Merging the Strengths of GPT-4 and DeepLab for Semantic Text Understanding in Digital Textbooks

Gathering all the necessary data is the major objective of this study so that a comprehensive picture can be painted of how new technologies might improve students' understanding of semantic content in digital textbooks. The recommendations for this approach make use of TIFA, CEG, and ACA, or the Adaptive Complexity Adjustment. This approach combines the best features of various algorithms. TIFA makes these learning aids easy to completely comprehend by combining written and visual material. Answering students' queries and providing answers that make sense in the context is CEG's responsibility. To make learning more tailored to everyone, ACA changes the difficulty of the content depending on performance metrics. Conversely, when students ask CEG questions, it finds answers. We compare the proposed method, Semantix Blend, to two more popular approaches, Textbook Classic and Trad Edu. Interpreting text, evaluating visual information, ensuring consistency in context, enabling customisation, making content more accessible, enhancing learning results, and many more advantages are all possible with Semantix Blend. The study's findings will contribute to the improvement of digital textbooks, leading to improved instruction for students in the long term.