## 2 Methods and Procedures
### Programs and Interventions
Instruction was led by a certified assistive technology specialist and full-time faculty member with over 10 years of experience, ensuring the quality and consistency of the content delivered. The instructional approach differed between the two groups. Students in the TLG studied sequentially using printed materials, progressing linearly through the content. Conversely, the ALG engaged in a dynamic and interactive learning process, actively searching for relevant information, reasoning through the material, engaging in discussions, and completing quizzes via the AI platform. To evaluate the intervention, a multi-dimensional assessment was conducted over the 15-week period. Quantitative methods included pre- and post-tests to measure knowledge acquisition, academic achievement assessments of task completion and project performance, and knowledge domain-specific evaluations to assess learning outcomes across different content areas. Additionally, qualitative data were collected through platform usage data (log data) in the ALG, which tracked learning frequency, keyword searches, and interaction patterns. Group discussions were also conducted separately for both groups to evaluate student satisfaction and identify areas for improvement in each instructional method. By integrating both quantitative and qualitative evaluations, this study comprehensively examined the effectiveness of AI-based self-directed learning (ALG) compared with traditional instructor-led methods (TLG). The findings provide insight into academic achievement, learning engagement, and overall student experiences across the two instructional approaches (Figure 2). This study was intentionally conducted within the context of a mandatory course to reflect a real-world educational setting where teaching methods would be naturally applied. The preselection of students was based solely on their academic year and enrollment in the program, without additional criteria such as prior academic performance or technological familiarity, thereby ensuring a diverse range of student profiles and mitigating selection bias. The random assignment of students into the ALG and TLG further ensured comparability between the groups and minimized potential bias. Importantly, the students were unaware of the broader research objectives, and the teaching methods were integrated as part of the standard curriculum, ensuring that the students' awareness did not influence their performance. To maintain consistency and eliminate potential biases among instructors, the entire course was delivered by a single instructor following a standardized practical manual developed as part of a national university innovation project. This manual provided detailed, step-by-step guidelines for delivering the course content, ensuring that both groups were exposed to identical content and procedures, differing only in the teaching methods (AI-based or traditional). The standardized manual minimized the influence of individual teaching styles, and it is expected that similar results would be obtained even if the course were delivered by a different instructor. Peer interactions and shared learning scenarios, which are natural in educational settings, were not discouraged since they reflect authentic classroom dynamics. However, the distinct teaching methods were robustly designed to withstand these interactions, and the evaluation focused on individual learning outcomes rather than group dynamics to ensure validity. Additionally, standardized assessments and consistent grading criteria guaranteed fairness in evaluation, leading to a reliable comparative analysis. This study employed rigorous scientific safeguards, including random assignment, homogeneity testing to confirm baseline comparability, and the use of mixed methods (quantitative and qualitative analysis) to comprehensively evaluate outcomes. These measures ensured the reliability and generalizability of the findings. By using a single instructor,  
Figure 2: Structured class flow: pre-test, learning phases, and post-test.