## 6 Conclusions  
This study evaluated the impact of using a generative artificial intelligence (Microsoft Copilot) compared to video-based instruction on university students' learning of web programming in PHP. Through a pre-test-intervention-post-test experimental design, the effects on learning and affective perceptions of 71 participants were analyzed. Participants were randomly divided into two groups: one that used Microsoft Copilot and another that followed instructional videos. The Hedonic-Motivation System Adoption Model (HMSAM) was employed to assess perceptions. The results revealed that students who received instruction through videos made greater progress in the post-test knowledge assessment than those who used Microsoft Copilot. This finding suggests that the sequential and multimodal structuring of information in videos, specifically designed to teach complex programming concepts and algorithms, facilitates the assimilation of concepts by reducing extraneous cognitive load. In contrast, Microsoft Copilot, as a general-purpose GenAI tool, lacks the tailored pedagogical focus of the videos, which may explain its relatively lower effectiveness for novice learners in this context. These conclusions highlight the potential of instructional videos as a powerful tool for introductory programming education, particularly in contexts where clarity, structure, and reduced cognitive load are critical for student success, such as with industrial engineering students who may lack extensive programming experience. At the same time, they suggest that while generative AI tools like Microsoft Copilot offer innovative possibilities, their adoption may require careful scaffolding and integration, especially for novice learners. Notably, although the participants (third-year industrial engineering students) had prior programming experience from courses in Java and databases, their foundations might not have been sufficient to leverage Microsoft Copilot in this context fully. Learning PHP, a new language for them, alongside the autonomous use of Microsoft Copilot without specific training, likely demanded advanced metacognitive skills (e.g., crafting effective prompts and critically evaluating AI responses) that were not yet fully developed. This suggests that Microsoft Copilot could be more beneficial for students in advanced stages or with a computer science background, where a stronger, language-specific conceptual foundation and familiarity with AI interaction enable them to maximize their potential for real-time problem-solving. Future research should investigate hybrid approaches that integrate the strengths of both methods to enhance learning outcomes across various stages of programming education and diverse learner profiles. This study's main contribution provides empirical evidence on the differential impact of generative AI tools and traditional instructional methods on programming learning. These findings underscore the importance of considering cognitive demands and students' affective perceptions when designing technology-based pedagogical strategies. Additionally, this study contributes relevant knowledge to the Latin American context, where research on the adoption of educational technologies is still scarce, and factors such as digital literacy and resource availability may influence the effectiveness of technological tools. While our results are rooted in the context of industrial engineering students learning PHP, they may hold broader implications. Due to their structured guidance and reduced cognitive load, the preference for instructional videos could generalize to other introductory programming courses (e.g., Python, JavaScript) or even non-programming domains (e.g., mathematics or engineering design) where novices benefit from clear, sequential instruction. Similarly, the potential of generative AI tools like Microsoft Copilot for advanced learners might extend to contexts requiring adaptive feedback.