## 4 Discussion
Stakeholders, including educators, students, and educational institutions, require functionalities such as natural language understanding, question answering, and diagnostic assessments. Non-functional priorities such as responsiveness, accuracy, security, and scalability are equally essential. Additionally, environmental analyses ensure compatibility with operating systems, platforms, and network conditions, creating a robust foundation for system development and integration [18; 19]. The selection and evaluation of AI models play a pivotal role in optimizing a system's performance. Candidate models such as GPT-2, DistilBERT, and TinyBERT must be assessed through benchmarking against criteria such as accuracy, processing speed, and memory efficiency. This process enables the identification of models that are best suited to the educational objectives while ensuring seamless integration into existing infrastructures. Balancing model performance with resource efficiency is critical for achieving effective deployment in educational contexts [20]. Data preparation is another cornerstone of a robust management framework. High-quality datasets, comprising textbooks, instructional content, and question-answer pairs, are crucial for training AI model. Data cleaning processes remove inconsistencies and noise, while augmentation techniques enhance dataset diversity to ensure robustness across various educational contexts. The structured division of datasets into training, validation, and testing sets ensures systematic performance evaluation and minimizes bias. This meticulous approach to data preparation underpins the system's accuracy and relevance in delivering educational outcomes [21,22]. Diagnostic assessments are integral to tailoring learning experiences for individual students. By identifying foundational knowledge gaps and learning styles, adaptive assessments dynamically adjust question difficulty based on responses. Employing a mix of quizzes, projects, and subjective evaluations enriches the diagnostic process, offering a comprehensive understanding of each learner's academic needs. These assessments enable the creation of personalized learning paths, fostering engagement and supporting targeted interventions to address weaknesses [23]. This comprehensive management framework, encompassing requirements analysis, model selection, data preparation, and diagnostic assessment, ensures the qualitative enhancement of AI textbooks. By focusing on robust system integration and adaptive learning capabilities, this framework lays the foundation for delivering effective and personalized educational experiences, ultimately contributing to the sustained improvement of AI-powered digital learning environments [24]. The use of generative AI in education has garnered significant attention due to its ability to enable personalized, real-time, and interactive learning. However, several challenges have been identified. As highlighted in this study, issues such as the provision of inaccurate information, a lack of access to the necessary electronic devices, the misinterpretation of pre-learned external data, and the inconvenience of frequent device battery recharging have emerged as notable concerns. Excessive reliance on AI tools may also hinder students' critical thinking and problem-solving abilities. Furthermore, AI-based learning systems often require the use of personal data, raising serious issues regarding data privacy and security. Ensuring transparency in data usage and compliance with regulatory standards are essential to address these concerns. The digital divide presents another significant challenge, as not all students have access to the required devices or a stable internet connections, thereby exacerbating inequities in learning opportunities. Another limitation lies in the insufficient technical expertise among educators and the lack of training required to effectively integrate AI tools into teaching practices.