## 5 Discussion  
### Learning Effects  
The findings confirm that, although both methods promote improvements in student performance, those who followed the video-based instruction route achieved significantly greater progress. Several factors can explain this phenomenon. From the cognitive load theory perspective [46; 85], learning complex content (such as web programming logic) benefits from resources that optimize the distribution of mental load. In video-based instruction, students can process information visually and audibly simultaneously, facilitating the formation of integrated mental models [44; 47]. This advantage is amplified by the fact that the instructional videos used in this study were specifically  
Figure 8: Post-perception scores of HMSAM constructs per condition. Figure 9: Pre-post-perception differences of HMSAM constructs per condition. designed to teach PHP programming, with structured explanations and examples tailored to address common challenges in understanding complex concepts and algorithms. In contrast, while Microsoft Copilot provides immediate and adaptive feedback [86; 87], it is a general-purpose GenAI tool not explicitly optimized for pedagogical purposes, requiring students to formulate questions and validate responses independently. Additionally, the ability to pause and rewind allows learners to control the pace of learning and focus on the most complex elements [28; 48], further enhancing the effectiveness of these purposefully crafted audiovisual resources. This adaptive control over the learning pace may help reduce extraneous cognitive load, allowing students to understand better the code's logic and structures [46]. Although Microsoft Copilot provides immediate and adaptive feedback [86; 87], students must formulate questions and validate the relevance of the generated responses. This process requires a higher level of metacognition and digital competencies to evaluate the quality of the feedback [38; 42]. In contrast, videos present carefully sequenced examples and explanations, reducing uncertainty about appropriate practices. In this way, students perceive constant reinforcement that increases their perceived competence [39] and motivates them to continue exploring without fear of initial failure [33]. The inclusion of follow-up questions in the Copilot group (Figure 3) sought to support this evaluative process. These prompts encouraged learners to critically analyze the AI's output and identify potential inaccuracies. However, the impact of these questions likely varied depending on the student's level of engagement. Some participants may have utilized them effectively to guide iterative improvement, while others may have skipped them or provided superficial answers, resulting in inconsistent benefits across the group. In addition to these cognitive factors, it is important to consider that the quality and structure of the instructional content may have differed significantly between groups. The video provided a consistent, pedagogically sequenced explanation for each exercise, ensuring uniform content delivery across all participants. In contrast, the AI group relied on individually formulated prompts, resulting in variability in the relevance, depth, and accuracy of the responses compared to Microsoft Copilot. This inconsistent content delivery may have contributed to the observed differences in learning outcomes. Future studies should control instructional structure across conditions to ensure more comparable and reliable evaluations of effectiveness. While [PERSON]'s test indicated homogeneity of variances between groups, the observed differences in post-test standard deviations (2.32 vs. 1.23) may suggest potential variance heterogeneity. However, given the substantial difference in post-test means, the coefficient of variation (CV) offers a more appropriate metric of relative dispersion, revealing that variability was not disproportionate when adjusted for group means.