul},
  doi       = {10.14569/IJACSA.2026.0170621},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170621}
}