## 5 Discussion
### Learning Effects
Consequently, the greater effectiveness of instructional videos may reflect their ability to meet the needs of learners with less programming expertise, suggesting that the comparative advantage of videos over Microsoft Copilot could vary with students who have stronger technical foundations. An additional consideration that may limit the generalizability of our findings is the exclusive focus on PHP. While PHP was chosen for its curricular relevance and role in web development, different programming languages pose distinct syntactic, semantic, and conceptual challenges. For example, languages such as Python or JavaScript offer various levels of abstraction and readability, which could influence how learners interact with generative AI tools or benefit from structured instructional materials. Thus, the effectiveness observed in this study may not directly translate to other programming contexts. Future research should replicate this design using diverse programming languages to assess whether the comparative impact of instructional videos and AI tools remains consistent. These conclusions open the door to the strategic use of both modalities, combining their strengths while mitigating their limitations. Thus, this integrated approach optimizes teaching and learning processes in web programming, databases, and Java. This integratedapproach holds significant potential for enhancing educational outcomes and addressing the diverse needs of learners at different stages of their programming journey. Nonetheless, it is important to underscore that the present findings are based solely on immediate post-intervention assessments. As such, they reflect short-term knowledge acquisition rather than long-term learning or retention. Without longitudinal follow-up data, we cannot determine whether the observed advantages of instructional videos persist over time. Nevertheless, it is important to note that the scope and design of this study were deliberately aligned with short-term, well-defined learning objectives. As established in instructional design theory and supported by [PERSON]'s taxonomy, limited interventions can be appropriate and effective when the targeted learning outcomes are specific, foundational, and skill-oriented [95; 96; 97]. Therefore, while long-term retention merits future study, the present design remains methodologically sound for its intended scope. Future studies should include delayed post-tests or follow-up assessments to examine the durability and transferability of these learning outcomes. Although the pre-test confirmed equivalence in baseline PHP knowledge between groups, it is important to note that this instrument did not directly assess broader aspects of digital literacy or prior experience with generative AI tools. While not measured, these factors could have contributed to the variability in how students interacted with Microsoft Copilot. For example, two students with similar programming knowledge may differ significantly in their ability to formulate prompts, interpret responses, or detect inaccuracies in AI outputs due to differences in their digital fluency. This represents a potential source of uncontrolled variance in the AI group. We recommend that future research incorporate explicit instruments to assess digital literacy and prior AI exposure, potentially including them as covariates or segmentation variables in experimental designs. While the present study included a structured exercise guide with follow-up prompts designed to scaffold critical engagement with Microsoft Copilot, it did not incorporate a system for recording the number, content, or quality of student interactions with the tool. This limits the ability to assess the consistency or depth of engagement across participants, as actual usage behaviors (e.g., number of prompts submitted, adherence to correction instructions, or time on task) were not tracked.