## 2 Methods and Procedures
### Data Analysis  
This study employs both quantitative and qualitative data analysis to comprehensively evaluate the effectiveness of AI-driven educational tools in enhancing academic performance and understanding student experiences. For the quantitative data, academic achievement is assessed through pre-tests, post-tests, and task or project scores. The pretest is conducted prior to the start of the course to measure the students' baseline knowledge and ensure equivalence between the groups. The posttest incorporates scores from mid-term and final examinations, serving as the central indicators of academic outcomes. Additionally, task and project results are considered supplementary performance measures. The assessment tools are designed to align with curriculum objectives and consist of validated test items to ensure reliability and content validity. For Group B, platform log data, such as AI-based textbook usage time, search frequency, and query response levels, are analyzed. These data are quantified to explore the correlation between learning behaviors and academic achievement, providing insights into the impact of the AI platform on student performance. For the qualitative data, surveys and focus group interviews are conducted to gain a deeper understanding of the student experience. Surveys measure general learning satisfaction, self-efficacy, and learning strategy usage using a 5-point Likert scale. Open-ended questions allow the students to elaborate on their experiences with the AI-based textbook, including challenges faced, usability concerns, and suggestions for improvement. Focus group interviews involve six to eight participants from both Group A and Group B, selected to provide diverse perspectives. The interviews focus on themes such as self-directed learning experiences, ease of information retrieval, and changes in learning motivation. The satisfaction survey is conducted separately via a Structured Satisfaction Questionnaire a more structured and systematic survey for qualitative evaluation. By integrating quantitative performance metrics with qualitative insights, this study offers a holistic understanding of the effectiveness of AI-based tools, their influence on academic outcomes, and their role in supporting personalized, self-directed learning. Statistical analysis was conducted using IBM SPSS Statistics 23 (IBM Corp., Armonk, NY, USA) for the quantitative data. An independent samples t-test was performed to compare the means between the two groups. A chi-squared test was conducted to assess the homogeneity of the categorical variables, such as gender distribution, between the AI Learning Group (ALG) and the Traditional Learning Group (TLG). The results confirmed that there were no significant differences between the groups, ensuring comparabil  
Figure 5: AI-powered report generation and adaptive learning interface example. ity and validating the randomization process. The qualitative data analysis was carried out using NVivo 10 (QSR International Pty Ltd., Melbourne, Australia), with a Matrix Coding Query employed to systematically analyze and categorize the data, guaranteeing a rigorous and structured approach to identifying patterns and relationships within the dataset. To establish the validity and reliability of the study, several strategies were implemented. Construct validity was secured through a review of the developed test items and survey questions by an expert panel consisting of faculty members and educational researchers, ensuring alignment with the study's objectives. Reliability was assessed by evaluating the internal consistency of the assessment tools, including tests and surveys, using [PERSON]'s alpha, which ensured the stability and consistency of the measurements. Internal validity was addressed by employing the random assignment of participants to experimental and control groups, while all lessons and assessments were conducted under identical conditions, such as the same environment and timeframe, to minimize confounding variables.