## 5 Discussion
### Learning Effects
Moreover, ANCOVA is generally robust to moderate violations of the homogeneity of variances assumption, particularly in balanced designs [88; 89]. Nonetheless, we acknowledge this as a potential limitation. As a future line of research, simulation studies [90] could be implemented to evaluate how deviations from normality or homoscedasticity might influence the robustness of ANCOVA results in educational contexts involving generative AI. The Hedonic-Motivation System Adoption Model (HMSAM) establishes that the sense of enjoyment and perceived usefulness are key determinants in the adoption and effectiveness of learning systems [16]. On the other hand, audiovisual resources (with narrative, practical examples, and demonstrations) tend to generate greater affective and cognitive engagement by stimulating attention, curiosity, and interest [44; 91]. In line with this, previous studies have shown that video-based learning can foster deeper emotional involvement, facilitating concept retention [29; 31]. In contrast, interaction with generative artificial intelligence may be less engaging and require self-regulation strategies that not all students have developed, especially at the early stages of programming [30]. From an instructional perspective, Microsoft Copilot promotes a more active learning approach regarding exploration and constantly testing hypotheses within the code [82; 92; 93]. However, this constructivist approach may generate uncertainty when students lack a robust foundation in programming syntax and logic. On the other hand, instructional videos adopt an expository approach, where the teacher's guidance is explicit, and students assimilate problem-solving strategies more directly [28]. This difference becomes particularly noticeable in the early stages of learning when familiarity with basic concepts is essential to avoid cognitive overload [47; 85]. Thus, despite the clear inclination of the results toward the effectiveness of videos, the findings do not diminish the value of generative artificial intelligence as a reinforcement tool or for advanced tutoring [80; 81; 94]. In particular, a mixed instructional strategy that combines the systematic and exemplified presentation of a video with the personalized feedback of Microsoft Copilot could maximize learning by providing a solid initial conceptual framework, followed by guided and immediate experimentation [29; 33]. Future research could explore the optimal integration of both methods based on student profiles, such as their level of experience, learning styles, and intrinsic motivations. The greater effectiveness of video in improving web programming learning can be explained by the reduced extraneous cognitive load, which facilitates sequential information reception through multiple modes; sustained attention and motivation derived from audiovisual resources; and the immediate and accessible support that reduces uncertainty for novice students. With this, we can answer the research question: Are there differences in learning between students who practice web programming using Microsoft Copilot versus instructional videos? Affirmatively, learning differences favor video-based instruction over generative artificial intelligence-based practice. The profile of the participants (third-year industrial engineering students rather than computer science students) may further explain the observed learning effects. Unlike computer science students, who typically have extensive prior exposure to programming, industrial engineering students in this study had limited experience, primarily from prior course modules in Java and databases. This relative novelty of programming, particularly in PHP, likely heightened the importance of affective factors such as curiosity, enjoyment, and immersion, as these students relied heavily on structured guidance (e.g., videos) to build confidence and competence.