## 1 Introduction
### Theoretical Framework  
Learning programming, especially in languages like PHP for web development, presents significant challenges for university students. Among the most common difficulties are the comprehension of abstract concepts, the resolution of syntactic and logical errors, and the lack of motivation to persist in the face of complex problems [9]. These barriers impact academic performance, generating frustration and disinterest among students and hindering their learning process [14]. In response to this scenario, educational institutions have sought to implement innovative pedagogical strategies that allow these limitations to be overcome and promote more effective and motivating learning. In recent years, active learning techniques have gained relevance in education, particularly in technical fields such as programming [15]. Instructional videos have been widely used to facilitate the understanding of complex concepts, as they allow students to learn at their own pace and review the content as many times as necessary [10]. However, these resources are often unidirectional and do not always promote active interaction between the student and the learning material. On the other hand, generative artificial intelligence tools, such as Microsoft Copilot, represent an evolution in active learning strategies. These tools provide immediate feedback and adapt their assistance based on the individual needs of the student [3]. Generative AI fosters a more interactive and personalized approach by offering real-time code suggestions and contextualized explanations, enhancing comprehension and student motivation. The Hedonic-Motivation System Adoption Model (HMSAM) offers a valuable theoretical framework for understanding how generative AI tools, such as Microsoft Copilot, can influence technology adoption and effective use in educational contexts. This model focuses on hedonic motivation, the pleasure or enjoyment users experience when interacting with a technology, as a key factor in its adoption [16]. According to HMSAM, cognitive absorption, which includes dimensions such as enjoyment, immersion, and curiosity, plays a crucial role in perceiving a technology's usefulness and ease of use. The HMSAM is based on three main constructs: perceived enjoyment, perceived control, and immersion. Perceived enjoyment refers to the extent to which the use of a system is perceived as pleasurable and fun, serving as a critical predictor in hedonic contexts [17]. Perceived control refers to the sense of autonomy and mastery that users experience when interacting with a system, which increases their confidence and willingness to adopt the technology [18]. Finally, immersion describes a psychological state in which the user feels fully absorbed by the technological experience, a key factor in systems such as virtual reality and video games [19]. In the context of programming education, each construct of the HMSAM plays a distinct role [20; 21]. For example, \"perceived usefulness\" and \"ease of use\" help assess how well students perceive these tools as effective in supporting coding tasks, especially important in introductory programming, where clear scaffolding is essential. Enjoyment and curiosity are crucial for maintaining engagement during error-prone debugging or logic design activities. \"Focused immersion\" and \"temporal dissociation\" are frequently reported by students when deeply engaged in solving complex programming challenges. \"Control\" reflects the learner's perceived autonomy when navigating through AI-suggested solutions, and \"behavioral intention to use\" is critical for understanding whether students would continue using AI tools beyond the classroom setting. These constructs were, therefore, selected not only for their theoretical grounding in HMSAM but also for their practical alignment with the motivational, emotional, and cognitive demands inherent to programming education [20].