Robiul Hasan Arman2025-11-232025-11-232025-07-30https://dspace.nstu.ac.bd/handle/123456789/143Bloom's Taxonomy, a widely recognized framework for predicting cognitive skills, guides the design and evaluation of educational assessments. Traditional methods often struggle to maintain an effective balance between Lower-Order Thinking Skills (LOTS) and Higher-Order Thinking Skills (HOTS), while manually creating exam questions remains time-consuming and inefficient. To address these challenges, this study presents an automated system for predicting and generating exam questions aligned with Bloom's Taxonomy, ensuring comprehensive representation across all cognitive levels. The system leverages transformer-based models, including BERT, Distil BERT, RoBERTa, T5, BART, DistillBART, XLNet, and BERT2BERT, capitalizing on their deep contextual and semantic understanding capabilities. Furthermore, the research incorporates a hy brid prediction model that combines RoBERTa with Long Short-Term Memory (LSTM), Convo lutional Neural Networks (CNN), and Term Frequency-Inverse Document Frequency (TF-IDF) features to enhance question prediction accuracy and robustness significantly. The system was trained and evaluated on a diverse, manually annotated dataset spanning multiple academic disci plines, ensuring broad applicability. Experimental results demonstrate that the hybrid model achieved an impressive question prediction accuracy of 96.39% in predicting Bloom's cognitive levels, effectively handling both LOTS and HOTS complexities. Additionally, the T5-based ques tion generation model attained a high accuracy of 95.36% alongside a BLEU score of 0.8628, illustrating its ability to produce contextually relevant and pedagogically diverse questions. This research contributes to the advancement of educational technology by offering a scalable, efficient, and intelligent solution for automated assessment design. Keywords: Bloom's Taxonomy, Automated question generation, Transformer models, BERT, RoBERTa, T5, BART, BLEU score, Educational Assessments.