Harnessing Generative AI for Enhanced Learning in Moroccan Education: A Case Study Investigation

This chapter investigates the integration of state-of-the-art natural language processing models, namely BERT (Bidirectional Encoder Representations from Transformers) and T5 (Text-To-Text Transfer Transformer), in the domain of science education within the Moroccan context. The primary objective is to evaluate the effectiveness of these models in generating educational content for personalized tutoring. A systematic approach is employed, encompassing data collection, model training, implementation, and evaluation phases. The study compares the performance of students who received educational content generated by BERT and T5 models with those who followed traditional methods. Evaluation metrics include exam success rates, comprehension scores, and student feedback. Results indicate that students exposed to content generated by BERT and T5 models exhibited higher exam success rates and comprehension scores compared to traditional methods. Moreover, positive feedback from students underscores the perceived effectiveness of AI-generated materials in aiding comprehension. However, limitations such as sample size and generalizability warrant further investigation. Overall, this research contributes to the growing body of research on AI integration in education and highlights the potential of BERT and T5 models to enhance personalized learning experiences in Moroccan science education.