Courses in computing, engineering, and data science often involve complex abstract reasoning and domain-specific terminology, which require pedagogical tools capable of aligning content to curricular standards while maintaining conceptual clarity. In response to these challenges, researchers have begun to propose AI-powered educational assistants tailored to specialized domains [5; 10; 11]. These efforts highlight the potential of intelligent systems to facilitate autonomous learning, especially when combined with retrieval mechanisms that ensure factual grounding and relevance [12; 13]. Parallel computing is a foundational paradigm in contemporary computer science, underpinning advances in high-performance systems, big data processing, and artificial intelligence. Despite its growing importance, parallel programming presents pedagogical challenges, especially within undergraduate computer science curricula [14; 15]. The technical complexity of the subject, which requires the understanding of concepts like concurrency, synchronization, and memory architecture, often relegates it to advanced levels of course-work [16; 17]. This approach delays the exposure of students to parallel problem-solving strategies, potentially limiting their ability to internalize parallel thinking early in their academic journey. Recent studies emphasize the importance of restructuring the way parallel programming is taught. [PERSON] et al. demonstrated through empirical experiments involving 252 students that early exposure, even among those without prior computing knowledge, can produce learning outcomes comparable to or better than those of advanced students, especially when combined with active methodologies such as Team-Based Learning (TBL) and Problem-Based Learning (PBL) [16]. [PERSON] et al., in a systematic review, reaffirmed the importance of using practical platforms such as low-cost clusters and single-board computers to deliver hands-on experiences in parallelism through tools such as OpenMP, MPI, and CUDA [18]. These findings suggest that educational strategies grounded in practice and accessibility can bridge gaps in learning complex computing paradigms. Despite the growing interest in LLMs for educational applications [6; 19; 20], challenges related to factual inaccuracies and limited contextual grounding persist, especially when addressing highly specialized technical content [21]. To mitigate these issues, hybrid frameworks such as Retrieval-Augmented Generation (RAG) have emerged as a promising solution. By integrating domain-specific knowledge sources into the generation process, RAG enhances the factual consistency and contextual relevance of AI-generated outputs [10; 22; 23]. This architecture is particularly suited for instructional settings that demand alignment with curricular content and high levels of technical precision. More advanced models, such as KG-RAG, take this a step further by incorporating structured knowledge graphs into the retrieval process. This approach not only mitigates hallucination risks but also ensures conceptual continuity across educational queries, aligning well with instructional goals. Studies show that KG-RAG significantly improves learning outcomes, with experiments reporting up to a 35% increase in student performance in controlled evaluations [11]. Such models have shown capacity to ground answers in course materials and assist independent learning [24; 25; 26; 27]. Although conversational AI is increasingly used in education, current intelligent tutoring systems and LLM-based assistants are overwhelmingly geared toward English-language resources, leaving Spanish-speaking learners with limited and fragmented support, particularly in advanced technical subjects such as parallel computing. This lack of linguistically and domain-appropriate study materials restricts self-directed learning, widens the achievement gap, and impedes early exposure to parallel thinking skills.