## 3 Results
### Qualitative Evaluation  
The results of collecting students' experiences, difficulties, and improvements in relation to using AI textbooks through the use of more systematic open-ended questions for each detailed item are as follows. The qualitative analysis of the structured satisfaction survey, supported by the provided data, offers detailed insights into the students' experiences with the AI-based textbook. The survey, conducted on 86 participants, revealed high levels of satisfaction, as demonstrated by the average positive responses across various items. For instance, 62.5% of the respondents agreed that the course materials were clear and easy to understand, while 25% strongly agreed, indicating the overall effectiveness of the instructional design. When evaluating the usefulness of AI-based tools, 62.5% of the participants found that the tools were supportive of their learning, and 25% strongly agreed with this sentiment. Similarly, 62.5% of the students acknowledged the applicability of the AI knowledge and skills acquired during the course to academic and professional contexts, with 25% strongly endorsing this point. These results highlight the alignment between the course objectives and the students' perception of the value of the curriculum. However, the survey also revealed areas for improvement. Although most students agreed that the AI-based tools contributed to personalized learning and schedule management, 12.5% of the respondents rated their experience as average, and a small proportion expressed dissatisfaction in open-ended feedback. Specific concerns included the need for additional support in utilizing the tools effectively and skepticism regarding the comparative efficiency of AI resources versus traditional materials. The qualitative analysis conducted using NVivo 10 employed a Matrix Coding Query to systematically categorize and evaluate recurring themes in the participant feedback. The analysis identified 10 key themes, each reflecting distinct aspects of the AI-based textbook and its implementation in the educational context. These themes, along with their occurrence frequencies, provided detailed insights into both the strengths and challenges of the approach (Figure 6). Figure 6: The matrix coding query results. The most frequently mentioned theme was time efficiency, cited 15 times, highlighting the participants' appreciation for the AI system's ability to save time by enabling direct and quick access to specific content without the need for manual browsing through textbooks. Similarly, generational relevance emerged as a prominent theme (14 mentions), with students acknowledging that the digital-first approach aligns well with their familiarity and comfort with digital devices over traditional textbooks. Practical activities were another notable theme, with 12 mentions emphasizing the usefulness of the hands-on exercises incorporated into the course. However, several participants (10 mentions) expressed concerns about errors in responses, including instances where incorrect or inconsistent answers caused confusion. Relatedly, the misinterpretation of answers was mentioned 11 times, underscoring the challenges faced when the AI system interpreted similar terms incorrectly, leading to potential misunderstandings. The theme of basic concept clarity appeared 8 times, with feedback indicating that although the content was engaging, additional foundational explanations for beginners could enhance the learning experience. Device dependency (9 mentions) also surfaced as a barrier, since students without access to tablets or laptops found it difficult to use the platform effectively on mobile phones, compounded by issues such as rapid battery depletion. Participants also raised concerns about traditional methods versus AI-based approaches (7 mentions), questioning the efficacy of AI tools for exam preparation, particularly in fields such as national licensure exams that rely heavily on conventional study methods.